Evaluation of the Economic Impact of the Oil Sector in Peru through an Autoregressive Model (VAR) (#2267)

Authors

Cruzado, Anthony

Gonzales, Diego Fernando

Ruiz, Nhayrin

Sinchitullo, Joseph

Abstract

This study examines the interrelationship between macroeconomic and oil sector variables using a Vector Autoregressive (VAR) model implemented in RStudio, with the aim of identifying patterns and dependencies that can contribute to a better understanding of the energy market. Data were collected and processed from April 2014 to December 2019, allowing eight key variables related to oil production, inflation, and fuel sales to be modeled and predicted based on Gross Domestic Product (GDP). Through the analysis of coefficients estimated in the VAR model, direct and inverse relationships between economic and energy variables were identified, evidencing their impact on economic stability and strategic planning of the sector. The results highlight the importance of considering these factors in the formulation of public policies and business strategies, promoting a data-driven approach to decision-making in highly volatile environments. This study provides a valuable quantitative tool to assess the effects of fluctuations in the oil sector on the national economy and suggests future lines of research to improve the accuracy of predictive models in uncertain scenarios.