CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

The banking sector is mainly dominated by deposit money banks and easily the

most significant in any developing nations like Nigeria. Around the world, the

exceptional role of deposit money banks as the driving force of growth in any

economy has been generally recognized (Adegbaju & Olokojo, 2013; Kolapo,

Ayeni & Oke, 2017; Mohammed, 2017). As a matter of fact, the intermediation

role of deposit money banks can be said to be a catalyst for economic growth and

development as investment funds are mobilized from the excess units in the

economy and made accessible to the deficit units. In doing this, deposit money

banks give an array of financial services to their customers. It can consequently be

said that the effective and efficient performance of the banking industry is a

significant foundation for the financial stability of any country. The extent to

which deposit money banks stretch out credit to the general population for

productive activities speeds up the pace of a country's economic growth and in

addition the long term sustainability of the banking industry (Kolapo, Ayeni &

Oke, 2017; Mohammed, 2017). In Nigeria, Imala (2010) expressed that the

primary aim of the banking system are to guarantee price stability and facilitate

1
quick economic development through their intermediation role of mobilization

savings and inculcating banking habit at the household and micro enterprise levels.

The deposit money banks truly do add to or deduct from the stock of money

accessible to the economy and they are likewise utilized as instrument through

which the Central bank of Nigeria (CBN) perform one of its primary function of

formulating an executive system and a stable economic growth. The Central Bank

of Nigeria (CBN) execute this responsibility for the government of Nigeria through

a process outlined in the Central Bank of Nigeria Decree 24 1991. In formulating

and executing monetary policy, the Central Bank of Nigeria governor is expected

to make proposals of the president of the Federal Republic of Nigeria who has the

power to accept or amend such proposals, therefore implementing the approval

monetary policy. The Central Bank of Nigeria directs banks and other financial

institutions to carry out specific duties in pursuit of approve monetary policy

guidelines and circular, operational within a fiscal year but could be amended in

the course of the year. Penalties are normally prescribed for non-compliance with

specific provision of the guideline (CBN Briefs, Series no 95/03).

The monetary authorities mostly depend on the manipulation of monetary policy

with the end goal of price stability, full employment, balance of payment

equilibrium, credit control budgeting discipline and economic growth (Oloyede,

2013). The techniques by which the monetary authority attempts to accomplish the
2
goals through the implementation of monetary policy measures must have certainly

affected positively or negatively on the performance of deposit money banks in

Nigeria, amongst other financial institution. The level and structure of interest rate,

money supply and growth of the banking sector competitiveness and liquidity

management are some of the elements that fall under the effect examination in this

research study. This research work plans to identify the monetary policy measures

utilized by the Central Bank of Nigeria, their efficacies and effect on the stability

of deposit money banks in Nigeria.

1.2 Statement of the Problem

Monetary policy is among the primary economic management tools that

governments utilize to shape economic performance. Measured against fiscal

policy, monetary policy is said to be faster at settling economic shocks. Monetary

policy aims are concerned with the management of different monetary targets

which include promotion of growth, smoothing the business cycle, stabilizing long

term interest rates and the real exchange rate, price stability, preventing financial

crises and achieving full employment. Experience shows that emphasis is mostly

placed on maintaining price stability or guaranteeing low inflation rates.

The Central Bank of Nigeria is answerable for the suggestion and implementation

of monetary policy tools in Nigeria. The CBN suggests the cash reserve ratio,

3
monetary policy rate and treasury bill rate. Those tools are implemented through

deposit money banks and they are aimed at stabilizing the price levels in the

economy. The utilization of cash reserve ratio influences the level of liquidity in

the deposit money banks. When commercial banks are confronted with limited

liquidity, they turn to other deposit money banks for inter-bank borrowing. Those

funds are borrowed at the monetary policy rate and it is mostly very high, which

influences the interest expense for the borrowing bank and the interest income for

the lending bank. The alternate method for increasing liquidity in the bank will be

to borrow by floating a debt instrument. The rate offered for the debt instrument is

likewise attached to the treasury bills or treasury bonds issued by the government

through the Central Bank. These effects of the monetary tools are expected to have

an effect on the stability of deposit money banks.

Various research studies have been done in relation to deposit money banks in

Nigeria. for instance, Gitonga (2015) studied the connection between interest rate

risk management and profitability of deposit money banks in Nigeria; Kimoro

(2015) did a survey of the foreign exchange reserves risk management strategies

adopted by the Central Bank of Nigeria and Mbotu (2015) did a study on the effect

of the Central Bank of Nigeria rate on deposit money banks’ benchmark lending

interest rates. Ongore and Kusa (2013) study analyzed the impacts of bank specific

factors and macroeconomic factors on the performance of deposit money banks in

4
Nigeria during the period from 2001–2010. Kiganda (2014) carried out a study on

impact of macroeconomic factors on the profitability of deposit money banks in

Nigeria with an emphasis on Union Bank.

This study has recognized a gap in the current literature and research regarding

monetary policy shocks and its effect on the stability of deposit money banks. The

literature reveals that while there is a lot effort by the government to affect the

money supply by instituting different policy tools, an analysis on the effects of

those tools on deposit money banks’ stability, which are the most utilized channel

of transmission of the policies, is inconclusive. This study will for this purpose, be

motivated to fill the knowledge gap on the effect of monetary policy shocks on

deposit money banks stability in Nigeria.

1.3 Research Questions

i. What effect does cash reserve ratio have on banks’ total asset in Nigeria?

ii. What effect does monetary policy rate have on banks’ total asset in Nigeria?

iii. What effect does treasury bill rate have on banks’ total asset in Nigeria?

1.4 Objectives of the Study

The broad objective of the study is to examine the effect of monetary policy shocks

on deposit money banks stability in Nigeria. The study specifically sought to;

i. examine the effect of cash reserve ratio on banks’ total asset in Nigeria.
5
 ii. examine the effect of monetary policy rate on banks’ total asset in Nigeria.

iii. examine the effect of treasury bill rate on banks’ total asset in Nigeria.

1.5 Research Hypotheses

For the purpose of evaluating or in order to efficiently and objectively analyze or

achieve the above objective, the hypotheses is formulated thus:

H01: Cash reserve ratio has no significant effect on banks’ total asset in Nigeria.

H02: Monetary policy rate has no significant effect on banks’ total asset in Nigeria.

H03: Treasury bill rate has no significant effect on banks’ total asset in Nigeria.

1.6 Significance of the Study

The study helps us with understanding the effect of monetary policy shocks on

deposit money banks stability in Nigeria. It would help the regulators to carefully

plan and forecast the effects of its policies to meet its aims of economic growth and

full employment. To bankers, it would reveal the connection existing between our

relevant variables, which will be of interest to them in their respective banks. This

would likewise benefit the academic community which would avail them the

opportunity of conducting further research in the topic of similar areas.

6
The study is expected to add to the existing literature in the field of monetary

policies. Future researchers can use this research as a foundation for further

research in the area of monetary policy theories.

The study will likewise enlighten management teams of deposit money banks on

the short term and long term effects of the monetary policy implementations by the

Central Bank. This will greatly assist them in designing the risk management

measures to employ given expected changes in monetary policies.

1.7 Scope and Limitations of the Study

This study covers various monetary policy instruments and policy options as they

affect banking operations. It is however limited to deposit money bank institutions

in Nigeria. The monetary policy tools that would be involved in this study are cash

reserve ratio (CRR), monetary policy rate (MPR) and treasury bill rate (TBR).

Emphasis is clearly laid on applications and not on process of formulation of

monetary policy. The study spans for a period of 31 years, 1991–2021.

1.8 Organizations of the Study

The research work is segmented into five chapters. The first chapter, Chapter 1,

covers Introduction; background of the study, statement of the problem, research

questions, objectives of the study, research hypotheses, significance of the study,

scope of the study, organization of the study, and definition of operational terms.

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The following chapter, Chapter 2, covers discussions on the Conceptual review;

concept of monetary policy, concept of banks stability, concept of interest rates,

monetary policy in Nigeria, monetary policy instruments, the role of deposit

money banks in the Nigeria economy, the Theoretical review of the study; the

classical theory, the Keynesian theory, the monetarist theory, anticipated income

theory, shiftability theory, and the Empirical review of the study. Chapter 3 which

follows, contains information on the research methods to be used for the study, and

they include research design, population of the study, sources and methods of data

collection, estimation techniques, model specification and definition of variables.

1.9 Definition of Terms

Cash Reserve Ratio: Cash reserve ratio is a specified minimum fraction of the

total deposits of customers, which deposit money banks have to hold as reserves

either in cash or as deposits with the central bank.

Deposit Money Banks: Deposit money banks are financial institutions that carry

out the functions of financial advisory, loan administration, funds safety, among

others.

Monetary Policy Rate: Minimum rediscount rate presently called monetary policy

rate is utilize to signal the desired direction of interest rate movement.

8
Total Asset: Total asset alludes to the aggregate sum of assets owned by an

individual or entity.

Treasury Bills Rate: Treasury bills rate is the rate at which treasury bills which

are government bonds or debt securities with maturity of under a year are issued.

9
 CHAPTER TWO

LITERATURE REVIEW

2.1 Preamble

This chapter evaluates existing literature about the effect of monetary policy

shocks on deposit money banks stability in Nigeria overtime in order to deepen our

understanding in the same regard. It is systematically arranged to contain 3

sections; the Conceptual review of the study, the Theoretical review of the study

and the Empirical review of the study.

