Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor

Badi Baltagi and Dong Li

Spatial Economic Analysis, 2006, vol. 1, issue 2, 175-185

Abstract: 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.

Keywords: Prediction; spatial correlation; panel data; liquor demand; C21; C23; C53 (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (82)

Downloads: (external link)
http://www.taylorandfrancisonline.com/doi/abs/10.1080/17421770601009817 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Prediction in the Panel Data Model with Spatial Correlation: The Case of Liquor (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:specan:v:1:y:2006:i:2:p:175-185

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RSEA20

DOI: 10.1080/17421770601009817

Access Statistics for this article

Spatial Economic Analysis is currently edited by Bernie Fingleton and Danilo Igliori

More articles in Spatial Economic Analysis from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().