Correcting for Truncation Bias Caused by a Latent Truncation Variable

10 Pages Posted: 29 Dec 2006 Last revised: 9 Feb 2023

See all articles by David E. Bloom

David E. Bloom

Harvard University - T.H. Chan School of Public Health; National Bureau of Economic Research (NBER)

Mark R. Killingsworth

Rutgers, The State University of New Jersey - Department of Economics

Date Written: June 1984

Abstract

We discuss estimation of the model Y[sub i] = X[sub i]b[sub y] + e[sub Yi] and T[sub i] =X[sub i]b[sub T] + e[sub Ti] when data on the continuous dependent variable Y and on the independent variables X are observed if the "truncation variable" T > 0 and when T is latent. This case is distinct from both (i) the "censored sample" case, in which Y data are available if T > 0, T is latent and X data are available for all observations, and (ii) the "observed truncation variable" case, in which both Y and X are observed if T > 0 and in which the actual value of T is observed whenever T > O. We derive a maximum-likelihood procedure for estimating this model and discuss identification and estimation.

Suggested Citation

Bloom, David E. and Killingsworth, Mark R., Correcting for Truncation Bias Caused by a Latent Truncation Variable (June 1984). NBER Working Paper No. t0038, Available at SSRN: https://ssrn.com/abstract=579737

David E. Bloom (Contact Author)

Harvard University - T.H. Chan School of Public Health ( email )

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Mark R. Killingsworth

Rutgers, The State University of New Jersey - Department of Economics ( email )

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