Interpreting Estimated Parameters and Measuring Individual Heterogeneity in Random Coefficient Models

19 Pages Posted: 13 Oct 2008

See all articles by William H. Greene

William H. Greene

New York University Stern School of Business

Multiple version iconThere are 2 versions of this paper

Date Written: May 2004

Abstract

Recent studies in econometrics and statistics include many applications of random parameter models. The underlying structural parameters in these models are often not directly informative about the statistical relationship of interest. As a result, standard significance tests of structural parameters in random parameter models do not necessarily indicate the presence or absence of a significant relationship among the model variables. This note offers a suggestion on how to examine the results of estimation of a general form of random parameter model. We also extend results on computing individual level parameters in a random parameters setting and show how simulation based estimates of parameters in conditional distributions can be used to examine the influence of model covariates (marginal effects) at an individual level

Keywords: Panel data, random effects, random parameters, maximum simulated likelihood, conditional mean, conditional variance, marginal effects, confidence interval

Suggested Citation

Greene, William H., Interpreting Estimated Parameters and Measuring Individual Heterogeneity in Random Coefficient Models (May 2004). NYU Working Paper No. 2451/26121, Available at SSRN: https://ssrn.com/abstract=1282551

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