Time and causality: A Monte Carlo assessment of the timing-of-events approach
AbstractWe 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 InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 141 (2007)
Issue (Month): 2 (December)
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Web page: http://www.elsevier.com/locate/jeconom
Other versions of this item:
- Gaure, Simen & Røed, Knut & Zhang, Tao, 2005. "Time and Causality: A Monte Carlo Assessment of the Timing-of-Events Approach," Memorandum 19/2005, Oslo University, Department of Economics.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
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