De La Salle University – Dasmariñas

FORECASTING THE PRICE OF

CORN IN THE PHILIPPINES

A Thesis
Presented to the Faculty of the
Allied Business Department
College of Business Administration and Accountancy
De La Salle University-Dasmariñas
Dasmariñas City, Cavite

In partial fulfillment
of the requirements for the degree of
Bachelor of Science in Business Administration
(Major in Economics)

BILLY JULIUS M. GESTIADA

June 2018

APPROVAL SHEET

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 De La Salle University – Dasmariñas

This thesis hereto, entitled

“FORECASTING THE PRICE OF

CORN IN THE PHILIPPINES”

Prepared and submitted by BILLY JULIUS M. GESTIADA in partial

fulfillment for the degree of Bachelor of Science in Business Administration Major in
Economics, is recommended for acceptance and approval for ORAL EXAMINATION.

WILLINGTON O. ONUH, Ph.D.

Adviser

Approved by the Committee on Oral Examination with a grade of PASSED on

December 19, 2017.

BENJAMIN A. USIGAN, MSE

Chair

ALICE T. VALERIO, Ph.D. ROMANO ANGELICO T. EBRON, MAE

Member Member

Accepted in partial fulfillment of the requirements for the degree of Bachelor of

Science in Business Administration Major in Economics.

ALICE T. VALERIO, Ph.D. ROSARIO T. REYES, MBA

Thesis Coordinator Chair, Allied Business Department

MARY FELIDORA FLORINOR M. AMPARO, Ph.D.

Dean, College of Business Administration and Accountancy

BIOGRAPHICAL SKETCH

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The author was born in Imus City, Cavite on October 8, 1995. He is the

youngest child of Julito and Elizabeth Gestiada and currently resides with his family in

Imus City, Cavite. He finished elementary education at Ann Marris Montessori School,

Inc., and secondary education at Mil Den Academy. He then pursued the degree of

Bachelor of Science in Business Administration Major in Economics at De La Salle

University – Dasmariñas.

During his elementary and high school years, he participated and won in various

school competitions such as quiz bees and choral competitions. He was also awarded

the Balagtas Award for being the best student in Filipino in the class.

In his college days, as part of his field specialization, he joined the home

organization of the department, the Allied Business Student Society, and assisted the

other students in organizing events. He also passed two Civil Service Commission

(CSC) eligibility examinations, namely: Career Service Examination - Paper and Pencil

Test (Sub-Professional Level); and Career Service Examination - Paper and Pencil Test

(Professional Level).

He presented his undergraduate thesis entitled, “Forecasting the Price of Corn in

the Philippines” during the International Conference on Multidisciplinary Perspectives

in Business Management, Social Sciences & Humanities Research (MPBSH) in Hotel

H2O in Luneta, Manila on March 19, 2018.

ACKNOWLEDGEMENT

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The author would like to extend his deepest gratitude to:

The loving, merciful, and Almighty God who is the one and only Source of

human wisdom and knowledge. Without Him, this paper would not have been

successfully accomplished;

The La Sallian community for providing him numerous opportunities during his

sojourn in the college, and for inculcating in his heart and mind the necessary values in

life;

His beloved parents, Mr. Julito E. Gestiada and Mrs. Elizabeth M. Gestiada, for

their unwavering love, patience, trust, and moral and financial support throughout his

years of study and through the course of writing this thesis;

His elder sister, Lady Belle M. Gestiada, who recently passed away, for all her

advice, motivations, prayers, and encouragements;

His thesis adviser, Dr. Willington O. Onuh, for being so accommodating and

generously helpful whenever he had problems with his research, and for being an

immediate mentor who helped him with every step of the thorough research process;

His panelists, Mr. Benjamin A. Usigan and Mr. Romano Angelico T. Ebron, for

their valuable comments and suggestions which helped to further improve the study;

His thesis coordinator, Dr. Alice T. Valerio, for consistently supervising his

output and for bringing out the best in him through critical evaluation of his work, and

for giving him the opportunity to present his thesis in an international business

conference; and

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His friends, Thea, Renz, and Nico, for relieving him from all the stress of this

thesis, for giving their intellectual comments and suggestions for the improvement of

this study despite academic pressures, and for consistently being by his side in the

entire collegiate journey.

BILLY JULIUS M. GESTIADA

ABSTRACT

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 De La Salle University – Dasmariñas

GESTIADA, B. J., M., Forecasting the Price of Corn in the Philippines.

Bachelor of Science in Business Administration Major in Economics. De La Salle
University-Dasmariñas, Dasmariñas City, Cavite, June 2018. Adviser: Willington O.
Onuh, Ph.D.

