TY - JOUR
AU - Bloom,David E.
AU - Killingsworth,Mark R.
TI - Correcting for Truncation Bias Caused by a Latent Truncation Variable
JF - National Bureau of Economic Research Technical Working Paper Series
VL - No. 38
PY - 1984
Y2 - June 1984
DO - 10.3386/t0038
UR - http://www.nber.org/papers/t0038
L1 - http://www.nber.org/papers/t0038.pdf
N1 - Author contact info:
David E. Bloom
Harvard T. H. Chan
School of Public Health
Department of Global Health and Population
665 Huntington Ave.
Building 1, Suite 1202
Boston, MA 02115
Tel: 617/432-0866
Fax: 617/432-6733
E-Mail: dbloom@hsph.harvard.edu
Mark R. Killingsworth
Department of Economics
Rutgers University
New Brunswick, NJ 08901
Tel: 732/932-7794
Fax: 732/932-7416
E-Mail: mrk@rci.rutgers.edu
AB - 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.
ER -