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内生抽样时间序列的估计：钢材市场价格投机案例AI 解读

We consider the problem of estimating a Markov process that is endogenously sampled. We observe a discrete-time Markov process {pt} at a set of random times {t1,…,tn} that depend on the outcome of a probabilistic sampling rule that depends on the state of the process and other observed covariates xt. We focus on a particular example where pt is the daily wholesale price of a standardized steel product. The endogenous sampling problem arises from the fact that we only observe…

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…

摘要暂缺

导言：推断与决策AI 解读

A major focus of applied econometric work is decision making in the context of uncertainty.Sometimes this work is several steps removed from the actual decision.For example, it may entail providing information in a form more suited to decision making, or reducing the uncertainty in the decision-making environment.Increasingly, applied econometricians are becoming more directly involved in decision making.The quality and quantity of data available are increasing, while the…

利用随机化突破维度灾难AI 解读

This paper introduces random versions of successive approximations and multigrid algorithms for computing approximate solutions to a class of finite and infinite horizon Markovian decision problems. The author proves that these algorithms succeed in breaking the 'curse of dimensionality' for a subclass of Markovian decision problems known as discrete decision processes.

GMC 公交车发动机的最优更换：Harold Zurcher 的实证模型AI 解读

This paper formulates a simple, regenerative, optimal-stopping model of bus-eng ine replacement to describe the behavior of Harold Zurcher, superinte ndent of maintenance at the Madison (Wisconsin) Metropolitan Bus Comp any. Admittedly, few people are likely to take particular interest in Harold Zurcher and bus engine replacement per se. The author focuses on a specific individual and capital good because it provides a simp le, concrete framework to illustrate two ideas: (1)…