Sensitivity Analysis for Dynamic Discrete Choice Models

TL;DR

This paper introduces local and global sensitivity analysis methods for dynamic discrete choice models, enabling researchers to assess how fixed parameters like the discount factor influence model outcomes without extensive re-estimation.

Contribution

It presents novel, computationally efficient sensitivity analysis procedures tailored for dynamic discrete choice models, addressing the challenge of fixed parameters.

Findings

Local sensitivity measure estimates parameter changes efficiently.

Global sensitivity analysis links target and fixed parameters using model primitives.

Applications demonstrate practical utility of the methods.

Abstract

In dynamic discrete choice models, some parameters, such as the discount factor, are being fixed instead of being estimated. This paper proposes two sensitivity analysis procedures for dynamic discrete choice models with respect to the fixed parameters. First, I develop a local sensitivity measure that estimates the change in the target parameter for a unit change in the fixed parameter. This measure is fast to compute as it does not require model re-estimation. Second, I propose a global sensitivity analysis procedure that uses model primitives to study the relationship between target parameters and fixed parameters. I show how to apply the sensitivity analysis procedures of this paper through two empirical applications.