格林 面板数据讲义-22.pptVIP

Econometric Analysis of Panel Data Econometric Analysis of Panel Data 22. Individual Heterogeneity and Random Parameter Variation Heterogeneity Observational: Observable differences across individuals (e.g., choice makers) Choice strategy: How consumers make decisions – the underlying behavior Structural: Differences in model frameworks Preferences: Differences in model ‘parameters’ Parameter Heterogeneity Distinguish Bayes and Classical Both depart from the heterogeneous ‘model,’ f(yit|xit)=g(yit,xit,βi) What do we mean by ‘randomness’ With respect to the information of the analyst (Bayesian) With respect to some stochastic process governing ‘nature’ (Classical) Bayesian: No difference between ‘fixed’ and ‘random’ Classical: Full specification of joint distributions for observed random variables; piecemeal definitions of ‘random’ parameters. Usually a form of ‘random effects’ Hierarchical Bayesian Estimation Allenby and Rossi: Structure Priors Bayesian Posterior Analysis Estimation of posterior distributions for upper level parameters: and Vβ Estimation of posterior distributions for low (individual) level parameters, βi|datai. Detailed examination of ‘individual’ parameters (Comparison of results to counterparts using classical methods) Classical Random Parameters Fixed Management and Technical Efficiency in a Random Coefficients Model Antonio Alvarez, University of Oviedo Carlos Arias, University of Leon William Greene, Stern School of Business, New York University The Production Function Model Application to Spanish Dairy Farms Translog Production Model Random Coefficients Model Discrete vs. Continuous Variation Classical context: Description of how parameters are distributed across individuals Variation Discrete: Finite number of different parameter vectors distributed across individuals Mixture is unknown as well as the parameters: Implies randomness from the point of the analyst. (Bayesian?) Might also be viewed as discrete approximation to a