- understand how the data is gathered - how reliable it is - does the data provide have incentive to distorted the data - interpret the data+align with the domain expert view - does the gap is persist? how? - From your understanding what is the most likely explanation and how we can get the edge on the gap that we have?

Models are important BUT you need to consult different actors involved in your sector/industry, particularly in a volatile environment

When faced with conflicting data in economic modeling, I prioritize data validation to ensure accuracy and consistency. I use scenario analysis to explore different outcomes based on varying assumptions, coupled with sensitivity analysis to identify key drivers of the conflict. Incorporating leading and lagging indicators helps to account for market timing. Peer consultation adds valuable perspectives, and I regularly update models to reflect real-time changes. This structured, adaptive approach helps navigate market shifts effectively.

A key element is the need to create your model followed by determining the data set you require and understanding the gap between historical data you have for the multiple variables in your model.

Verify data sources when faced with conflicting data in economic modeling. and check the accuracy again Use sensitivity analysis to measure the impact of changing variables. and develop scenarios to plan for uncertainty. Consult with industry experts to gain additional insights and adjust your model as needed. Maintain appropriate documentation of data sources and model modifications. Update your model with new data regularly. and use statistical techniques to identify patterns.

Los datos generados por el modelo dependen en gran medida de la calidad de datos con los cuales se alimenten, entonces corresponde consultar varias fuentes de información y mirar con detalle aquella información que esté fuera de la media y altere los resultados

To navigate conflicting information in economic modeling: Verify the source: Guarantee the reliability and correctness of the information. Assess quality: Check for discrepancies in collection methods. Use statistics: Use techniques such as regression analysis to adjust for differences. Consider context: Factors in economic conditions and events Cluster analysis: Analyze data into categories to identify anomalies. Scenario Analysis: Develop scenarios to see different outcomes. Consult with Experts: Get insights from domain experts. Update the model: Refresh the model with new data regularly. Document Assumptions: Note assumptions to clarify limitations. Communicate uncertainty: Be transparent about ideal boundaries

It is useful to identify specific experts that have deep knowledge about the market in question. Follow them on LinkedIn, X or other suitable platform. They frequently provide clarity when non-experts are confused.