Working Paper: NBER ID: w21527
Authors: Myrto Kalouptsidi; Paul T. Scott; Eduardo Souza-Rodrigues
Abstract: Dynamic discrete choice (DDC) models are not identified nonparametrically, but the non-identification of models does not necessarily imply the non-identification of counterfactuals. We derive novel results for the identification of counterfactuals in DDC models, such as non- additive changes in payoffs or changes to agents' choice sets. In doing so, we propose a general framework that allows the investigation of the identification of a broad class of counterfactuals (covering virtually any counterfactual encountered in applied work). To illustrate the results, we consider a firm entry/exit problem numerically, as well as an empirical model of agricultural land use. In each case, we provide examples of both identified and non-identified counterfactuals of interest.
Keywords: Dynamic Discrete Choice Models; Counterfactuals; Identification; Agricultural Land Use; Firm Entry-Exit
JEL Codes: C5; Q1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| dynamic discrete choice models are not identified nonparametrically (C35) | multiple payoff functions can explain observed choices (G40) |
| flow payoffs change additively by predefined amounts (G19) | counterfactuals are identified (C52) |
| state transition process changes (J62) | counterfactual behavior is not identified (D91) |
| certain types of counterfactual transformations are identified (C24) | prespecified additive changes are identified (C22) |
| identification of counterfactual behavior is necessary but not sufficient for identifying welfare (D69) | identification of counterfactual welfare differs from identification of counterfactual behavior (D69) |
| identification of counterfactuals can be sensitive to the model's assumptions and the empirical context (C50) | identification of counterfactuals varies (D80) |