Testing for Selectivity Bias in Panel Data Models

Datos Bibliográficos

ID23325852
AutoresMarno Verbeek (0000-0002-4079-6774), Theo Nijman
Año1992
Volumen33
Número3
Páginas681
Fecha de publicación1992-08-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaInternational Economic Review (JOURNAL)
Identificadores de la revistaISSN: 0020-6598 • E-ISSN: 1468-2354
EditorialJSTOR (PUBLISHER)
DOI10.2307/2527133
OpenAlexW2586769758
IdiomaEN
Citas recibidas86
Referencias citadas1

Missing observations are a rule rather than an exception in panel data.In this paper we discuss several tests to check for the presence of selectivity bias in regression estimates based on panel data.One approach to test for selectivity bias i n these estimates is to specify the missing data mechanism explicitly and to estimate the response mechanism and the regres-equation jointly.Alternatively, one can derive the asymptotically efficient Lagrange Multiplier test once an assumption on the response mechanism has been made.Both approaches are computationally demanding as e.g.multivariate probit models have to be estimated.We propose the use of simple variable addition and (quasi) Hausman tests to test for selectivity bias and compare the power of these tests with the asymptotically efficient tests using Monte Carlo methods.Keynesian and New Classical Models of Unemployment Revisited

Econometrics · Lagrange multiplier · Mathematical optimization · Monte Carlo method · Panel data · Probit · Statistics · Economic Policies and Impacts · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis