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Correcting for Truncation Bias Caused by a Latent Truncation Variable

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  • David E. Bloom
  • Mark R. Killingsworth

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.

Suggested Citation

  • David E. Bloom & Mark R. Killingsworth, 1984. "Correcting for Truncation Bias Caused by a Latent Truncation Variable," NBER Technical Working Papers 0038, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0038
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    15. Colombo, Massimo G. & Delmastro, Marco & Grilli, Luca, 2004. "Entrepreneurs' human capital and the start-up size of new technology-based firms," International Journal of Industrial Organization, Elsevier, vol. 22(8-9), pages 1183-1211, November.
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