2.2 Conceptual Review

2.2.1 Concept of Monetary Policy

Ezenduyi (2004) defines monetary policy as the policy which involve the

adjustment of money stock, interest rate, exchange rate as well as expectation to

influence the level of economic activities and inflation in desired direction,

targeting as the mapping up of excess liquidity armed at ensuring a noninflationary

macroeconomic environment. Monetary policy can be defined as the instruments at

the disposal of the monetary authorities to influence the availability and cost of

credit/money with the ultimate objective of achieving price stability as

demonstrated by Ibeabuchi (2012). Onouorah, et al (2016) defined monetary policy

10
as a rule and regulation imposed by the monetary authority into controlling the

money supply inflation and achieves economic growth. Onyeiwu (2012) defines

monetary policy as a technique of economic management to bring about

sustainable economic growth and development which has been the pursuit of

nations and formal articulation of how money affects economic aggregate. Chigbu

& Okonkwo (2014) held that monetary policy generally refers to the deliberate

efforts of the government to use changes in money supply, cost of credit, size of

credit and direction of credit to influence the level of economic activities to

achieve desired macroeconomic stability in an economy. Richard (1999) stated that

the instrument tools of monetary policy have been classified broadly in two

categories traditional and nontraditional quantitative instrument. Monetary

policies, as adopted in Nigeria, have four broad objectives which are:

i. To maintain a high level of employment (full employment): Full

employment means employment of labor, plant and capital at a tolerable

capacity to achieve the set goals of national economic policy aimed at

combating recession and economic depression.

ii. To maintain stable price level: Price level stability goal is related in an

important sense to the control of inflation, it refers to a situation of sustained

and rapid increase in the general level of prices, however, generated

11
 (Nnanna, 2006). According to Ibeabuchi (2012), inflation reduces real

disposable income and consequently the purchasing power of money.

iii. To maintain the highest sustainable rate of economic growth: This

means both quantitative and qualitative increase in the total quantity of

goods and services produced in the economy annually. Nnanna (2006)

opined that economic growth is said to be achieved in a country in a

situation where there is an increase in the income position of the citizens of

the country and also a corresponding increase in the amount of goods and

services which a given quantity of money can buy.

iv. To maintain the highest equilibrium in the balance of payments: A

country’s balance of payment may be in total equilibrium if there exists a

balance between total payments and total receipts, that is, the avoidance of

larger or chronic deficit or surplus in the balance of payments.

2.2.2 Concept of Banks Stability

Banks stability focuses on the long run survival of banks. A banking institution can

have increasing profitability level and yet be considered financially unstable.

Therefore, banks stability is defined as the absence of banking crisis (Brunnermier,

Crockett, Goodhart, Persaud & Shin, 2009). Ozili & Thankom (2018) define banks

stability to be the absence of abnormal disruption in credit supply, payment

systems and banking services. Barth, Caprio & Levine (2013) pointed out reasons
12
why bank could become unstable. The study revealed that banks instability can be

triggered by shaky regulation or ineffective supervision. Although strict

supervision is highly recommended, the study noted that strict supervision did not

lead to greater banking stability. Ozili (2018) investigated the determinants of

banking stability in Africa banks and his study showed that the presence of foreign

banks, banking efficiency, banking concentration, bank size, political stability were

key determinants of banking stability.

Since profitability and stability tend to be used interchangeably, Anchor (2016)

sought to identify the interrelationship between profitability and stability and the

result showed that there is a negative relationship between profitability and

stability. Hence, lower banks stability is expected to lead to higher profitability

level. This result confirms the risk-return theory, where higher risk is found to lead

to an increase in expected return. Berger & De Young (1997) identified bank

efficiency to be a key determinant of bank stability. This implies that efficient

banks are better able to manage credit risk as they can improve their stability by

mitigating high nonperforming loans. Githinji (2016) noted that banks can become

unstable because of low capital quality, low asset quality, associated with

aggression of their credit policy that increases credit risk. The size of bank capital

was found to determine bank’s ability to maintain stability during financial crisis

(Klaas & Vagigova, 2014). Hodachnik (2009) observes that banks stability is

13
maintained by sufficient capitalization that is characterized by security level of risk

asset and acts as the guarantor of bank reliability and liquidity. Bank stability can

be measured with the Z-score coefficient which is based on five financial ratios

that can be calculated from data found on banks annual bulletin. A high Z-score

indicates high bank stability and lesser signifies probability of going bankrupt.

2.2.3 Concept of Interest Rates

According to Keynes, interest rate is the reward for not hoarding but for parting

with liquidity for a specific period of time. Keynes’ definition of interest rate

focuses more on the lending rate. Adebiyi (2009) defines interest rate as the return

or yield on equity or opportunity cost of deferring current consumption into the

future. Some examples of interest rate include the saving rate, lending rate, and the

discount rate. Professor Lerner, in Jhingan (2008), defines interest as the price

which equates the supply of credit or savings plus the net increase in the amount of

money in the period, to the demand for credit or investment plus net hoarding in

the period. This definition implies that an interest rate is the price of credit which

like other price is determined by the forces of demand and supply; in this case, the

demand and supply of loanable funds.

Ibimodo (2010) defined interest rates, as the rental payment for the use of credit by

borrowers and return for parting with liquidity by lenders. Like other prices interest

14
rates perform a rationing function by allocating limited supply of credit among the

many competing demands. Bernhardsen (2013) defined the interest rate as the real

interest rate, at which inflation is stable and the production gap equals zero. That

interest rate very often appears in monetary policy deliberations. However, Irving

Fisher (1956) states that interest rates are charged for a number of reasons, but one

is to ensure that the creditor lowers his or her exposure to inflation. Inflation causes

a nominal amount of money in the present to have less purchasing power in the

future. Expected inflation rates are an integral part of determining whether or not

an interest rate is high enough for the creditor.

The real interest rate represents a fundamental valuation of temporary provision of

capital corresponding to a price level constant in time. It is also obvious from the

above relation that if inflationary expectations change, nominal interest rates have

to change aliquot at a constant real interest rate (Cottrell; 2010). The real interest

rate concept is irreplaceable in the research into the mutual relations of inflation,

because assuming that the creditors are rational, inflation and nominal interest rates

influence each other. For similar reasons, the real interest rate is used in broader

economic analyses. Expected inflation is an unobservable quantity. In an expose

analysis, it can be replaced by the actual rate of inflation in the following period,

which is equivalent to assuming rational expectations (Bencik; 2014).

15
Theoretically less satisfactory, but easier to apply, is the assumption of adaptive

expectations; this replaces expected inflation in the future by actual inflation in the

present. Inflation is very important, because when there is increased inflation over

a long period of time, economic agents recognize the actual value of money, stop

suffering from money illusion and accept increased nominal rates. Therefore,

investment as the main link between the interest rates and the real economy is

considered a function of the real interest rates, as standard (Bencik; 2014).

2.2.4 Monetary Policy in Nigeria

CBN act 1959 clearly states that the objectives to be achieved by the CBN act

include full employment attainment, long term interest rate stability and optimal

exchange rate target pursuance. According to Onyeiwu (2012) the CBN monetary

policy in use has been charged with authority of devising and enforcing monetary

policy of the CBN act (1958). The development of monetary policy is categorized

in two stages which are direct control era (1959–1986) and market based controls

era (1986–date). Direct control phase was an exceptional time in Nigeria’s

monetary management period. This is because it aligned with different changes in

the structure of the economy. This includes economic base shift from agriculture to

petroleum, civil war enforcement, the boom and crash in oil prices in both 1970s

and 1980s, with the establishment of the Structural Adjustment Programme (SAP).

In this era, the monetary policies of the central bank was concentrated on putting in
16
place and managing the rate of interest and exchange, discerning allocation to

certain sectors, discount rate manipulations, finally moral suasion.

Structural Adjustment Programme commenced in 1986 and adjustments made to

the CBN act in 1991 brought in a new era of implementation of monetary policy in

Nigeria. This precisely guaranteed CBN goal autonomy and full instrument.

Employing this method, CBN influences parameters in the economy indirectly via

its open market operation. The activities conducted are mainly on treasury bill and

repurchasing options serving a complimentary role with reserve requirements

usage, liquidity ratio and cash reserve ratio. The above instruments set is employed

to cause changes in the quantity base nominal anchor (monetary aggregates)

employed in monetary programming.

In other way, the cash reserve ratio is used as the price based nominal anchor in

swaying the direction in the economy cost of fund. Movements in this rate is a

signal to the banks’ monetary disposition, either it is pursuing a tightening or an

expansionary monetary policy. They are generally placed within 26% and 8%

range from 1986. The CBN latter established in 2006 the monetary policy rate to

replace cash reserve ratio.

2.2.5 Monetary Policy Instruments

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Monetary policy instruments are tools that the Central Bank of Nigeria can use to

control and influence money supply, interest rates and exchange rate to achieve a

monetary objective. The instruments of monetary policy can be categorized into

two which are direct or qualitative instruments and indirect or quantitative

instruments.

Direct Instruments or Qualitative Instruments of Monetary Policy Tools

Though there is an avalanche of instruments available for money and credit

control, the instrument mix to be employed at any time depends on the goals to be

achieved and the effectiveness of such instrument to a large extent hinges on the

economic fortunes of the country. The commonly used direct instrument are:

i. Reserve Requirement: The Central Bank may require Deposit Money

Banks to hold a fraction of their deposit liabilities as vault cash and or

deposits with it. Fractional reserve limits the amount of loans banks can

make to the domestic economy and thus limit the supply of money. The

assumption is that Deposit Money Banks generally maintain a stable

relationship between their reserve holdings and the amount of credit they

extend to the public.

ii. Special Deposits: The central bank has the power to issue directories from

time to time requiring all banks to maintain with it as special deposit an

18
 amount equal to the percentages of the institution’s deposits liabilities or the

absolute increase in its deposit liabilities over an amount outstanding at a

certain date.

iii. Moral Suasion: Moral suasion simply means the employment by the

monetary authority of friendly persuasive statement, public pronouncement

outright appeal, the monetary authority sometimes uses the less tangible

technique to influence the lending policies of commercial banks.

Consequences to the banking system and the economy as a whole, the

Central Bank of Nigeria holds periodic meetings with the bankers

committees and on other occasion meets formally or informally with the

leaders in the banking community (CBN, 2013). With the leaders in the

banking community, such contracts are geared towards the development of

confidence between the central bank and other banks. It affords the central

bank opportunity to discuss the improvement in standards and conducts in

the banking industry.

iv. Selective Credit Control: According to Nnanna (2006), this instrument is

used to distinguish among the sectors of the economy into preferred and less

preferred sectors. This is usually designed to influence the direction of

credits in the economy so as to ensure that credits go to those sectors

designed “preferred”. It is very useful where a country operates development

19
 plans like Nigeria. When plans are drawn up these credit controls will be

integrated in the budget. In course of the government’s programme to

revitalize agricultural production which is the most favored sector, credits to

the favored sector is at lower interest rate while the least favored sectors pay

the highest rate of interest.

v. Direct Credit Control: According to CBN (2013), the Central Bank can

direct Deposit Money Banks on the maximum percentage or amount of

loans to different economic sectors or activities, interest rate caps, liquid

asset ratio and issue credit guarantee to preferred loans. In this way the

available savings is allocated and investment directed in particular

directions.

vi. Prudential Guidelines: The Central Bank may in writing require the

Deposit Money Banks to exercise particular care in their operations in order

that specified outcomes are realized (CBN, 2013). Key elements of

prudential guidelines remove some discretion from bank management and

replace it with rules in decision making.

