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

Publisert i

Journal of Econometrics 141(2), 2007, pages 1159-1195


We present new Monte Carlo evidence regarding the feasibility of separating causality from selection within non-experimental duration data, by means of the non-parametric 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 pre-specified number of support points, may cause substantial bias.


By Simen Gaure, Knut Røed and Tao Zhang
Published Aug. 5, 2011 3:26 PM - Last modified Aug. 5, 2011 3:28 PM