Pairwise-Difference Estimation of a Dynamic Optimization Model
提出一种新的估计方法，用于处理含有未观测冲击和确定性状态变量积累的动态优化模型，并通过加拿大安大略省牛奶生产配额市场的动态交易模型进行实证说明。
We develop a new estimation methodology for dynamic optimization models with unobserved shocks and deterministic accumulation of the observed state variables. Investment models are an important example of such models. Our pairwise-difference approach exploits two common features of these models: (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.