Rationalizable Counterfactual Choice Probabilities in Dynamic Binary Choice Processes
We address two issues in nonparametric structural analyses of dynamic binary choice processes (DBCP). First, the DBCP is not testable and decision makers’ single-period payoffs (SPP) cannot be identified even when the distribution of unobservable states (USV) is known. Numerical examples show setting SPP from one choice to arbitrary utility levels to identify that from the other can lead to errors in predicting choice probabilities under counterfactual state transitions. We propose two solutions. First, if a data generating process (DGP) has exogenous variations in observable state transitions, the DBCP becomes testable and SPP is identified. Second, exogenous economic restrictions on SPP (such as ranking of states by SPP, or shape restrictions) can be used to recover the identified set of rationalizable counterfactual choice probabilities (RCCP) that are consistent with model restrictions. The other (more challenging) motivating issue is that when the USV distribution is not known, misspecification of the distribution in structural estimation leads to errors in counterfactual predictions. We introduce a simple algorithm based on linear programming to recover sharp bounds on RCCP. This approach exploits the fact that some stochastic restrictions on USV (such as independence from observable states) and economic restrictions on SPP can be represented (without loss of information for counterfactual analyses) as linear restrictions on SPP and distributional parameters of USV. We use numerical examples to illustrate the algorithm and show sizes of identified sets of RCCP can be quite small relative to the outcome space.
|Date of creation:||20 Jun 2009|
|Date of revision:|
|Contact details of provider:|| Postal: 3718 Locust Walk, Philadelphia, PA 19104|
Web page: http://economics.sas.upenn.edu/pier
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Arcidiacono, Peter & Jones, John B., 2000.
"Finite Mixture Distribution, Sequential Likelihood, and the EM Algorithm,"
00-16, Duke University, Department of Economics.
- Peter Arcidiacono & John Bailey Jones, 2003. "Finite Mixture Distributions, Sequential Likelihood and the EM Algorithm," Econometrica, Econometric Society, vol. 71(3), pages 933-946, 05.
- Pedro Mira & Victor Aguirregabiria, 2007.
"Dynamic Discrete Choice Structural Models: A Survey,"
- Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
- Victor Aguirregabiria & Pedro mira, 2007. "Dynamic Discrete Choice Structural Models: A Survey," Working Papers tecipa-297, University of Toronto, Department of Economics.
- Heckman, James J. & Navarro, Salvador, 2005.
"Dynamic Discrete Choice and Dynamic Treatment Effects,"
IZA Discussion Papers
1790, Institute for the Study of Labor (IZA).
- Heckman, James J. & Navarro, Salvador, 2007. "Dynamic discrete choice and dynamic treatment effects," Journal of Econometrics, Elsevier, vol. 136(2), pages 341-396, February.
- James J. Heckman & Salvador Navarro, 2005. "Dynamic Discrete Choice and Dynamic Treatment Effects," NBER Technical Working Papers 0316, National Bureau of Economic Research, Inc.
- Martin Pesendorfer & Philipp Schmidt-Dengler, 2008. "Asymptotic Least Squares Estimators for Dynamic Games -super-1," Review of Economic Studies, Oxford University Press, vol. 75(3), pages 901-928.
- Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
- Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
- Ariel Pakes & Michael Ostrovsky & Steven Berry, 2007.
"Simple estimators for the parameters of discrete dynamic games (with entry/exit examples),"
RAND Journal of Economics,
RAND Corporation, vol. 38(2), pages 373-399, 06.
- Ariel Pakes & Michael Ostrovsky & Steve Berry, 2004. "Simple Estimators for the Parameters of Discrete Dynamic Games (with Entry/Exit Samples)," NBER Working Papers 10506, National Bureau of Economic Research, Inc.
- Ariel Pakes & Michael Ostrovsky & Steve Berry, 2004. "Simple Estimators for the Parameters of Discrete Dynamic Games (with Entry/Exit Examples)," Harvard Institute of Economic Research Working Papers 2036, Harvard - Institute of Economic Research.
- Victor Aguirregabiria, 2005. "Another Look at the Identification of Dynamic Discrete Decision Processes," Econometrics 0504006, EconWPA.
- Keane, Michael P & Wolpin, Kenneth I, 1997.
"The Career Decisions of Young Men,"
Journal of Political Economy,
University of Chicago Press, vol. 105(3), pages 473-522, June.
- Aguirregabiria, Victor, 2005.
"Nonparametric identification of behavioral responses to counterfactual policy interventions in dynamic discrete decision processes,"
Elsevier, vol. 87(3), pages 393-398, June.
- Victor Aguirregabiria, 2004. "Nonparametric Identification of Behavioral Responses to Counterfactual Policy Interventions in Dynamic Discrete Decision Processes," Econometrics 0408004, EconWPA.
- Zvi Eckstein & Kenneth I. Wolpin, 1989. "The Specification and Estimation of Dynamic Stochastic Discrete Choice Models: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 24(4), pages 562-598.
When requesting a correction, please mention this item's handle: RePEc:pen:papers:09-022. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dolly Guarini)
If references are entirely missing, you can add them using this form.