Sequential Matching Estimation of Dynamic Causal Models
By M. Lechner. Social Science Research Network, 2004
69 citations recorded by the source corpus.
This paper proposes sequential matching and inverse selection probability weighting to estimate dynamic causal effects. The sequential matching estimators extend simple, matching estimators based on propensity scores for static causal analysis that have been frequently applied in the evaluation literature. A Monte Carlo study shows that the suggested estimators perform well in small and medium size samples. Based on the application of the sequential matching estimators to an empirical problem - an evaluation study of the Swiss active labour market policies - some implementational issues are discussed and results are provided.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/69233810cafcbca02e75f4dbeff7cedabc5559ba
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Marco Caliendo
- M. Lechner
- Conny Wunsch
- James J. Heckman
- E. Vytlacil
- Reinhard Hujer
- Bernd Fitzenberger
- S. Speckesser
In Statistics and Probability
- Marco Caliendo
- M. Lechner
- Conny Wunsch
- James J. Heckman
- E. Vytlacil
- Bernd Fitzenberger
- Reinhard Hujer
- S. Speckesser
Citations in the corpus
- Long-Run Effects of Public Sector Sponsored Training in West Germany (2011, methodology): “Thus, such an analysis of the effects of sequences of programmes requires a dynamic evaluation approach as suggested by Miquel and Lechner (2002), or Lechner (2004), IABDiscussionPaper No. 3/2005 21 which is not feasible with our data without further aggregation of programme types, which is…”
- Employment effects of the provision of specific professional skills and techniques in Germany (2007, background): “9We do not analyze multiple sequential treatments, see Bergemann et al. (2004), Lechner and Miquel (2001), and Lechner (2004).”
- The microeconometric estimation of treatment effects—An overview (2006, background): “Lechner and Miquel (2002) discuss identifying assumptions for so-called sequential matching estimators.”
- Get Training or Wait? Long-Run Employment Effects of Training Programs for the Unemployed in West Germany (2006, unknown): “JEL Classification: C14, J68, H43 Keywords: multiple treatments, training programs, employment effects, local linear matching, administrative data, active labor market programs Corresponding author: Bernd Fitzenberger Department of Economics Goethe-University PO Box 11 19 32 (PF 247) 60054 Frankfurt”
- Causal Effects of Monetary Shocks: Semiparametric Conditional Independence Tests with a Multinomial Propensity Score (2011, unknown): “Similarly, Robins, Greenland, and Hu (1999), Lok et al. (2004), and Lechner (2004) have considered panel-type settings with time-varying treatments and sequential randomized trials.”
- 1. The Scientific Model of Causality (2005, background): “Gill and Robins (2001), Abbring and Van Den Berg (2003), Lechner (2004), Heckman and Vytlacil (2006a,b), and Heckman and Navarro (2006) develop models for dynamic counterfactuals.”
- Some Practical Guidance for the Implementation of Propensity Score Matching (2008, background): “To our knowledge Lechner (2004) is the only application so far and hence practical experiences with sequential matching estimators are rather limited.”
- Econometric Evaluation of Social Programs, Part I: Causal Models, Structural Models and Econometric Policy Evaluation (2007, background): “Gill and Robins (2001), Abbring and Van den Berg (2003), Abbring and Heckman (2007, Chapter 72), Lechner (2004) and Heckman and Navarro (2007) develop models for dynamic counterfactuals, where time-subscripted and ω-subscripted S arise as information accrues.”