Institutional Affiliation: University of California at Los Angeles
|Nonparametric Identification and Estimation of Nonadditive Hedonic Models|
with , : w15226
This paper studies the identification and estimation of preferences and technologies in equilibrium hedonic models. In it, we identify nonparametric structural relationships with nonadditive heterogeneity. We determine what features of hedonic models can be identified from equilibrium observations in a single market under weak assumptions about the available information. We then consider use of additional information about structural functions and heterogeneity distributions. Separability conditions facilitate identification of consumer marginal utility and firm marginal product functions. We also consider how identification is facilitated using multimarket data.
Published: James J. Heckman & Rosa L. Matzkin & Lars Nesheim, 2010. "Nonparametric Identification and Estimation of Nonadditive Hedonic Models," Econometrica, Econometric Society, vol. 78(5), pages 1569-1591, 09. citation courtesy of
|Simulation and Estimation of Nonaddative Hedonic Models|
with , : w9895
Making use of restrictions imposed by equilibrium, theoretical progress has been made on the nonparametric and semiparametric estimation and identification of scalar additive hedonic models (Ekeland, Heckman, and Nesheim, 2002) and scalar nonadditive hedonic models (Heckman, Matzkin, and Nesheim, 2002). However, little is known about the practical aspects of estimating such models or of the characteristics of equilibrium in such models. This paper presents computational and analytical results that fill some of these gaps. We simulate and estimate examples of equilibrium in the additive hedonic models and provide evidence on the performance of several estimation techniques. We also simulate examples of equilibria in nonadditive models and provide evidence on the performance of the nonadditi...
Published: Kehoe, T., T.N. Srinivasan, J. Whalley (eds.) Frontiers in Applied General Equilibrium Modeling. Cambridge University Press, 2004.
|Panel Data Estimators for Nonseparable Models with Endogenous Regressors|
with : t0267
We propose two new estimators for a wide class of panel data models with nonseparable error terms and endogenous explanatory variables. The first estimator covers qualitative choice models and both estimators cover models with continuous dependent variables. The first estimator requires the existence of a vector z such that the density of the error term does not depend on the explanatory variables once one conditions on z. In some panel data cases we may find z by making the assumption that the distribution of the error term conditional on the vector of the explanatory variables for each cross-section' unit in the panel is exchangeable in the values of those explanatory variables. This situation may be realistic, in particular, when each unit is a group of individuals, so that the observa...
Published: Altonji, Joseph G. and Rosa L. Matzkin. "Cross Section And Panel Data Estimators For Nonseparable Models With Endogenous Regressors," Econometrica, v73(4,Jul), 2005, 1053-1101.