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

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

DI PERSIO, Luca
2015-01-01

Abstract

This work is the first part of a project dealing with an in-depth study of effectivetechniques used in econometrics in order to make accurate forecasts in the concrete frameworkof one of the major economies of the most productive Italian area, namely the province ofVerona. In particular, we develop an approach mainly based on vector autoregressions, wherelagged values of two or more variables are considered, Granger causality, and the stochastictrend approach useful to work with the cointegration phenomenon. Latter techniques constitutethe core of the present paper, whereas in the second part of the project, we present how theseapproaches can be applied to economic data at our disposal in order to obtain concrete analysisof import–export behavior for the considered productive area of Verona.
2015
Econometrics time series, autoregressive models, Granger causality,cointegration, stochastic nonstationarity, AIC and BIC criteria, trends and breaks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/924470
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