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利用平滑与筛方法求解动态离散选择模型AI 解读

We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. We use importance sampling to approximate the Bellman operators defining the two functions. The random Bellman operators, and therefore also the corresponding solutions, are generally non-smooth which is undesirable. To circumvent this issue, we introduce smoothed versions of the random…

离散—连续动态选择模型的内生网格法：有无偏好冲击的情形AI 解读

We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables. The solution method we develop for structural estimation extends the endogenous grid-point method (EGM) to discrete-continuous (DC) problems. Discrete choices can lead to kinks in the value functions and discontinuities in the optimal policy rules, greatly complicating the solution of the model. We show how these…

评论《结构模型估计的约束优化方法》AI 解读

We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (SJ, 2012). Their implementation of the nested fixed point algorithm used successive approximations to solve the inner fixed point problem (NFXP-SA). We re-do their comparison using the more efficient version of NFXP proposed by Rust (1987), which combines successive approximations and…