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

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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 neighboring 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 one year and five 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 neterogeneity across the states perform the best for one year ahead forecasts. However, for two to five years ahead forecasts, estimators that take into account the heterogeneity across the states yield the best forecasts.

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Bibliographic Info

Paper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 84.

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Length: 20 pages
Date of creation: Jul 2006
Date of revision:
Handle: RePEc:max:cprwps:84

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Keywords: prediction; spatial correlation; panel data; liquor demand;

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  1. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 239-53, January.
  2. Baltagi, Badi H & Griffin, James M, 1995. "A Dynamic Demand Model for Liquor: The Case for Pooling," The Review of Economics and Statistics, MIT Press, vol. 77(3), pages 545-54, August.
  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  4. J. A. Hausman, 1976. "Specification Tests in Econometrics," Working papers 185, Massachusetts Institute of Technology (MIT), Department of Economics.
  5. Baillie, R.T. & Baltagi, B.H., 1994. "Prediction from the Regression Model with one-way Error Components," Papers 9405, Michigan State - Econometrics and Economic Theory.
  6. Wansbeek, Tom & Kapteyn, Arie, 1983. "A note on spectral decomposition and maximum likelihood estimation in models with balanced data," Statistics & Probability Letters, Elsevier, vol. 1(4), pages 213-215, June.
  7. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
  8. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
  9. Wansbeek, T.J. & Kapteyn, A.J., 1983. "A note on spectral decomposition and maximum likelihood estimation in ANOVA models with balanced data," Open Access publications from Tilburg University urn:nbn:nl:ui:12-364319, Tilburg University.
  10. Amemiya, Takeshi, 1971. "The Estimation of the Variances in a Variance-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 12(1), pages 1-13, February.
  11. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
  12. Taub, Allan J., 1979. "Prediction in the context of the variance-components model," Journal of Econometrics, Elsevier, vol. 10(1), pages 103-107, April.
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