FORECASTING ECONOMIC TIME SERIES WITH EXOGENOUS VARIA-BLES: OPPORTUNITIES IN COMPUTATIONAL PARAMETRIZATION TECHNIQUES FOR SUPPORTING PUBLIC DEVELOPMENT POLICIES

This study presents a comprehensive literature review exploring the integration of exogenous variables in economic time series forecasting, focusing on supporting public development policies. It systematically examines various computational parametrization techniques incorporating these external variables, emphasizing their potential to enhance prediction accuracy and address practical challenges in policy-driven contexts. By meticulously analyzing the existing body of research, this review identifies significant gaps and outlines future opportunities for advancing computational techniques and model development. Addressing these gaps could lead to more robust and precise economic forecasting models, offering valuable insights for both academic researchers and policymakers in the formulation and implementation of effective public development strategies. This paper consolidates current knowledge and paves the way for innovative computational approaches that can revolutionize economic forecasting by integrating multidimensional data from diverse external sources.