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Another Look at the Identification of Dynamic Discrete Decision Processes

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Author Info

  • Victor Aguirregabiria

    (Boston University)

Abstract

This paper presents an econometric approach to estimate the behavioral effects of counterfactual policy experiments in the context of dynamic decision models where the current utility function and the distribution of unobservables are nonparametrically specified. Previous studies have shown that the identification of the current utility function in dynamic decision models requires of stronger assumptions than in static decision models. We show in this paper that knowledge of the current utility function (or of a 'normalized' utility function) is not necessary to identify counterfactual choice probabilities in dynamic models. To identify these counterfactuals we need the probability distribution of the unobservables and the difference between the present value of choosing always the same alternative and the present value of deviating one period from this strategy. We show that both functions are identified from the factual choice probabilities under similar conditions as in static decision models. Based on this result we propose a nonparametric procedure to estimate the behavioral effects of counterfactual experiments in dynamic decision models. We apply this method to evaluate the effects of an investment subsidy program in the context of a model of machine replacement.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0504006.

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Length: 41 pages
Date of creation: 19 Apr 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0504006

Note: Type of Document - pdf; pages: 41
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Web page: http://128.118.178.162

Related research

Keywords: Dynamic discrete decision processes; Nonparametric Identification; Counterfactual experiments.;

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References

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Citations

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Cited by:
  1. Xun Tang, 2009. "Rationalizable Counterfactual Choice Probabilities in Dynamic Binary Choice Processes," PIER Working Paper Archive 09-022, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  2. Pedro Mira & Victor Aguirregabiria, 2007. "Dynamic Discrete Choice Structural Models: A Survey," Working Papers wp2007_0711, CEMFI.
  3. Patrick Bajari & Han Hong, 2006. "Semiparametric Estimation of a Dynamic Game of Incomplete Information," NBER Technical Working Papers 0320, National Bureau of Economic Research, Inc.
  4. Le-Yu Chen, 2009. "Identification of structural dynamic discrete choice models," CeMMAP working papers CWP08/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  5. Hiroyuki Kasahara & Katsumi Shimotsu, 2006. "Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices," Working Papers 1092, Queen's University, Department of Economics.

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