GMM inference in the matrix exponential spatial specification

原文摘要

This paper offers hypothesis and specification tests for the best generalized methods of moment estimator (BGMME) of the matrix exponential spatial specification (MESS) developed by Debarsy et al. (2015). First, as the BGMME is a two-step estimator, we formulate corrected standard errors using a modified version of the finite sample correction method in Windmeijer (2005) that accounts for the fact that the BGMME makes more extensive use of the first-stage estimator than the GMM model analyzed by Windmeijer. Second, since the BGMME uses different moment conditions under normal, non-normal, and heteroskedastic disturbances, we propose a pretest strategy to determine which set of moment conditions is most suitable for the data at hand. Third, we consider and examine the performance of test statistics that help choose between MESS(1,1), MESS(1,0) and MESS(0,1) models. The performance of these tools is examined with Monte Carlo experiments, which also allow for varying degrees of spatial correlation in the explanatory variables. The correction in the standard errors is especially useful when the sample size is small, such as in a study with state-level, provincial or country-level data: the corrected standard errors improve statistical inference, yielding better size properties. The pretest strategy is effective when the heteroskedasticity, if present, is correlated with explanatory variables in the model. Spatial lags in the outcome variable are more easily detected than those in the disturbance. An empirical study of housing prices illustrates the new tools.

摘要来源：RePEc · 出版社摘要