Unnesting the Fixed Point in Dynamic Program Estimation

A conditional choice probability (CCP) estimator of a dynamic empirical model solves both a dynamic programming problem and a maximum likelihood problem. The estimator can dispatch the former problem before tackling the latter when the utility function is linearly parameterized; otherwise it must nest the former within the latter. This ``nested fixed point" bogs down the estimator, requiring it to solve hundreds or thousands of difficult value function equations. I develop a method to disentangle the two problems under any utility function (thus circumventing the onerous value function calculations). My estimator is asymptotically efficient and has a closed-form characterization when the utility function is linearly parameterized.