Xun Tang () (Department of Economics, University of Pennsylvania)
Abstract
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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number
09-022.
Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models
This paper has been announced in the following NEP Reports:
References listed on IDEAS 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.:
Did you know? Each page is provided with a technical contact, in case something is not right with the supplied information. See under "publisher info".