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Correcting for truncation bias caused by a latent truncation variable

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  • Bloom, David E.
  • Killingsworth, Mark R.

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.
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  • Bloom, David E. & Killingsworth, Mark R., 1985. "Correcting for truncation bias caused by a latent truncation variable," Journal of Econometrics, Elsevier, vol. 27(1), pages 131-135, January.
    • Handle: RePEc:eee:econom:v:27:y:1985:i:1:p:131-135
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    1. Jerry A. Hausman & David A. Wise, 1976. "The Evaluation of Results from Truncated Samples: The New Jersey Income Maintenance Experiment," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 421-445, National Bureau of Economic Research, Inc.
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