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Time and causality: A Monte Carlo assessment of the timing-of-events approach

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  • Gaure, Simen
  • Roed, Knut
  • Zhang, Tao

Abstract

We present new Monte Carlo evidence regarding the feasibility of separating causality from selection within non-experimental interval-censored duration data, by means of the nonparametric maximum likelihood estimator (NPMLE). Key findings are: i) the NPMLE is extremely reliable, and it accurately separates the causal effects of treatment and duration dependence from sorting effects, almost regardless of the true unobserved heterogeneity distribution; ii) the NPMLE is normally distributed, and standard errors can be computed directly from the optimally selected model; and iii)unjustified restrictions on the heterogeneity distribution, e.g., in terms of a prespecified number of support points, may cause substantial bias.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 141 (2007)
Issue (Month): 2 (December)
Pages: 1159-1195

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Handle: RePEc:eee:econom:v:141:y:2007:i:2:p:1159-1195

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