Autoregressive approaches to import-export time series I: basic techniques

TL;DR

This paper explores econometric techniques like vector autoregressions, Granger causality, and cointegration to forecast import-export dynamics in Verona's economy, laying the groundwork for detailed economic analysis.

Contribution

It introduces a methodological framework combining VAR, Granger causality, and cointegration for import-export forecasting in a regional economic context.

Findings

Developed VAR-based models for Verona's import-export data

Identified causal relationships between economic variables

Demonstrated cointegration among key economic indicators

Abstract

This work is the first part of a project dealing with an in-depth study of effective techniques used in econometrics in order to make accurate forecasts in the concrete framework of one of the major economies of the most productive Italian area, namely the province of Verona. In particular, we develop an approach mainly based on vector autoregressions, where lagged values of two or more variables are considered, Granger causality, and the stochastic trend approach useful to work with the cointegration phenomenon. Latter techniques constitute the core of the present paper, whereas in the second part of the project, we present how these approaches can be applied to economic data at our disposal in order to obtain concrete analysis of import--export behavior for the considered productive area of Verona.