Correcting for Truncation Bias Caused by a Latent Truncation Variable
AbstractWe 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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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0038.
Date of creation: Jun 1984
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Publication status: published as Bloom, David E. and Mark R. Killingsworth. "Correcting for Truncation Bias Caused by a Latent Truncation Variable." Journal of Econometrics, Vol. 27 , No. 1, January 1985, pp.131-135.
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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.
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