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Time and Causality: A Monte Carlo Assessment of the Timing-of-Events Approach
By Gaure, Simen, Knut Røed and Tao Zhang
2007
Journal of Econometrics, Vol 141, 1159-1195
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Abstract |
We present new Monte Carlo evidence regarding the feasibility of separating causal-ity from selection within non-experimental duration data, by means of the nonpara-metric maximum likelihood estimator (NPMLE). Key findings are: i) the NPMLE is extremely reliable, and it accurately separates the causal effects of treatment and dura-tion dependence from sorting effects, almost regardless of the true unobserved hetero-geneity distribution; ii) the NPMLE is normally distributed, and standard errors can be computed directly from the optimally selected model; and iii) unjustified restric-tions on the heterogeneity distribution, e.g., in terms of a pre-specified number of support points, may cause substantial bias. |
| JEL Classification:
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C14, C15, C41, |
| Keywords:
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NPMLE, treatment effect |
| Project:
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1151 Mobilizing labour force participation
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| Financing:
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Norges Forskningsråd |
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