Pairwise-Difference Estimation of a Dynamic Optimization Model∗

We develop a new estimation methodology for a dynamic optimization model with unobserved shocks. We propose a pairwise-difference approach which exploits two common features of the dynamic optimization problem we consider: (1) the monotonicity of the agent’s decision (policy) function in the shocks, conditional on the observed state variables; and (2) the state-contingent nature of optimal decision-making which implies that, conditional on the observed state variables, the variation in observed choices across agents must be due to randomness in the shocks across agents. We illustrate our procedure by estimating a dynamic trading model for the milk production quota market in Ontario, Canada.