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Time and Causality: A Monte Carlo Assessment of the Timing-of-Events Approach

Sammendrag

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.

Om publikasjonen

Forfattere:

Gaure, Simen, Knut Røed and Tao Zhang

År:

2007

Tidsskrift:

Journal of Econometrics

Serie:

Vitenskapelige tidsskrift
Nummer i serie: 141

JEL:

C14, C15, C41,

Nøkkelord:

NPMLE, treatment effect

Prosjekt:

1151 - Mobilisering av arbeidstilbudet

Finansiering:

Norges Forskningsråd

Lenke:

[DOI]