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Autoregressive approaches to import–export time series I: basic techniques
Volume 2, Issue 1 (2015), pp. 51–65
Luca Di Persio  

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https://doi.org/10.15559/15-VMSTA22
Pub. online: 20 April 2015      Type: Research Article      Open accessOpen Access

Received
9 February 2015
Revised
8 April 2015
Accepted
8 April 2015
Published
20 April 2015

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.

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Bee Dagum, E.: Analisi delle Serie Storiche, Modellistica, Previsione e Scomposizione. Springer, Milano (2002)
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Bernstein, S., Bernstein, R.: Statistica Inferenziale. McGraw-Hill, Milano (2003)
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Brandt, P.T., Williams, J.T.: Multiple Time Series Models. Sage Publications, Thousand Oaks, CA (2007)
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Harris, R., Sollis, R.: Applied Time Series Modelling and Forecasting. John Wiley & Sons Ltd, West Sussex, England (2003)
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Kirchgässner, G., Wolters, J.: Introduction to Modern Time Series Analysis. Springer, Berlin, Heidelberg (2007). MR2451567
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Keywords
Econometrics time series autoregressive models Granger causality cointegration stochastic nonstationarity AIC and BIC criteria trends and breaks

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