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Clustering Regression Functions in a Panel

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  • Farshid Vahid

    (Monash University)

Abstract

When time series data of a reasonable length for several cross sectional units are available (for example in the analysis of CO2 emission in industrial countries, or the estimation of production functions for 20 manufacturing sectors), researchers begin by testing whether the data can be pooled and a single dynamic model can be built for all cross sectional units. The "pooling restriction" is often rejected, and then researchers usually proceed by estimating separate dynamic regressions for each cross sectional unit. However, it has been noted in many of such situations that using the pooled model, or shrinking the individual models towards the pooled model, produces superior forecasts relative to individual models. We note that rejecting the grand pooling restriction does not necessarily imply that all cross sectional units must be different. This paper suggests a hierarchical clustering algorithm with a global objective function, to partially pool regressions when the overall pooling restriction is rejected by the data. In addition to the lack of fit and lack of parsimony, the objective function also penalizes lack of conformity with theoretical priors and imprecision in the estimated parameters. This algorithm is used for clustering the gasoline demand functions of OECD countries. The results are compared with those of an alternative method based on a classification and regression tree (CART) procedure. Keywords: Medium sized panels, cluster analysis, information criteria, minimum message length, classification and regression tree (CART).

Suggested Citation

  • Farshid Vahid, 2000. "Clustering Regression Functions in a Panel," Econometric Society World Congress 2000 Contributed Papers 0251, Econometric Society.
  • Handle: RePEc:ecm:wc2000:0251
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    References listed on IDEAS

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    3. Duck, Nigel W, 1993. "Some International Evidence on the Quantity Theory of Money," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 25(1), pages 1-12, February.
    4. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    5. Baltagi, Badi H. & Griffin, James M., 1983. "Gasoline demand in the OECD : An application of pooling and testing procedures," European Economic Review, Elsevier, vol. 22(2), pages 117-137, July.
    6. Durlauf, Steven N & Johnson, Paul A, 1995. "Multiple Regimes and Cross-Country Growth Behaviour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(4), pages 365-384, Oct.-Dec..
    7. Bearse, Peter M & Bozdogan, Hamparsum & Schlottmann, Alan M, 1997. "Empirical Econometric Modelling of Food Consumption Using a New Informational Complexity Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(5), pages 563-586, Sept.-Oct.
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    9. Boozer, Michael A., 1997. "Econometric Analysis of Panel Data Badi H. Baltagi Wiley, 1995," Econometric Theory, Cambridge University Press, vol. 13(05), pages 747-754, October.
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    11. Burnside, Craig, 1996. "Production function regressions, returns to scale, and externalities," Journal of Monetary Economics, Elsevier, vol. 37(2-3), pages 177-201, April.
    12. Anderson, Heather M. & Vahid, Farshid, 1998. "Testing multiple equation systems for common nonlinear components," Journal of Econometrics, Elsevier, vol. 84(1), pages 1-36, May.
    13. Baltagi, Badi H. & Griffin, James M., 1997. "Pooled estimators vs. their heterogeneous counterparts in the context of dynamic demand for gasoline," Journal of Econometrics, Elsevier, vol. 77(2), pages 303-327, April.
    14. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
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    Cited by:

    1. Enrica De Cian & Elisa Lanzi & Roberto Roson, 2013. "Seasonal temperature variations and energy demand," Climatic Change, Springer, vol. 116(3), pages 805-825, February.

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