@techreport{NBERt0038,
title = "Correcting for Truncation Bias Caused by a Latent Truncation Variable",
author = "David E. Bloom and Mark R. Killingsworth",
institution = "National Bureau of Economic Research",
type = "Working Paper",
series = "Technical Working Paper Series",
number = "38",
year = "1984",
month = "June",
doi = {10.3386/t0038},
URL = "http://www.nber.org/papers/t0038",
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.},
}