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AI 根据所提供的论文材料归类:摘要联合建模企业购买策略与内生抽样的马尔可夫价格时间序列。

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关注这篇论文的作者
George Hall
John Rust

内生抽样时间序列的估计:钢材市场价格投机案例

研究问题
当钢材价格仅在企业购买当天被观测、购买时点又取决于价格状态和协变量时,如何估计价格过程并检验企业购买行为的最优性?
方法与思路
作者分别建立利润最大化购买策略和允许非最优的随机马尔可夫策略,以筛法近似后者,并模拟相同购买抽样机制进行矩估计。
材料陈述的贡献
摘要称,两种行为设定下估计均可实施;应用估计截断对数正态AR(1)钢材价格过程,并拒绝企业购买行为最优性的假设。
阅读关注点
应核对抽样机制与价格过程的联合识别、最优策略的经济设定、模拟矩选择,以及非最优策略的筛近似与最优性检验。
阅读边界
仅依据所给出版社摘要;摘要未说明数据规模、检验水平或模型敏感性,拒绝最优性仅是所述模型与应用下的作者结果。

原文摘要

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 pt on the days the firm purchases steel. We show how to solve this problem under two different assumptions about firm behavior: (1) optimality: the timing of steel purchases is governed by an optimal purchasing strategy that maximizes expected discounted profits, and (2) potential suboptimality: we allow the firm to use any randomized, Markovian purchasing strategy. In the latter case, the estimation problem becomes semi-parametric and we use the method of sieves to estimate a flexible parametric approximation to the firm’s purchasing behavior that best fits the data without imposing optimality. We show how estimation of this model becomes tractable under either of these assumptions using the method of simulated moments (MSM). We simulate realizations of wholesale steel prices and sample them in the same way as they are sampled in the actual data, i.e. only on days where purchases occur. We use the MSM estimator to estimate a truncated lognormal AR(1) model of the wholesale price processes for particular types of steel plate and test and reject the assumption that the firm is behaving optimally.

笔记与分组
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