The study aimed to provide a forecast of the price of corn in the Philippines

using the monthly farmgate price of yellow corn from 2007 to 2017gathered at the

Philippine Statistics Authority. Despite corn being the Philippines’ second largest

produced crop, there has only been a few studies about it, and it is unclear whether the

farmers are earning from producing corn or not. This univariate study was conducted to

help Philippine corn farmers decide on whether continue production by comparing the

predicting performance of the Autoregressive Integrated Moving Average (ARIMA)

and Autoregressive (AR) models. The historical research design was adapted because

the study dealt with the collection of past data to determine the presence of trend,

seasonal, and cycle components. Evaluative research design was used as well because

the study compared the performances of the ARIMA and AR models.

The trend of monthly farmgate prices of corn in the Philippines is found to be

on an upward trend, with some fluctuations. Technology is the main factor contributing

to the increasing corn prices, while the price fluctuations are caused by several factors,

namely: (a) short- and long-term factors; (b) microeconomic and macroeconomic

factors; and (c) supply- and demand-related factors. Among these factors, technology

and natural conditions are the main contributors.

The accuracy of the two models was assessed using Root Mean Squared Error

(RMSE), Mean Absolute Percentage Error (MAPE), and Theil’s Inequality Coefficient.

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The ARIMA model required six steps, namely: (a) examination; (b) decomposition; (c)

stationarity test; (d) autocorrelation test; (e) fitting; and (f) evaluation. The ARIMA

model (3,1,3) was found to be the best ARIMA model. The results showed that ARIMA

model (3,1,3) performed better than the AR model and is the best model in forecasting

the price of corn in the Philippines based on the values of RMSE, and Theil’s Inequality

Coefficient.

The results also showed that AR model has a closer predictive value of farmgate

prices to the actual value than ARIMA (3,1,3) model. This is attributed to the fact that

AR model performed better than the ARIMA (3,1,3) model on the basis of MAPE

value. This means that there are circumstances when AR model would better fit the

series compared to ARIMA (3,1,3 model).

TABLE OF CONTENTS

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TITLE PAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
APPROVAL SHEET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
BIOGRAPHICAL SKETCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
ACKNOWLEDGEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Background of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Statement of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Objectives of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Hypotheses of the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
Significance of the Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Scope and Limitations of the Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Definition of Terms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
REVIEW OF RELATED LITERATURE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Impact of Commodity Prices to the Economy . . . . . . . . . . . . . . . . . . . . . . . 14
Determinants of Commodity Prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Commodity Price Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
FRAMEWORKS OF THE STUDY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Theoretical Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Judgmental forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Unit root model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
ARIMA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Futures forecast model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Vector autoregressive model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Conceptual Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Sources of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Methods of Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
ARIMA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
AR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
RESULTS AND DISCUSSION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Trend of Farmgate Prices of Corn in the Philippines . . . . . . . . . . . . . . . . . . 35
Accuracy of the Forecasting Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
ARIMA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
AR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Actual Data and Predictive Data of Farmgate Corn Prices . . . . . . . . . . . . . 85
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS . . . . . . . . . . . 90
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

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Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

LIST OF TABLES

Table Page

1 Monthly Farmgate Prices of Corn,

Philippines, 2007-2017. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
47

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2 Augmented Dickey-Fuller Testing of

LFPRICESA at Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Correlogram of LFPRICESA . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4 ARIMA Criteria Table and Summary of

LFPRICESA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5 Correlogram of D(LFPRICESA). . . . . . . . . . . . . . . . . . . . . . . . 54

6 ARIMA Criteria Table and Summary of

D(LFPRICESA). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

7 Autoregression Estimates of LFPRICESA . . . . . . . . . . . . . . . . 62

8 Ordinary Least Squares Estimation of

LFPRICESA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

9 Forecast Evaluation of AR Model. . . . . . . . . . . . . . . . . . . . . . . 67

10 Actual and Forecasted Farmgate Corn Prices
Using ARIMA (3,1,3) Model . . . . . . . . . . . . . . . . . . . . . . . . . . 69

11 Actual and Forecasted Farmgate Corn Prices

Using AR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

12 ARIMA (3,1,3) Model Estimation

Using OLS Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

LIST OF FIGURES

Figure Page

1 Overall framework on forecasting the price

of corn in the Philippines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2 Farmgate prices of corn, Philippines,

2007-2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

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3 Forecast evaluation graph of

ARIMA (2,0,3) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

4 Forecast evaluation graph of

ARIMA (2,1,3) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5 Forecast evaluation graph of

ARIMA (3,0,3) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

6 Forecast evaluation graph of

ARIMA (3,1,3) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

7 Actual and forecasted prices using

ARIMA (3,1,3) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

8 Actual and forecasted prices using

AR model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

9 Forecast comparison graph between the

actual data and predictive data using
ARIMA (3,1,3) model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
10 Forecast comparison graph between the
actual data and predictive data using
AR model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

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