Autoregressive approaches to import--export time series II: a concrete case study

TL;DR

This paper presents an econometric case study on forecasting import-export time series in Verona, Italy, using vector autoregression, Granger causality, and cointegration techniques to improve accuracy with real economic data.

Contribution

It applies and evaluates advanced econometric methods specifically tailored for regional import-export forecasting in a major Italian economy.

Findings

Effective use of VAR models for regional trade data

Identification of Granger causality relationships

Successful handling of cointegration in economic forecasting

Abstract

The present work constitutes the second part of a two-paper project that, in particular, deals 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. It is worth mentioning that this region is indubitably recognized as the core of the commercial engine of the whole Italian country. This is why our analysis has a concrete impact; it is based on real data, and this is also the reason why particular attention has been taken in treating the relevant economical data and in choosing the right methods to manage them to obtain good forecasts. In particular, we develop an approach mainly based on vector autoregression where lagged values of two or more variables are considered, Granger causality, and the stochastic trend approach useful to work with the cointegration phenomenon.