Indirect Instruments or Quantitative Instruments of Monetary Policy Tools

Fiduciary or paper money is issued by the Central Bank on the basis of

computation of estimated demand for cash. To conduct monetary policy, some

monetary variables which the Central Bank controls are adjusted in order to affect
20
the goals which it does not control. The instruments of monetary policy used by the

Central Bank depend on the level of development of the economy, especially its

banking sector. The commonly used instruments are discussed below (CBN,

2016):

i. Open Market Operations: The Central Bank buys or sells on behalf of the

Fiscal Authorities, securities to the banking and nonbanking public such as

treasury bills. When the Central Bank sells securities, it reduces the supply

of reserves and when it buys securities, it increases the supply of reserves to

the Deposit Money Banks, thus affecting the supply of money (Ibeabuchi,

2012; CBN, 2013; Ojo, 2013; Solomon, 2013).

ii. Lending by the Central Bank: The Central Bank sometimes provide credit

to Deposit Money Banks, thus affecting the level of reserves and hence the

monetary base (CBN, 2013).

iii. Interest Rate: The Central Bank lends to financially sound Deposit Money

Banks at a most favorable rate of interest, called the minimum rediscount

rate. The minimum rediscount rate sets the floor for the interest rate regime

in the money market and thereby affects the supply of credit, the supply of

savings and the supply of investment according to Obidike, Ejeh &

Ugwuegbe (2015).

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iv. Exchange Rate: The balance of payments can be in deficit or in surplus and

each of these affect the monetary base, and hence the money supply in one

direction or the other. By selling or buying foreign exchange, the Central

Bank ensures that the exchange rate is at levels that do not affect domestic

money supply in undesired direction, through the balance of payments and

the real exchange rate. The real exchange rate when misaligned affects the

current account balance because of its impact on external competitiveness

(Sanusi, 2009; Ibeabuchi, 2012; Akpan, 2013: Imoisi, Olatunji &

Ekpenyong, 2013).

v. Rediscount Rate: The rediscount rate is the rate at which the central bank

provide loan accommodation to commercial banks (CBN, 2013). As a lender

of last resort, such lending by the central bank is usually at panel rates. By

making appropriate changes in the rate, the central bank controls the volume

of total credits indirectly. This has the purpose of influencing the lending

capacity of the commercial banks. During the periods of inflation, the central

bank may raise the rediscount rate making obtaining of funds from the

central bank more expensive. In this way, credit is made tighter. Similarly,

in depression, when it is necessary to encourage commercial banks to create

more credits, the central bank will lower the rediscount rate.

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vi. Cash Reserve Requirements: Ojo (2013) posit that the reserve requirement

can be manipulated by the central bank to reduce the ability of commercial

banks to make loans to the public by simply increasing the ratio or

enhancing their lending position by decrease in the ratio. Reserve

requirement is one of the most powerful instruments of monetary control

(CBN, 2013). A change in the required reserve ratio changes the ratio by

which the banking system can expand deposit through the multiplier effect.

If the required reserve ratio increases, the multiplier decreases and thereby

reduces the liquidity position of the banking system.

2.2.6 The Role of Deposit Money Banks in the Nigeria Economy

The traditional role of banks is to accept deposits and make loans and derive a

profit from the difference in the interest rates paid and charged to depositors and

borrowers respectively. The process performed by banks of taking in funds from a

depositor and then lending them out to a borrower is known as financial

intermediation (Sanderson, 2013). Through the process of financial intermediation,

certain assets are transformed into different assets or liabilities. As such, financial

intermediaries channel funds from people who have extra money or surplus

savings to those who do not have enough money to carry out a desired activity.

Banking thrives on the financial intermediation abilities of financial institutions

that allow them to lend out money and receiving money on deposit. The bank is the
23
most important financial intermediary in the economy as it connects surplus and

deficit economic agents. Sanderson (2013) summarized roles of deposit money

banks to include:

i. Credit Provision: Credit fuels economic activity by allowing businesses to

invest beyond their cash on hand, households to purchase homes without

saving the entire cost in advance, and governments to smooth out their

spending by mitigating the cyclical pattern of tax revenues and to invest in

infrastructure projects.

ii. Liquidity Provision: Businesses and households need to have protection

against unexpected needs for cash. Banks are the main direct providers of

liquidity, both through offering demand deposits that can be withdrawn any

time and by offering lines of credit. Further, banks and their affiliates are at

the core of the financial markets, offering to buy and sell securities and

related products at need, in large volumes, with relatively modest transaction

costs.

iii. Risk Management Services: Banks allow businesses and households to

pool their risks from exposures to financial and commodity markets. Much

of this is provided by banks through derivatives instruments transactions.

Banks also enable individuals and businesses to take part in the global

foreign exchange and commodity markets indirectly. It would be very

24
 difficult for example for a small company needing only a few million

Japanese yen to import a vehicle from Japan to get onto the global currency

markets without the aid of a bank.

iv. Remittance of Money: Cash can be transferred easily from one place to

another and from one country to another by the help of a bank. It has

facilitated transactions in distant places. This, in turn, has expanded the

internal and external trade and market. People have become free of the risks

of carrying cash, gold, silver etc. The credit instruments issued by banks

such as cheque, draft, real time gross settlement, credit cards etc., have

facilitated the transfer of money.

v. Rapid Economic Development: the banks make available loans of different

periods to agriculture, industry and trade. They make direct investments in

industrial sectors. They provide industrial, agricultural and commercial

consultancy, hence, facilitating the process of economic development.

2.3 Theoretical Review

2.3.1 The Classical Theory

The widely accepted approach to monetary economics was known as the quantity

theory of money, used as part of a broader approach to micro and macro issues

referred to as classical economics from the works of Irving Fisher who lay the

25
foundation of the quantity theory of money through his equation of exchange.

Diamond (2008) states in his proposition that money has no effect on economic

aggregates but price. The classical school evolved through concerted efforts and

contribution of economists like Jean Baptist Say, Adam Smith, David Richardo,

Pigou and others who shared the same beliefs. The classical economists decided

upon the quantity theory of money as the determinant of the general price level.

Most were of the opinion that the quantity of money determines the aggregate

demand which in term determine the price level as posited by Amacher & UIbrich

(2006).

Onouorah, Shaib, Oyathelemi & Friday (2016) mentions that the quantity theory of

money was not only a theory about the influence of money on the economy and

how a Central Bank should manage the economy’s money supply, but it

represented a specific view of the private market economy and the role of

government. The private market such as banks provided the best framework for

achieving socially and economically desired outcomes. According to the theory,

the role of government was providing a system of laws and security to protect

private property, as well as providing a stable financial and monetary framework.

Solomon (2013) acknowledges that theory posit that money affects the economy

which is the reason why Central banks adopt monetary policy to control the flow

of money in the economy through banks that are regarded as the private market

26
industry that mobilizes the largest volume of money in any economy. The

economic depression of the 1930s, according to Onyemaechi (2010) drastically

changed attitudes about the role of money and monetary policy as a tool of

economic stabilization. Monetary policy was then viewed as an ineffective method

of fighting depressions, and the belief in a self-regulating market that reached

socially desirable results was destroyed.

2.3.2 The Keynesian Theory

The Keynesian Economists think of monetary policy as working primarily through

interest rate. In Keynesian transmission mechanism, an increase in the money

supply leads to a fall in interest rate to include the public to hold additional money

balances.

Consequently, a fall in interest rate may stimulate investment. The increased

investments also increase the level of income or output through the multiplier,

which may stimulate economic activities. Thus, monetary policy affects economic

activity indirectly through their impact on interest rates and investment. Therefore,

the Keynesian transmission mechanism is characterized by a highly detailed sector

building up of aggregate demand and a detailed specification of portfolio

adjustment process that attaches central role to interest as an indirect link between

monetary policy and fiscal demand.

27
In simple terms, the monetary mechanism of Keynesians emphasizes the role of

money, but involves an indirect linkage of money with aggregate demand via the

interest rate as symbolically shown below:

↓OMO→↓R→↑MS→↓r→I→↑GNP

Where,

OMO = Open Market Operation.

R = Commercial Bank Reserve.

MS = Money Supply.

r = Interest Rate.

I = Investment.

GNP = Gross National Product.

On a more analytical note, if the economy is initially at equilibrium and there is

open market purchase of government securities by the Central Bank of Nigeria

(CBN), this open market operation (OMO) will increase the commercial banks

reserve (R) and raise the bank reserves. The bank then operates to restore their

desired ratio by extending new loans or by expanding bank credit in other ways.

Such new loans create new demand deposits, thus increasing the money supply

(MS). A rising money supply causes the general level of interest rate (r) to fall. The

28
falling interest rates affects commercial bank performance and in turn stimulate

investment, given businessmen expected profit. The induced investment

expenditure causes successive rounds of final demand spending by gross national

product (GNP) to rise by a multiple of the initial change in investment. On the

other hand, a fall in money supply causes the general level of interest rate (R) to

rise or increase thereby increasing the commercial banks profitability (Jhingan,

2010).

2.3.3 The Monetarist Theory

The monetarist economist recognize that money is not just a close substitute for a

small class of financial assets but rather a substitute for large spectrum of financial

and real asset. Given an equilibrium position, an increase in money supply raises

the actual proportion of money relative to the desired proportion. Symbolically, the

monetarist conception of money transmission mechanism can be summarized

below:

↑OMO→↑MS→Spending→↑GNP

The monetarist argument centers on the old quantity theory of money. If velocity

of money in circulation is constant, variation in money supply will directly affect

prices and output or income (GNP).

