Sarafidis, Vasilis and Weber, Neville (2009): To pool or not to pool: a partially heterogeneous framework.
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This paper proposes a partially heterogeneous framework for the analysis of panel data with fixed T. In particular, the population of cross-sectional units is grouped into clusters, such that slope parameter homogeneity is maintained only within clusters. Our method assumes no a priori information about the number of clusters and cluster membership and relies on the data instead. The unknown number of clusters and the corresponding partition are determined based on the concept of �partitional clustering�, using an information-based criterion. It is shown that this is strongly consistent, i.e. it selects the true number of clusters with probability one as N approaches unity. Simulation experiments show that the proposed criterion performs well even with moderate N and the resulting parameter estimates are close to the true values. We apply the method in a panel data set of commercial banks in the US and we find five clusters, with significant differences in the slope parameters across clusters.
|Item Type:||MPRA Paper|
|Original Title:||To pool or not to pool: a partially heterogeneous framework|
|Keywords:||Partial heterogeneity, partitional clustering, information-based criterion, model selection|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C33 - Models with Panel Data; Longitudinal Data; Spatial Time Series
|Depositing User:||Vasilis Sarafidis|
|Date Deposited:||25. Jan 2012 02:39|
|Last Modified:||12. Feb 2013 10:26|
Ahn, S. C. and A. Horenstein (2008) "Eigenvalue ratio test for the number of factors", Mimeo.
Bai, Z., Rao, C.R. and Y. Wu (1999) "Model Selection with Data-Oriented Penalty," Journal of Statistical Planning and Inference 77, 103-117.
Baltagi, Badi H., and James M. Griffin, "Pooled Estimators vs. their Heterogeneous Counterparts in the Context of Dynamic Demand for Gasoline," Journal of Econometrics 77 (1977), 303-327.
Baltagi, Badi H., James M. Griffin, and Weiwen Xiong, "To Pool or not to Pool: Homogeneous Versus Heterogeneous Estimators Applied to Cigarette Demand," Review of Economics and Statistics 82:1 (2000), 117-126.
Beasley, J. E, Chu P. C. (1996). A Genetic algorithm for the set covering problem. European Journal of Operational Research 94, 392-404.
Berger, Allen N., and David B. Humphrey, "Efficiency of Financial Institutions: International Survey and Directions for Future Research," European Journal of Operational Research 98 (1997), 175-212.
Browning. M., Carro, J. (2007). Heterogeneity and microeconometric modelling. In: Blundell, R., Newey, W., Persson, T. ed. Advances in Economics and Econometrics 3. Cambridge: Cambridge University Press.
Burnside, Craig, "Production Function Regressions, Returns to Scale, and Externalities," Journal of Monetary Economics 37 (1996), 177-201.
Durlauf, Steven, and Paul Johnson, "Multiple Regimes and Cross-country Growth Behaviour," Journal of Applied Econometrics 10 (1995), 365ñ384.
Everitt, Brian, Cluster analysis, 3rd ed. (London: Eward Arnold, 2003).
Fernandez-Val, I. (2005). Bias correction in panel data models with individual specific parameters. Mimeo.
Fisher, W. D. (1958). On grouping for maximum homogeneity. Journal of the American Statistical Association 53, 789-798.
Fries, S., Taci, A. (2005). Cost efficiency of banks in transition: evidence from 289 banks in 15 post-communist countries. Journal of Banking and Finance 29:55-81.
Garfinkel, R. S., Nemhauser, G. L. (1969). The set-partitioning problem: set covering with equality constraints. Operations Research 17:848-856.
Graham, B. S., Powel, J. L. (2008). Identification and estimation of �irregular�correlated random coefficient models. Mimeo.
Hancock, Diana, "A Model of Financial Firm with Imperfect Asset and De- posit Elasticities," Journal of Banking and Finance 10 (1986), 37-54.  Hsiao, Cheng, Analysis of Panel Data, 2nd ed. (Cambridge: Cambridge University Press, 2003).
Kapetanios, George, "Cluster Analysis of Panel Datasets Using Non-Standard Optimisation of Information Criteria," Journal of Economic Dynamics and Control 30:8 (2006), 1389-1408.
Kaparakis, Emmanuel I., Stephen M. Miller, and Athanasios G. Noulas "Short-Run Cost Ine¢ ciency of Commercial Banks: A Flexible Frontier Approach," Journal of Money, Credit and Banking 26:4 (1994), 875-893.
Kaufman, Leonard, and Peter J. Rousseeuw, Finding groups in data: An introduction to cluster analysis, (NY: John Wiley & Sons 1990).
Kumbhakar, Subal C., and Efthymios G. Tsionas, "Scale and efficiency measurement using a semiparametric stochastic frontier model: evidence from the U.S. commercial banks," Empirical Economics 34 (2008), 585-602.
Kwan, S. H. (2006). The X-efficiency of commercial banks in Hong Kong. Journal of Banking and Finance 30:1127-1147.
McAllister, Patrick H., and Douglas A. McManus, ìResolving the Scale Efficiency Puzzle in Banking," Journal of Banking and Finance 17 (1993), 389-405.
Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. University of Cambridge, Faculty of Economics, Cambridge Working Papers in Economics No. 0435.
Pesaran, Hashem M., "Estimation And Inference In Large Heterogeneous Panels With A Multifactor Error Structure," Econometrica 74:4 (2006), 967-1012.
Pesaran, Hashem M., Yongcheol Shin, and Ron J. Smith,"Pooled Mean Group Estimation of Dynamic Heterogeneous Panels," Journal of the American Statistical Association 94 (1999), 621-634.
Pesaran, M. H., Ullah, A., Yamagata, T. (2008). A Bias-adjusted test of error cross section dependence. The Econometrics Journal 11:105-127.
Rao, M. R. (1971). Cluster Analysis and Mathematical Programming. Journal of the American Statistical Association 66:622-626.
Rota, Gian-Carlo, "The Number of Partitions of a Set," American Mathematical Monthly 71:5 (1964), 498-504.
Sealey, C.W., Lindley, J. T. (1977). Inputs, outputs, and theory of production cost at depository financial institutions. Journal of Finance 32:1251-1266.
Selim, S. Z., Ismail, M. A. (1984). K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. IEEE Transactions on Pattern Analysis and Machine Intelligence 6:81-87.
Shao, Qing, and Yuehua Wu, "A Consistent Procedure for Determining the Number of Clusters in Regression Clustering," Journal of Statistical Planning and Inference 135 (2005), 461-476.
Vahid, Farshid, "Partial Pooling: A Possible Answer to Pool or Not to Pool," in Cointegration, Causality and Forecasting: Festschrift in Honor of Clive W. J. Granger, ed. by R. Engle and H. White, 1999.
Wan, S. J., Wong, S. K. M., Prusinkiewicz, P. (1988). An algorithm for multidimensional data clustering. ACM Transactions on Mathematical Software 14:153-162.
Yitzhaki, S. (1996). On using linear regressions in welfare economics. Journal of Business and Economic Statistics 14:478-486.
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To Pool or Not to Pool: A Partially Heterogeneous Framework. (deposited 20. Feb 2010 16:57)
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