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Dynamic discrete choice models are widely employed to answer substantive andpolicy questions in settings where individuals' current choices have futureimplications. However, estimation of these models is often computationallyintensive and/or infeasible in high-dimensional settings. Indeed, evenspecifying the structure for how the utilities/state transitions enter theagent's decision is challenging in high-dimensional settings when we have noguiding theory. In this paper, we present a semi-parametric formulation ofdynamic discrete choice models that incorporates a high-dimensional set ofstate variables, in addition to the standard variables used in a parametricutility function. The high-dimensional variable can include all the variablesthat are not the main variables of interest but may potentially affect people'schoices and must be included in the estimation procedure, i.e., controlvariables. We present a data-driven recursive partitioning algorithm thatreduces the dimensionality of the high-dimensional state space by taking thevariation in choices and state transition into account. Researchers can thenuse the method of their choice to estimate the problem using the discretizedstate space from the first stage. Our approach can reduce the estimation biasand make estimation feasible at the same time. We present Monte Carlosimulations to demonstrate the performance of our method compared to standardestimation methods where we ignore the high-dimensional explanatory variableset.

A Recursive Partitioning Approach for Dynamic Discrete Choice Modeling in High Dimensional Settings