2.3.4 Anticipated Income Theory

29
This theory states that banks should involves themselves in a broad range of

lending which may include long term loans to business, consumer installment

loans and amortized real estate mortgage loans considering the fact that the

likelihood of loan repayment which generates a cash flow that supplement bank

liquidity depends on the anticipated income of the borrower and not the use made

of the funds. This implies that a high excess reserve increases profitability of banks

by increasing the availability of loanable investment funds.

2.3.5 Shiftability Theory

The central thesis of this theory holds that the liquidity of a bank depends on its

ability to shift its assets to someone else at a predictable price. Better still; the

theory of shiftability exposes the banks vulnerability to government security for

liquidity. Whether or not a bank can quickly realize liquidity through this means

depends on the marketability of the securities and their relative prices. The theory

tries to broaden the list of assets demand legitimate for ownership and hence

redirected the attention of bankers and the banking authorities from loan to

investment as source of bank liquidity.

It is hypothesized that an increase in capital investment will lead to commercial

banks profitability. However, increase in profits may also motivate further increase

in capital investment, which in turn expands the scope of banking operations for

30
increased profitability. Adequate capital investment provides for a bank to perform

the intermediation function and provide related financial services. It also provides

protection in conditions of near economic collapse against unanticipated adversity

leading to loss in excess of normal expectations and permits banks to continue

operations in periods of difficulty until a normal level of earning is restored.

2.4 Empirical Review

Ayodeji & Oluwole (2018) examined the impact of monetary policy on bank

performance in Nigeria by developing a model that is able to investigate how

monetary policy of the government has affected bank performance through the use

of multi-variable regression analysis. Unit root test was conducted and all their

estimating variables were stationary at first difference except the component of

interest rate which showed that their model interpretation would not be spurious

and a true representation of the relationships that exists between the explained and

explanatory variables. Error Correction Model was introduced in their estimation

in order to have a parsimonious model. From their result, two variables (money

supply and exchange rate) had a positive but fairly insignificant impact on bank

performance. Measures of interest rate and liquidity ratio on the other hand, had a

negative but highly significant impact on bank performance. In addition, Engle-

Granger co-integration test was done and showed the existence of a long run

relationship between monetary policy and bank performance in Nigeria. It was

31
recommended that partial autonomy should be replaced with full autonomy for the

central banks in Nigeria which is invariably subjected to government interference

and its politics. Finally, monetary policies should be used to create a favorable

investment climate by facilitating the emergency of market based interest rate and

exchange rate regimes that attract both domestic and foreign investments.

Sanusi (2018) examined the effect of monetary policy on the financial performance

of deposit money banks in Nigeria. Specifically, his study established the effect of

central bank rate on the financial performance of deposit money banks, he also

establish the effect of reserve ratio requirement on the financial performance of

deposit money banks. The methodology used for data collection was mainly from

primary source which included questionnaire and personal interview in order to

have knowledge of monetary policy on the financial performance in Union Bank

Plc. Information was also gathered from the secondary source which included

literature review of previous research, consultation of textbooks and internet.

Simple percentage and Chi-square statistical method were used to analyze the data

collected before reaching conclusion. The findings of his research indicated that

deposit money bank policy affect banking operations in its bid to regulate money

supply in the economy with particular reference to deposit and credit creation. His

recommendation was that while bank size was found to lead to better financial

32
performance, it is important that banks understand the source of its funds and the

costs associated with the funds.

Ufoeze, Odimgbe, Ezeabalisi & Alajekwu (2018) investigated the effect of

monetary policy on performance of deposit money banks in Nigeria. Their time

series data was the market-controlled period covering 1986 to 2016. Their study

adopted an Ordinary Least Squared technique and also conducted the unit root and

co-integration tests. Their study showed that long run relationship exists among the

variables. In addition, the core finding of their study showed that monetary policy

rate, interest rate, and investment have insignificant positive effect on the

performance of deposit money banks in Nigeria. Money supply however has

significant positive effect on the performance of deposit money banks in Nigeria.

Exchange rate has significant negative effect on the performance of deposit money

banks in Nigeria. Thus, their study concluded that monetary policy can be

effectively used to control the performance of deposit money banks.

Aginam & Obi-Nwosu (2019) examined the effect of monetary policy on banks

performance in Nigeria. The variables of monetary policy rate, liquidity ratio,

broad money supply and interest rate were regressed on return on equity for the

period of 30 years (1987–2017). Their study adopted an ex-post facto research

design because the data for the study are secondary data that already existed.

Econometric techniques, including Augmented Dicker Fuller and Philip Perron

33
tests for unit roots and Ordinary Least Square (OLS) were employed for the

analysis. The result of their study indicated that monetary policy rate, liquidity

ratio and broad money supply have positive and significant effect on return on

equity while interest rate has negative and insignificant effect on return on equity

within the period under review. Their study thus concluded that monetary policy

could be used to influence the performance of deposit money banks in Nigeria.

Their study recommended that interest rate should be reduced to a single digit.

Bank management should ensure that capital is properly channeled to the

productive sector of the economy. The relevant monetary authorities should apply

with caution monetary policy variables to significantly influence commercial banks

loans and advances. Expansionary monetary policy should be adopted by the CBN

to force down interest rate and increase money supply because a fall in the bank

rate will reduce interest on loans made by commercial banks. This will encourage

more customers to secure loans from their banks thereby, increasing investment

opportunities in the country, ceteris paribus.

Ibrahim (2019) assessed the impact of monetary policy on the performance of

deposit money banks in Nigeria. Three objectives guided his study. He employed

quarterly data spanning 1986:Q1–2018:Q4, and used the Autoregressive

Distributed Lag (ARDL) model, and the Granger causality test to carry out its

empirical analysis and achieving its objectives. Findings from his study revealed

34
that the monetary policy rate had a positive impact on the performance of deposit

money bank, but it was however not statistically significant. The broad money

supply as a monetary policy instrument had a much more positive and highly

statistically significant impact on the performance of deposit money banks in

Nigeria. As such, his study recommended that the CBN should embark on a

comprehensive monitoring of monetary instruments and aggregates and place less

emphasis on inflation targeting alone. It is important to use other instruments

which the central bank can control effectively like the broad money supply.

Cross & Victor (2020) investigated the relationship between monetary policy and

bank performance in Nigeria. The scope of the study covered the span of 18 years

from 2000–2017. Their study used the time series data of the variables in question

and adopted the ordinary least square technique; they also conducted the ADF unit

root test for stationary as well as the error correction mechanism for co-integration

tests. Their study showed that, there is a long run relationship between monetary

policy and bank performance. Their study found out that, interest rate, open market

operations, and exchange rate have positive impact on bank performance, on the

other hand monetary policy rate and cash reserve ratio have inverse relationship

with bank performance. Hence, their study recommended that government

appropriate policy action on the relevant variables as it concerns the set goals and

objectives.

35
Okoh & Otene (2020) examined the impact of monetary policy on bank

performance in Nigeria. The Vector Auto-regression Technique (VAR) was used

to analyze data between 1980 and 2017. The result of their paper showed that

monetary policy represented by money supply (M2) has a positive impact on bank

performance. Monetary policy variables which are interest rate, money supply,

exchange rate and liquidity ratio all had a negative and non-significant relationship

with inflation. Their paper however recommended that the gap between monetary

policy formulation and implementation should be bridged so as to ensure the

attainment of the set goals.

Daniel, Phillip, James & Ibrahim (2020) investigated the effectiveness of monetary

policy with the aim of examining the effects of money supply and exchange rate on

bank performance in Nigeria. To obtain a robust and reliable results from the data

employed in their empirical investigation, various economic techniques like

Augmented Dickey Fuller Unit Root Test, Johansen Co-integration Test and

Vector Error Correction Model (VECM) were employed and the following

information surfaced: None of the variables was stationary at level meaning they

all have unit roots. But all the variables became stationary after first difference.

Their study found that except exchange rate, all the other monetary instruments

reflect direct impacts on bank performance in the long run. Broad money supply

had positive and significant impact on bank performance in the long run, exchange

36
rate had a negative and significant impact on bank performance in the long run,

and foreign reserve had positive and insignificant impact on bank performance in

the long run. In terms of short run, their study found that broad money supply had

a negative relationship with bank performance at lag four. The short run result also

showed a negative relationship between exchange rate and bank performance; and

with foreign reserve at lag four. Therefore, their study recommended stabilization

in exchange rate, proper regulation of money supply and prudential use of the

accumulated reserve. Hence, they concluded that maintenance of bank

performance using monetary policy measures largely depend on the stabilization of

both internal and external values of Naira; proper coordination of monetary and

fiscal policies among others.

Timothy (2022) examined the effectiveness of monetary policy in stimulating the

performance of banks in Nigeria between 1990 and 2019. Secondary data were

sourced mainly from CBN publications. His theoretical framework was based on

the Keynesian transmission mechanism. In the cause of empirical investigation,

various advanced econometric techniques like Augmented Dickey Fuller Unit Root

Test, ARDL Bounds Test and Error Correction Mechanism (ECM) were employed

and his result revealed that all the variables were stationary at first difference

except monetary policy rate that was stationary at level, meaning that the variables

were integrated of different order justifying ARDL Bounds Test and error

37
correction mechanism test. The ARDL Bounds Test result indicated that there is

long run relationship among the variables with the lower bound and upper bound

less than the calculated at 5% level of significant. The result of the error correction

mechanism (ECM) test indicates an 88% adjustment back to equilibrium. He

therefore recommended that since the performance of banks in Nigeria is greatly

influenced in the long-run by interest rate and reserve requirement making

monetary policy an effective tool in stimulating the performance of banks. He also

recommended that Nigerian government through its monetary authorities should

unveil other policies that will stimulate the performance of banks not only in the

long run but also, in the short run period.

Umar, Iliya, Nazeef & Rabiu (2022) analyzed the effect of monetary policy on the

performance of deposit money banks in Nigeria. Their research was based on

secondary source of data extracted out from Central Bank of Nigeria (CBN)

statistical bulletin. The Autoregressive Distributed Lag (ARDL) approach to co-

integration was applied to achieve the objective. Their empirical results revealed

that both in the long run and short run, bank lending rate has been found to have a

significant positive impact on banks loans and advances, this means that bank

lending rate has significant positive impact on the performance of deposit money

banks in Nigeria. While liquidity rate has significant impact in the long run but has

no significant impact in the short run likewise interest rate has no significant

38
impact in the long run but in the short run has significant and positive impact on

the performance of deposit money banks. Their study concluded that increasing the

interest rate can equally lead to improve performance in the short run as this can

motivate customers to save more but this effect will neutralize in the long run.

