Correcting for Truncation Bias Caused by a Latent Truncation Variable
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.
|Date of creation:||Jun 1984|
|Date of revision:|
|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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