Robust Misspecification Tests for the Heckman's Two-Step Estimator
基于双变量Edgeworth级数展开，构建了针对Heckman两步估计量一致性的拉格朗日乘子和Neyman C(α)检验，这些检验对局部误设具有稳健性，蒙特卡洛结果和女性劳动供给数据应用表明其有效性。
This article constructs and evaluates Lagrange multiplier (LM) and Neyman's C(α) tests based on bivariate Edgeworth series expansions for the consistency of the Heckman's two-step estimator in sample selection models, that is, for marginal normality and linearity of the conditional expectation of the error terms. The proposed tests are robust to local misspecification in nuisance distributional parameters. Monte Carlo results show that testing marginal normality and linearity of the conditional expectations separately have a better size performance than testing bivariate normality. Moreover, the robust variants of the tests have better empirical size than nonrobust tests, which determines that these tests can be successfully applied to detect specific departures from the null model of bivariate normality. Finally, the tests are applied to women's labor supply data.