Prévision pour le panel datamodel avec corrélation spatiale: Le cas de l'alcool

Translated title of the contribution: Prediction in the panel data model with spatial correlation: The case of liquor

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965- 1994. The spatial autocorrelation due to neighbouring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states' demand for liquor for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random-effects GLS estimator ignoring spatial correlation and random-effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, estimators that take into account spatial correlation and heterogeneity across the states perform the best for forecasts 1 year ahead. However, for forecasts 2- 5 years ahead, estimators that take into account the heterogeneity across the states yield the best forecasts.

Translated title of the contributionPrediction in the panel data model with spatial correlation: The case of liquor
Original languageFrench
Pages (from-to)175-185
Number of pages11
JournalSpatial Economic Analysis
Volume1
Issue number2
DOIs
StatePublished - 2006

Keywords

  • Liquor demand
  • Panel data
  • Prediction
  • Spatial correlation

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Economics, Econometrics and Finance(all)
  • Statistics, Probability and Uncertainty
  • Earth and Planetary Sciences (miscellaneous)

Fingerprint Dive into the research topics of 'Prediction in the panel data model with spatial correlation: The case of liquor'. Together they form a unique fingerprint.

  • Cite this