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.
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- Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
- Heckman, James, 2013.
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- 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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