Test statistics and critical values in selectivity models

R C Hill, L C Adkins, K A Bender

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationMaximum Likelihood Estimation of Misspecified Models
Subtitle of host publicationTwenty years later
EditorsTB Fomby, RC Hill
Place of PublicationAmsterdam
PublisherJAI-ELSEVIER SCI BV
Pages75-105
Number of pages31
Volume17
ISBN (Print)0-7623-1075-8
DOIs
Publication statusPublished - 2003

Keywords

  • sample-selection
  • specification error
  • bias
  • estimators
  • time

Cite this

Hill, R. C., Adkins, L. C., & Bender, K. A. (2003). Test statistics and critical values in selectivity models. In TB. Fomby, & RC. Hill (Eds.), Maximum Likelihood Estimation of Misspecified Models: Twenty years later (Vol. 17, pp. 75-105). JAI-ELSEVIER SCI BV. https://doi.org/10.1016/S0731-9053(03)17004-1