Their study recommended that the central bank of Nigeria should redefine its

monetary policy instruments to make them more attractive to the banks. This will

make banks to embrace them beyond mere.

39
 CHAPTER THREE

RESEARCH METHODS

3.1 Preamble

This chapter outlines the method adopted in the conduct of the study. It specifies

information on the research methods to be used for the study, and they include

research design, population of the study, sources and methods of data collection,

estimation techniques, model specification and definition of variables.

3.2 Research Design

Research design refers to the overall strategy chosen by a researcher to integrate

the different components of the study in a coherent and logical way, thereby,

ensuring it effectively addresses the research problem (De Vaus, 2001).

The ex post facto research design was used in the carrying out the study because it

is the best method that can be used to explain the effect of the given independent

variables on the dependent variable. Ex post facto design is a quasi-experimental

study examining how an independent variable, present prior to the study in the

participants, affect a dependent variable. Ex post facto study or after-the-fact

research is a category of research design in which the investigation starts after the

fact has occurred without interference from the researcher. Also, the study

40
employed various descriptive and inferential statistics in examination of the effect

of monetary policy shocks on deposit money banks stability in Nigeria. The

descriptive statistics gives stylized fact on the features of the main variables in the

model while the inferential statistics facilitates the establishment of the extent of

agreement or divergence in examining the effect of monetary policy shocks on

deposit money banks stability in Nigeria.

3.3 Population of the Study

The population of study comprised of banks’ total asset, cash reserve ratio,

monetary policy rate, treasury bill rate and banks’ credit to private sector (as a

control variable) in Nigeria.

3.4 Sources and Methods of Data Collection

The sources used in collecting data in any study or investigation depends on the

type of data needed and the purpose of the investigation. However, in achieving the

set objectives of this study, this study will employ the use of secondary data

collection. It relied on time series data from the Central Bank of Nigeria (CBN)

Statistical Bulletin. The data collected are on annually basis from 1991–2021 (a

period of 31 years).

3.5 Estimation Technique

41
The application of the Augmented Dickey-Fuller (ADF) test statistics, the

Johansen Co-integration techniques and Auto-Regressive Distributed Lag Models

(ARDL) would be used to analyze the short run and long run relationship between

the dependent variable and the independent variables using E-Views 10 Output

Statistical Software.

3.6 Model Specification

The model used for the study was adopted from the work of Uwazie & Aina (2015)

who examined the impact of monetary policy on deposit money banks in Nigeria.

The model is stated thus:

BTA = f(CRR, MPR, TBR, CPS)

Where,

BTA = Banks’ Total Asset.

CRR = Cash Reserve Ratio.

MPR = Monetary Policy Rate.

TBR = Treasury Bill Rate.

CPS = Banks’ Credit to Private Sector (as a control variable).

Hence, the estimating equation used in this model is:

42
BTA = β0 + β1CRR + β2MPR + β3TBR + β4CPS + µt (1)

β0 and µ are the constant and error term respectively. While β 1, β2, β3 and β4 are the

coefficients of cash reserve ratio, monetary policy rate, and banks’ credit to private

sector, respectively.

3.7 Definition of Variables

Cash Reserve Ratio: Cash reserve ratio is a specified minimum fraction of the

total deposits of customers, which deposit money banks have to hold as reserves

either in cash or as deposits with the central bank.

Credit to Private Sector: This alludes to financial resources provided by banks to

the private sector, such as, loans and advances, purchases of non-equity securities,

trade credits and other accounts receivable, which lay out a claim for repayment.

Monetary Policy Rate: Minimum rediscount rate presently called monetary policy

rate is utilize to signal the desired direction of interest rate movement.

Total Asset: Total asset alludes to the aggregate sum of assets owned by an

individual or entity.

Treasury Bills Rate: Treasury bills rate is the rate at which treasury bills which

are government bonds or debt securities with maturity of under a year are issued.

43
 CHAPTER FOUR

RESULT PRESENTATION AND ANALYSIS PRESENTATION

4.1 Preamble

The data used to examine the effect of monetary policy shocks on deposit money

banks stability in Nigeria are presented in the appendix (after references) for the

period of 1991–2021. The application of the Augmented Dickey-Fuller (ADF) test

statistics, the Johansen Co-integration techniques and Auto-Regressive Distributed

Lag Models (ARDL) would be used to analyze the short run and long run

relationship between the dependent variable and the independent variables using E-

Views 10 Output Statistical Software.

4.2 Empirical Results

4.2.1 Stationarity Test

The data gathered are subjected to unit root test. Since carrying out regressions on

non-stationary time series data would lead to spurious regression outcomes, we

employed the widely used Augmented Dickey-Fuller (ADF) test to ascertain the

stationarity of the data.

44
Table 4.1: Unit Root Test Results

Variable Level First Difference Order of

Integration
BTA 2.678073 -3.523589 I(1)
CRR 0.376990 -4.455324 I(1)
MPR -2.955088 -7.789379 I(1)
TBR -2.820270 -6.361856 I(1)
CPS 4.247613 -2.866660 I(0)
5% critical value
BTA -2.967767 -2.967767
CRR -2.963972 -2.967767
MPR -2.963972 -2.967767
TBR -2.963972 -2.967767
CPS -2.963972 -2.967767
Source: Researcher’s Computation, 2024.

The result of the unit root test shows that BTA, CRR, MPR and TBR are non-

stationary at levels, while CPS is stationary at level. However, all the variables

(BTA, CRR, MPR, TBR and CPS) attained stationarity at first difference. Since

the variables are stationary at least at first differences, it is suitable to go on with

co-integration test for long run relationship among the variables of the study.

4.2.2 Co-integration Test using the Johansen Methodology

Co-integration test was carried out to examine the long run relationship among the

variables using Johansen co-integration test.

45
Table 4.2: Unrestricted Co-integration Rank Test result for model

Hypothesis Trace Critical Prob** Hypothesis Max- Critical Prob**

ed No. of Stat. Value ed No. of Eigen Value
CE(s) (0.05) CE(s) Stat. (0.05)
None* 106.6675 69.81889 0.0000 None* 46.53500 33.87687 0.0010
At most 1* 60.13248 47.85613 0.0023 At most 1 26.17711 27.58434 0.0748
At most 2* 33.95537 29.79707 0.0157 At most 2 14.66403 21.13162 0.3131
At most 3* 19.29134 15.49471 0.0127 At most 3 11.59631 14.26460 0.1267
At most 4* 7.695035 3.841466 0.0055 At most 4* 7.695035 3.841466 0.0055
Source: Researcher’s Computation, 2024.

The results of the Unrestricted Co-integration Rank test for the model is presented

in Table 4.2 above. Starting with the null hypothesis that there are no co-

integrating vector (number of co-integrating vectors = 0) in the model, the results

show that there exists five co-integrating equations Trace statistics and at least two

Max-Eigen statistics, as both the Trace and Max-Eigen statistics reject the null of

number of co-integrating vectors = 0 as against the alternative of number of co-

integrating vectors = 1 at 5 per cent level of significance which shows that there is

a long run relationship between banks’ total asset, cash reserve ratio, monetary

policy rate, treasury bill rate and banks’ credit to private sector in Nigeria.

4.2.3 Short Run Error Correction Representation

The results of the short run error correction representation for the model is reported

in Table 4.3 below.

46
Table 4.3: Short Run Error Correction Representation for the Model

Variable Coefficient Std. Error t-Statistic Prob.

ECM(-1) -0.872028 0.205601 -4.241353 0.0002
C 21.72547 262.1476 0.082875 0.9345
Source: Researcher’s Computation, 2024.

The error correction mechanism (ECM) which is -0.872028 is statistically

significant and has the appropriate negative sign. It suggests that there is a high

adjustment process in the effect of monetary policy shocks on deposit money

banks stability. It is also a confirmation that indeed banks’ total asset, cash reserve

ratio, monetary policy rate, treasury bill rate and banks’ credit to private sector in

Nigeria are co-integrated.

4.3 Regression Result

This section shows the data results of the two outputs of the Auto-Regressive

Distributed Lag Models (ARDL) which are the long run estimates and The Short

run results.

Table 4.4: Long Run Regression Estimates

Variables Coefficient Std. Error t-Statistic Prob.

CRR -0.652790 0.090240 -7.233917 0.0054
MPR 2.870344 0.386377 7.428873 0.0050
TBR -0.589460 0.184525 -3.194469 0.0495
CPS 1.079974 0.021010 51.40233 0.0000
C -3.773336 0.566073 -6.665816 0.0069
Source: Researcher’s Computation, 2024.

47
From the coefficients of the variables in Table 4.4 above, CRR and TBR have

negative effect on BTA, while MPR and CPS was shown to have positive effect on

BTA. The long run estimates are relied on and used for further analysis.

Table 4.5: Short Run Result

Variables Coefficient Std. Error t-Statistic Prob.

D(BTA(-1)) 0.637475 0.516984 1.233066 0.3428
D(BTA(-2)) -1.987968 1.409073 -1.410834 0.2937
D(BTA(-3)) -2.299879 1.772654 -1.297422 0.3240
D(BTA(-4)) 2.995083 1.884296 1.589497 0.2529
D(CRR) 0.028702 0.331306 0.086633 0.9389
D(CRR(-1)) -0.606701 0.380680 -1.593731 0.2520
D(CRR(-2)) -0.442988 0.508524 -0.871124 0.4755
D(CRR(-3)) -0.445158 0.229120 -1.942902 0.1915
D(CRR(-4)) 0.168195 0.172063 0.977522 0.4314
D(MPR) 0.069780 0.770588 0.090555 0.9361
D(MPR(-1)) 0.955325 0.737818 1.294798 0.3247
D(MPR(-2)) 1.529442 1.024609 1.492708 0.2741
D(MPR(-3)) 1.963726 1.105821 1.775809 0.2178
D(MPR(-4)) 0.213617 0.239822 0.890731 0.4671
D(TBR) 0.522844 0.552714 0.945958 0.4440
D(TBR(-1)) 0.017886 0.473650 0.037763 0.9733
D(TBR(-2)) -0.594034 0.406133 -1.462660 0.2811
D(TBR(-3)) -0.573933 0.319316 -1.797384 0.2141
D(CPS) 0.498972 0.530247 0.941018 0.4460
D(CPS(-1)) 1.010621 0.999256 1.011373 0.4183
D(CPS(-2)) -0.524039 1.210134 -0.433042 0.7072
D(CPS(-3)) -0.451898 0.744389 -0.607072 0.6055
D(CPS(-4)) 0.891087 0.689890 1.291635 0.3256
C 0.078046 0.194144 0.402002 0.7266
Source: Researcher’s Computation, 2024.

