The Heckman two-step estimator (Heckit) for the selectivity model is widely applied in Economics and other social sciences. In this model a non-zero outcome variable is observed only if a latent variable is positive. The asymptotic covariance matrix for a two-step estimation procedure must account for the estimation error introduced in the first stage. We examine the finite sample size of tests based on alternative covariance matrix estimators. We do so by using Monte Carlo experiments to evaluate bootstrap generated critical values and critical values based on asymptotic theory.
Hill, R.C., Adkins, L.C. and Bender, K.A. (2003), "TEST STATISTICS AND CRITICAL VALUES IN SELECTIVITY MODELS", Fomby, T.B. and Carter Hill, R. (Ed.) Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later (Advances in Econometrics, Vol. 17), Emerald Group Publishing Limited, Bingley, pp. 75-105. https://doi.org/10.1016/S0731-9053(03)17004-1
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