48
4.4 Test of Hypotheses

The hypothesis was tested for the significance of the independent variables using

the student’s t-test at 0.05 level of significance. The tabulated t values were

obtained from the student’s t-distribution and 27 degrees of freedom. Where; n =

number of observations, and k = number of parameter estimates. Then, (df) degree

of freedom = n – k i.e. 31 – 4 = 27. From the t-table, t tabulated at 5% level of

significance = 2.052.

Decision Rule: Reject the null hypothesis if the t-calculated is greater than t-

tabulated; if otherwise, do not reject the null hypothesis.

Hypothesis 1

H0: Cash reserve ratio has no significant effect on banks’ total asset in Nigeria.

The t-calculated of the estimated coefficient of the variable (CRR) in Table 4.4

above i.e. -7.233917, was compared with the t-tabulated value of 2.052 to test the

hypothesis. Following the rule above, since t-tabulated is lesser than t-calculated,

we reject the null hypothesis and conclude that cash reserve ratio has a significant

effect on banks’ total asset in Nigeria.

Hypothesis 2

H0: Monetary policy rate has no significant effect on banks’ total asset in Nigeria.

49
The t-calculated of the estimated coefficient of the variable (MPR) in Table 4.4

above i.e. 7.428873, was compared with the t-tabulated value of 2.052 to test the

hypothesis. Following the rule above, since t-tabulated is lesser than t-calculated,

we reject the null hypothesis and conclude that monetary policy rate has a

significant effect on banks’ total asset in Nigeria.

Hypothesis 3

H0: Treasury bill rate has no significant effect on banks’ total asset in Nigeria.

The t-calculated of the estimated coefficient of the variable (TBR) in Table 4.4

above i.e. -3.194469, was compared with the t-tabulated value of 2.052 to test the

hypothesis. Following the rule above, since t-tabulated is lesser than t-calculated,

we reject the null hypothesis and conclude that treasury bill rate has a significant

effect on banks’ total asset in Nigeria.

4.5 Interpretation of Findings

The first finding reveal that cash reserve ratio has a significant effect on banks’

total asset in Nigeria. This finding does not conform to the short run result which

shows no existence of a significant effect of cash reserve ratio on banks’ total

asset. This is shown by the t-calculated value of D(CRR) (0.086633 in table 4.5)

which is lesser than the t-tabulated value of 2.052. This implies that in the long

run, there is a causality between cash reserve ratio and banks’ total asset in

50
Nigeria. But in the short run, there is no causality between cash reserve ratio and

banks’ total asset in Nigeria.

The second finding reveal that monetary policy rate has a significant effect on

banks’ total asset in Nigeria. This finding does not conform to the short run result

which shows no existence of a significant effect of monetary policy rate on banks’

total asset. This is shown by the t-calculated value of D(MPR) (0.090555 in table

4.5) which is lesser than the t-tabulated value of 2.052. This implies that in the

long run, there is a causality between monetary policy rate and banks’ total asset in

Nigeria. But in the short run, there is no causality between monetary policy rate

and banks’ total asset in Nigeria.

The third finding reveal that treasury bill rate has a significant effect on banks’

total asset in Nigeria. This finding does not conform to the short run result which

shows no existence of a significant effect of treasury bill rate on banks’ total asset.

This is shown by the t-calculated value of D(TBR) (0.945958 in table 4.5) which is

lesser than the t-tabulated value of 2.052. This implies that in the long run, there is

a causality between treasury bill rate and banks’ total asset in Nigeria. But in the

short run, there is no causality between treasury bill rate and banks’ total asset in

Nigeria.

51
The general interpretations of the findings of these variables are that a change in

cash reserve ratio, monetary policy rate and treasury bill rate will definitely lead to

a change in banks’ total asset in Nigeria.

52
 CHAPTER FIVE

SUMMARY OF FINDINGS, CONCLUSION AND

RECOMMENDATIONS

5.1 Preamble

The study’s last chapter contains a summary of all findings derived and analyzed

by the researcher, as well as a conclusion based on these findings and

recommendations to Deposit Money Banks and Central Bank of Nigeria (CBN) on

the true nature of monetary policy shocks effect on deposit money banks stability

in Nigeria. The chapter concluded with recommendations for future research on the

subject of discourse.

5.2 Summary of Findings

This section contains a summary of the study’s principal conclusions, which are

based on the examination of the effect of monetary policy shocks on deposit

money banks stability in Nigeria. As a result, it is more convenient to summarize

these findings as follows:

Cash reserve ratio has a significant effect on banks’ total asset in Nigeria.

Monetary policy rate has a significant effect on banks’ total asset in Nigeria.

Treasury bill rate has a significant effect on banks’ total asset in Nigeria.

53
5.3 Conclusion

The empirical investigation on the effect of monetary policy shocks on deposit

money banks stability in Nigeria was examined with the application of the

Augmented Dickey-Fuller (ADF) test statistics, the Johansen co-integration

techniques, the error correction methodology (short run) and Auto-Regressive

Distributed Lag Models (ARDL) on a multiple regression framework. The results

from the Augmented Dickey-Fuller (ADF) test statistics confirms the stationarity

of the selected monetary policy shocks and deposit money banks stability variables

(banks’ total asset, cash reserve ratio, monetary policy rate, treasury bill rate and

banks’ credit to private sector) were all stationary at first difference respectively.

The Johansen co-integration test results indicated a long run relationship between

banks’ total asset, cash reserve ratio, monetary policy rate, treasury bill rate and

banks’ credit to private sector in Nigeria, while the results of the error correction

model suggested that there is a high adjustment process in the effect of monetary

policy shocks on deposit money banks stability. The Auto-Regressive Distributed

Lag Models (ARDL) revealed that cash reserve ratio, monetary policy rate and

treasury bill rate have a significant effect on banks’ total asset in Nigeria.

5.4 Recommendations

54
Based on the empirical findings of the study, the following recommendations were

made:

i. Monetary authority should keep monetary policy rate at a moderate level

that would not generate instability in the banking system.

ii. Central Bank of Nigeria should adjust the monetary policy rate by reducing

the cash reserve ratio which will increase liquidity to enable the Deposit

Money Banks to discharge their lending and investment duties effectively to

the public.

iii. Since excess liquidity could cause banking system instability, the Central

Bank of Nigeria should always ensure cash reserve ratio and liquidity ratio

are maintained at conformable levels to ensure stability of the banking

system.

iv. It is important that monetary and fiscal policies be complimentary and not

working at variance.

v. There is a need for Central Bank of Nigeria and the ministry of finance to

work more closely to objectively articulate policies in the same economic

direction.

55
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64
 Appendix

Statistical Data

YEAR BTA CRR MPR TBR CPS

1991 117.51 10.00 15.50 15.00 41.35
1992 159.19 10.00 17.50 21.00 58.12
1993 226.16 10.00 26.00 26.90 127.12
1994 295.03 10.00 13.50 12.50 143.42
1995 385.14 10.00 13.50 12.50 180.00
1996 458.78 10.00 13.50 12.25 238.60
1997 584.38 10.00 13.50 12.00 316.21
1998 694.62 10.00 13.50 12.95 351.96
1999 1070.02 10.00 18.00 17.00 431.17
2000 1568.84 10.00 14.00 12.00 530.37
2001 2247.04 10.00 20.50 12.95 764.96
2002 2766.88 10.00 16.50 18.88 930.49
2003 3047.86 10.00 15.00 15.02 1096.54
2004 3753.28 10.00 15.00 14.21 1421.66
2005 4515.12 10.00 13.00 7.00 1838.39
2006 7172.93 5.00 10.00 8.80 2290.62
2007 10847.12 3.00 9.50 6.91 3668.66
2008 15836.64 2.00 9.75 4.50 7899.14
2009 17292.32 2.00 6.00 6.13 9889.58
2010 17308.32 2.00 6.25 10.25 10518.17
2011 19362.94 8.00 12.00 16.75 9600.02
2012 21235.29 12.00 12.00 17.20 13293.64
2013 24415.73 12.00 12.00 13.34 14461.41
2014 27508.45 20.00 13.00 15.99 16753.00
2015 28237.83 20.00 11.00 16.28 18688.42
2016 31953.50 22.50 14.00 18.50 21025.24
2017 35038.11 22.50 14.00 18.98 22459.18
2018 38407.79 22.50 14.00 14.55 22646.33
2019 43578.60 22.50 13.50 15.00 25676.87
2020 51361.72 27.50 11.50 6.31 29030.01
2021 59494.51 27.50 11.50 10.00 32868.49

65
BTA at Level

Null Hypothesis: BTA has a unit root

Exogenous: Constant
Lag Length: 1 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic 2.678073 1.0000
Test critical values: 1% level -3.679322
5% level -2.967767
10% level -2.622989
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(BTA)
Method: Least Squares
Date: 01/19/24 Time: 18:27
Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
BTA(-1) 0.071481 0.026691 2.678073 0.0127
D(BTA(-1)) 0.479424 0.205573 2.332140 0.0277
C 185.0149 317.3540 0.582992 0.5649
R-squared 0.730959 Mean dependent var 2046.046
Adjusted R-squared 0.710264 S.D. dependent var 2272.307
S.E. of regression 1223.118 Akaike info criterion 17.15389
Sum squared resid 38896434 Schwarz criterion 17.29534
Log likelihood -245.7314 Hannan-Quinn criter. 17.19819
F-statistic 35.31984 Durbin-Watson stat 1.821425
Prob(F-statistic) 0.000000

BTA at First Difference

66
Null Hypothesis: D(BTA) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -3.523589 0.8725
Test critical values: 1% level -3.679322
5% level -2.967767
10% level -2.622989
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(BTA,2)
Method: Least Squares
Date: 01/19/24 Time: 18:28
Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(BTA(-1)) -0.067902 0.129685 -3.523589 0.6048
C 398.9889 340.4308 1.172012 0.2514
R-squared 0.010051 Mean dependent var 279.0038
Adjusted R-squared -0.026613 S.D. dependent var 1338.040
S.E. of regression 1355.728 Akaike info criterion 17.32854
Sum squared resid 49625979 Schwarz criterion 17.42283
Log likelihood -249.2638 Hannan-Quinn criter. 17.35807
F-statistic 0.274145 Durbin-Watson stat 2.070234
Prob(F-statistic) 0.604836

CRR at Level

Null Hypothesis: CRR has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)

67
 t-Statistic Prob.*
Augmented Dickey-Fuller test statistic 0.376990 0.9785
Test critical values: 1% level -3.670170
5% level -2.963972
10% level -2.621007
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(CRR)
Method: Least Squares
Date: 01/19/24 Time: 18:29
Sample (adjusted): 1992 2021
Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
CRR(-1) 0.025339 0.067214 0.376990 0.7090
C 0.284753 0.908369 0.313478 0.7562
R-squared 0.005050 Mean dependent var 0.583333
Adjusted R-squared -0.030484 S.D. dependent var 2.400012
S.E. of regression 2.436318 Akaike info criterion 4.683193
Sum squared resid 166.1981 Schwarz criterion 4.776607
Log likelihood -68.24790 Hannan-Quinn criter. 4.713077
F-statistic 0.142122 Durbin-Watson stat 1.743535
Prob(F-statistic) 0.709023

CRR at First Difference

Null Hypothesis: D(CRR) has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.455324 0.0015
Test critical values: 1% level -3.679322
68
 5% level -2.967767
10% level -2.622989
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(CRR,2)
Method: Least Squares
Date: 01/19/24 Time: 18:31
Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(CRR(-1)) -0.847383 0.190196 -4.455324 0.0001
C 0.511352 0.470213 1.087490 0.2864
R-squared 0.423692 Mean dependent var 0.000000
Adjusted R-squared 0.402347 S.D. dependent var 3.176364
S.E. of regression 2.455584 Akaike info criterion 4.701079
Sum squared resid 162.8071 Schwarz criterion 4.795375
Log likelihood -66.16564 Hannan-Quinn criter. 4.730611
F-statistic 19.84991 Durbin-Watson stat 2.076210
Prob(F-statistic) 0.000132

MPR at Level

Null Hypothesis: MPR has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.955088 0.0509
Test critical values: 1% level -3.670170
5% level -2.963972
10% level -2.621007
*MacKinnon (1996) one-sided p-values.
69
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(MPR)
Method: Least Squares
Date: 01/19/24 Time: 18:32
Sample (adjusted): 1992 2021
Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
MPR(-1) -0.475466 0.160898 -2.955088 0.0063
C 6.317159 2.265958 2.787854 0.0094
R-squared 0.237733 Mean dependent var -0.133333
Adjusted R-squared 0.210509 S.D. dependent var 3.748410
S.E. of regression 3.330585 Akaike info criterion 5.308514
Sum squared resid 310.5983 Schwarz criterion 5.401927
Log likelihood -77.62770 Hannan-Quinn criter. 5.338397
F-statistic 8.732542 Durbin-Watson stat 2.179955
Prob(F-statistic) 0.006276

MPR at First Difference

Null Hypothesis: D(MPR) has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -7.789379 0.0000
Test critical values: 1% level -3.679322
5% level -2.967767
10% level -2.622989
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

70
Dependent Variable: D(MPR,2)
Method: Least Squares
Date: 01/19/24 Time: 18:32
Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(MPR(-1)) -1.378306 0.176947 -7.789379 0.0000
C -0.259077 0.663703 -0.390350 0.6993
R-squared 0.692042 Mean dependent var -0.068966
Adjusted R-squared 0.680637 S.D. dependent var 6.320282
S.E. of regression 3.571733 Akaike info criterion 5.450451
Sum squared resid 344.4465 Schwarz criterion 5.544747
Log likelihood -77.03154 Hannan-Quinn criter. 5.479984
F-statistic 60.67443 Durbin-Watson stat 1.996073
Prob(F-statistic) 0.000000

TBR at Level

Null Hypothesis: TBR has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.820270 0.0674
Test critical values: 1% level -3.670170
5% level -2.963972
10% level -2.621007
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(TBR)
Method: Least Squares
Date: 01/19/24 Time: 18:33

71
Sample (adjusted): 1992 2021
Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
TBR(-1) -0.450538 0.159750 -2.820270 0.0087
C 6.015461 2.322281 2.590324 0.0151
R-squared 0.221225 Mean dependent var -0.166667
Adjusted R-squared 0.193412 S.D. dependent var 4.676436
S.E. of regression 4.199918 Akaike info criterion 5.772348
Sum squared resid 493.9007 Schwarz criterion 5.865761
Log likelihood -84.58521 Hannan-Quinn criter. 5.802231
F-statistic 7.953924 Durbin-Watson stat 1.833234
Prob(F-statistic) 0.008718

TBR at First Difference

Null Hypothesis: D(TBR) has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.361856 0.0000
Test critical values: 1% level -3.679322
5% level -2.967767
10% level -2.622989
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(TBR,2)
Method: Least Squares
Date: 01/19/24 Time: 18:34
Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
72
 D(TBR(-1)) -1.180006 0.185481 -6.361856 0.0000
C -0.433250 0.858606 -0.504597 0.6179
R-squared 0.599841 Mean dependent var -0.079655
Adjusted R-squared 0.585020 S.D. dependent var 7.162553
S.E. of regression 4.614038 Akaike info criterion 5.962556
Sum squared resid 574.8124 Schwarz criterion 6.056852
Log likelihood -84.45706 Hannan-Quinn criter. 5.992088
F-statistic 40.47321 Durbin-Watson stat 2.115377
Prob(F-statistic) 0.000001

CPS at Level

Null Hypothesis: CPS has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic 4.247613 1.0000
Test critical values: 1% level -3.670170
5% level -2.963972
10% level -2.621007
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(CPS)
Method: Least Squares
Date: 01/19/24 Time: 18:34
Sample (adjusted): 1992 2021
Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
CPS(-1) 0.093940 0.022116 4.247613 0.0002
C 354.0859 266.8521 1.326900 0.1953

73
R-squared 0.391862 Mean dependent var 1094.238
Adjusted R-squared 0.370143 S.D. dependent var 1394.817
S.E. of regression 1106.976 Akaike info criterion 16.92099
Sum squared resid 34311084 Schwarz criterion 17.01441
Log likelihood -251.8149 Hannan-Quinn criter. 16.95088
F-statistic 18.04222 Durbin-Watson stat 1.839058
Prob(F-statistic) 0.000216

CPS at First Difference

Null Hypothesis: D(CPS) has a unit root

Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -2.866660 0.0617
Test critical values: 1% level -3.679322
5% level -2.967767
10% level -2.622989
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(CPS,2)
Method: Least Squares
Date: 01/19/24 Time: 18:35
Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
D(CPS(-1)) -0.526713 0.183737 -2.866660 0.0079
C 658.2901 300.5719 2.190125 0.0373
R-squared 0.233341 Mean dependent var 131.7831
Adjusted R-squared 0.204946 S.D. dependent var 1436.974
S.E. of regression 1281.289 Akaike info criterion 17.21559

74
Sum squared resid 44325958 Schwarz criterion 17.30989
Log likelihood -247.6261 Hannan-Quinn criter. 17.24513
F-statistic 8.217741 Durbin-Watson stat 2.088055
Prob(F-statistic) 0.007946

Unrestricted Co-integration Rank Test result for model

Date: 01/19/24 Time: 18:36

Sample (adjusted): 1993 2021
Included observations: 29 after adjustments
Trend assumption: Linear deterministic trend
Series: BTA CRR MPR TBR CPS
Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05
Critical
No. of CE(s) Eigenvalue Statistic Value Prob.**
None * 0.799041 106.6675 69.81889 0.0000
At most 1 * 0.594510 60.13248 47.85613 0.0023
At most 2 * 0.396890 33.95537 29.79707 0.0157
At most 3 * 0.329595 19.29134 15.49471 0.0127
At most 4 * 0.233059 7.695035 3.841466 0.0055
Trace test indicates 5 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05
Critical
No. of CE(s) Eigenvalue Statistic Value Prob.**
None * 0.799041 46.53500 33.87687 0.0010
At most 1 0.594510 26.17711 27.58434 0.0748

75
 At most 2 0.396890 14.66403 21.13162 0.3131
At most 3 0.329595 11.59631 14.26460 0.1267
At most 4 * 0.233059 7.695035 3.841466 0.0055
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

BTA CRR MPR TBR CPS
-0.001173 -0.294673 0.517241 -0.381128 0.001954
1.85E-05 0.113383 -0.357142 0.372687 -0.000344
-0.000110 -0.301461 0.509490 0.005577 0.000449
-0.000419 -0.203757 -0.362962 0.296211 0.000675
-0.000876 0.081162 -0.136050 0.011013 0.001309

Unrestricted Adjustment Coefficients (alpha):

D(BTA) -995.3247 -415.3410 27.73691 -97.78601 64.58140
D(CRR) -0.360639 -0.657444 0.580163 1.075438 0.079863
D(MPR) -0.587166 -0.524909 -1.366197 0.938488 0.750857
D(TBR) 1.335991 -1.918095 -1.874520 0.755357 0.152855
D(CPS) -424.8322 35.26351 -114.6160 129.8512 -274.2040

1 Cointegrating Equation(s): Log likelihood -667.9114

Normalized cointegrating coefficients (standard error in parentheses)
BTA CRR MPR TBR CPS
1.000000 251.1982 -440.9297 324.8984 -1.665485
(37.7629) (78.2421) (52.2186) (0.03942)

Adjustment coefficients (standard error in parentheses)

D(BTA) 1.167584
(0.19181)
D(CRR) 0.000423
(0.00057)
D(MPR) 0.000689
(0.00080)
D(TBR) -0.001567
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 (0.00104)
D(CPS) 0.498357
(0.16858)

2 Cointegrating Equation(s): Log likelihood -654.8228

Normalized cointegrating coefficients (standard error in parentheses)
BTA CRR MPR TBR CPS
1.000000 0.000000 365.2815 -522.1813 -0.941747
(284.819) (236.024) (0.11134)
0.000000 1.000000 -3.209462 3.372156 -0.002881
(1.10415) (0.91499) (0.00043)

Adjustment coefficients (standard error in parentheses)

D(BTA) 1.159902 246.2025
(0.16126) (43.3989)
D(CRR) 0.000411 0.031728
(0.00054) (0.14612)
D(MPR) 0.000679 0.113506
(0.00079) (0.21360)
D(TBR) -0.001603 -0.611159
(0.00092) (0.24864)
D(CPS) 0.499009 129.1847
(0.16837) (45.3117)

3 Cointegrating Equation(s): Log likelihood -647.4908

Normalized cointegrating coefficients (standard error in parentheses)
BTA CRR MPR TBR CPS
1.000000 0.000000 0.000000 321.1676 -1.398728
(71.7853) (0.05149)
0.000000 1.000000 0.000000 -4.037735 0.001134
(0.77007) (0.00055)
0.000000 0.000000 1.000000 -2.308765 0.001251
(0.35232) (0.00025)

Adjustment coefficients (standard error in parentheses)

D(BTA) 1.156849 237.8409 -352.3550
(0.16182) (59.9486) (111.113)

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 D(CRR) 0.000347 -0.143169 0.343850
(0.00053) (0.19468) (0.36084)
D(MPR) 0.000829 0.525361 -0.812303
(0.00072) (0.26655) (0.49404)
D(TBR) -0.001396 -0.046064 0.421011
(0.00080) (0.29621) (0.54902)
D(CPS) 0.511625 163.7370 -290.7302
(0.16664) (61.7340) (114.423)

4 Cointegrating Equation(s): Log likelihood -641.6927

Normalized cointegrating coefficients (standard error in parentheses)
BTA CRR MPR TBR CPS
1.000000 0.000000 0.000000 0.000000 -1.196498
(0.05085)
0.000000 1.000000 0.000000 0.000000 -0.001408
(0.00024)
0.000000 0.000000 1.000000 0.000000 -0.000203
(0.00015)
0.000000 0.000000 0.000000 1.000000 -0.000630
(0.00015)

Adjustment coefficients (standard error in parentheses)

D(BTA) 1.197827 257.7655 -316.8624 195.7434
(0.16976) (65.3904) (120.370) (82.7791)
D(CRR) -0.000104 -0.362297 -0.046493 0.214222
(0.00048) (0.18427) (0.33921) (0.23327)
D(MPR) 0.000436 0.334138 -1.152939 0.298530
(0.00072) (0.27791) (0.51158) (0.35182)
D(TBR) -0.001713 -0.199973 0.146845 -1.010741
(0.00082) (0.31755) (0.58453) (0.40199)
D(CPS) 0.457210 137.2789 -337.8613 212.8818
(0.17344) (66.8094) (122.982) (84.5754)

Short Run Error Correction Representation for the Model

Null Hypothesis: ECM has a unit root

78
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.241353 0.0024
Test critical values: 1% level -3.670170
5% level -2.963972
10% level -2.621007
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(ECM)
Method: Least Squares
Date: 01/19/24 Time: 18:38
Sample (adjusted): 1992 2021
Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
ECM(-1) -0.872028 0.205601 -4.241353 0.0002
C 21.72547 262.1476 0.082875 0.9345
R-squared 0.391160 Mean dependent var 111.0580
Adjusted R-squared 0.369415 S.D. dependent var 1802.306
S.E. of regression 1431.200 Akaike info criterion 17.43475
Sum squared resid 57353321 Schwarz criterion 17.52817
Log likelihood -259.5213 Hannan-Quinn criter. 17.46464
F-statistic 17.98907 Durbin-Watson stat 1.777045
Prob(F-statistic) 0.000219

Long Run Regression Estimates

ARDL Long Run Form and Bounds Test

Dependent Variable: D(BTA)
Selected Model: ARDL(4, 4, 4, 3, 4)
Case 2: Restricted Constant and No Trend

79
Date: 01/19/24 Time: 18:41
Sample: 1991 2021
Included observations: 27
Conditional Error Correction Regression
Variable Coefficient Std. Error t-Statistic Prob.
C -5.857530 3.121618 -1.876440 0.1572
BTA(-1)* -1.552348 0.837945 -1.852565 0.1610
CRR(-1) -1.013358 0.474996 -2.133403 0.1226
MPR(-1) 4.455772 1.990413 2.238617 0.1111
TBR(-1) -0.915048 0.454324 -2.014087 0.1374
CPS(-1) 1.676495 0.925931 1.810604 0.1679
D(BTA(-1)) 1.232658 0.677262 1.820061 0.1663
D(BTA(-2)) -0.581860 0.491432 -1.184008 0.3217
D(BTA(-3)) -2.225528 0.810783 -2.744911 0.0710
D(CRR) -0.045744 0.221594 -0.206432 0.8497
D(CRR(-1)) 0.498569 0.353017 1.412308 0.2527
D(CRR(-2)) 0.322929 0.163145 1.979399 0.1421
D(CRR(-3)) -0.131872 0.138942 -0.949116 0.4126
D(MPR) 0.253504 0.438024 0.578746 0.6034
D(MPR(-1)) -3.102017 1.404300 -2.208942 0.1142
D(MPR(-2)) -1.824380 0.760768 -2.398076 0.0960
D(MPR(-3)) -0.252226 0.197956 -1.274155 0.2924
D(TBR) 0.278383 0.189041 1.472602 0.2373
D(TBR(-1)) 1.024652 0.410975 2.493223 0.0882
D(TBR(-2)) 0.542509 0.202817 2.674873 0.0754
D(CPS) 0.547952 0.375200 1.460428 0.2403
D(CPS(-1)) -0.506427 0.391180 -1.294612 0.2861
D(CPS(-2)) -0.364747 0.406650 -0.896955 0.4358
D(CPS(-3)) -0.566834 0.378382 -1.498047 0.2311
* p-value incompatible with t-Bounds distribution.

Levels Equation
Case 2: Restricted Constant and No Trend
Variable Coefficient Std. Error t-Statistic Prob.
CRR -0.652790 0.090240 -7.233917 0.0054

80
 MPR 2.870344 0.386377 7.428873 0.0050
TBR -0.589460 0.184525 -3.194469 0.0495
CPS 1.079974 0.021010 51.40233 0.0000
C -3.773336 0.566073 -6.665816 0.0069
EC = BTA - (-0.6528*CRR + 2.8703*MPR -0.5895*TBR + 1.0800*CPS
-3.7733 )

F-Bounds Test Null Hypothesis: No levels relationship

Test Statistic Value Signif. I(0) I(1)
Asymptotic: n=1000
F-statistic 4.517091 10% 2.2 3.09
k 4 5% 2.56 3.49
2.5% 2.88 3.87
1% 3.29 4.37

Actual Sample Size 27 Finite Sample: n=35

10% 2.46 3.46
5% 2.947 4.088
1% 4.093 5.532

Finite Sample: n=30

10% 2.525 3.56
5% 3.058 4.223
1% 4.28 5.84

Short Run Result

Dependent Variable: D(LBTA)

Method: ARDL
Date: 01/19/24 Time: 18:45
Sample (adjusted): 1996 2021
Included observations: 26 after adjustments
Maximum dependent lags: 4 (Automatic selection)
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (4 lags, automatic): D(LCRR) D(LMPR) D(LTBR)
81
 D(LCPS)
Fixed regressors: C
Number of models evalulated: 2500
Selected Model: ARDL(4, 4, 4, 3, 4)
Variable Coefficient Std. Error t-Statistic Prob.*
D(LBTA(-1)) 0.637475 0.516984 1.233066 0.3428
D(LBTA(-2)) -1.987968 1.409073 -1.410834 0.2937
D(LBTA(-3)) -2.299879 1.772654 -1.297422 0.3240
D(LBTA(-4)) 2.995083 1.884296 1.589497 0.2529
D(LCRR) 0.028702 0.331306 0.086633 0.9389
D(LCRR(-1)) -0.606701 0.380680 -1.593731 0.2520
D(LCRR(-2)) -0.442988 0.508524 -0.871124 0.4755
D(LCRR(-3)) -0.445158 0.229120 -1.942902 0.1915
D(LCRR(-4)) 0.168195 0.172063 0.977522 0.4314
D(LMPR) 0.069780 0.770588 0.090555 0.9361
D(LMPR(-1)) 0.955325 0.737818 1.294798 0.3247
D(LMPR(-2)) 1.529442 1.024609 1.492708 0.2741
D(LMPR(-3)) 1.963726 1.105821 1.775809 0.2178
D(LMPR(-4)) 0.213617 0.239822 0.890731 0.4671
D(LTBR) 0.522844 0.552714 0.945958 0.4440
D(LTBR(-1)) 0.017886 0.473650 0.037763 0.9733
D(LTBR(-2)) -0.594034 0.406133 -1.462660 0.2811
D(LTBR(-3)) -0.573933 0.319316 -1.797384 0.2141
D(LCPS) 0.498972 0.530247 0.941018 0.4460
D(LCPS(-1)) 1.010621 0.999256 1.011373 0.4183
D(LCPS(-2)) -0.524039 1.210134 -0.433042 0.7072
D(LCPS(-3)) -0.451898 0.744389 -0.607072 0.6055
D(LCPS(-4)) 0.891087 0.689890 1.291635 0.3256
C 0.078046 0.194144 0.402002 0.7266
R-squared 0.943822 Mean dependent var 0.193847
Adjusted R-squared 0.297772 S.D. dependent var 0.130061
S.E. of regression 0.108990 Akaike info criterion -2.313923
Sum squared resid 0.023757 Schwarz criterion -1.152603
Log likelihood 54.08100 Hannan-Quinn criter. -1.979505
F-statistic 1.460911 Durbin-Watson stat 2.450756
Prob(F-statistic) 0.485678
*Note: p-values and any subsequent tests do not account for model

82
selection.

83