Dynamic Latent Variable Models-Likelihood Evaluation

动态潜变量模型-似然评估

• 批准号: 9223365
• 负责人: Jean-Francois Richard
• 金额: $ 20.07万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-03-15 至 1996-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9223365&HistoricalAwards=false
• 关键词: Dynamic; Latent; Variable; Models; Likelihood

Since the 1970's there has been a major resurgence of interest in the topic of latent variable models. Latent variable models are broadly defined as variables which enter the formulation of an econometric model and yet are not (directly) observable. Latent variables are widely recognized to be major components of the behavior of economic agents. They are inherently dynamic for a broad class of models including intertemporal optimization, search or duration processes, error correction mechanisms, habit formation or persistence, rational expectations and state dependence. The likelihood functions of dynamic latent variable models are mostly analytically intractable, largely because the elimination of the latent variables require high dimensional interdependent integration with the additional complication that the actual distribution of the latent variables conditional on the observables can rarely be seen. Without likelihood functions, it is impossible to rigorously and accurately test economic theories with real-world data because one can not answer the question what is the probability that the observations are due to chance and do not reflect the theory being tested. A number of Monte Carlo simulation techniques have been developed and used to estimate the characteristics of these likelihood functions. The main problem arises from the fact the "brute force" MC estimates of likelihood functions typically take a prohibitively large number of iterations for all but small sample sizes. A number of acceleration techniques are currently available. These techniques can produce fairly dramatic efficiency gains (by a factor of 1,000 or more), but they come nowhere close to what is actually required in order to render MC likelihood evaluation practical for moderate to large sample sizes. The contribution of this project comes from developing a new generic acceleration technique that in a pilot study achieved results unheard of in the econometric literature. All indications are that the new technique might well constitute a major leap in Monte Carlo technology. This project operationalizes the new acceleration technique within the context of a broad range of dynamic latent variable models. By removing a key stumbling block to the analysis of dynamic latent variable models, the new technique opens intriguing new avenues of research across a broad range of important economic applications. In this project the technique is applied it to a set of substantive real-life applications drawn from the recent literature on financial markets, disequilibrium models and housing markets. Systematic comparisons with results derived under alternative techniques provide an assessment of the merits of the new techniques from a practitioner's viewpoint. Software needed to use the new technique will be made available to other researchers.

自20世纪70年代以来，人们对 潜在变量模型的主题。 潜在变量模型是 广义上被定义为进入公式的变量， 经济计量模型，但不能（直接）观察。 潜 变量被广泛认为是 经济行为主体。 对于一个 包括跨期优化在内的广泛的模型类别， 搜索或持续过程，纠错机制，习惯 形成或持续，理性预期和状态 依赖 动态潜变量的似然函数 模型在分析上大多是难以处理的，主要是因为 隐变量的消除需要高维 相互依存的一体化， 潜在变量的实际分布取决于 可观测的东西很少能被看到。 没有可能性 功能，不可能严格和准确地测试 经济理论与现实世界的数据，因为一个人不能回答 这个问题是什么概率的意见是 这是偶然的，并不反映正在测试的理论。 一 已经开发了许多蒙特卡罗模拟技术 并用于估计这些可能性的特征 功能协调发展的 主要的问题在于， 力”的MC估计的似然函数通常采取 除了小样本之外，所有样本的迭代次数都非常多 尺寸. 目前有许多加速技术 available. 这些技术可以产生相当戏剧性的 效率的提高（1,000倍或更多），但它们 与实际所需的相差甚远， 适用于中到大样本的可能性评估 尺寸. 该项目的贡献来自于开发一个 新的通用加速技术，在试点研究中实现 计量经济学文献中闻所未闻的结果。 所有 有迹象表明，这项新技术很可能构成一种 蒙特卡罗技术的重大飞跃。 该项目实施了新的加速技术 在广泛的动态潜在变量的背景下， 模型 通过消除一个关键的绊脚石， 动态潜变量模型，新技术打开 有趣的新的研究途径，在广泛的范围内， 重要的经济应用。 在这个项目中， 将其应用于一系列实质性的现实应用中， 从最近关于金融市场的文献中得出， 非均衡模型和住房市场。 系统 与替代技术得出的结果的比较 提供一个评估的优点，新技术从一个 实践者的观点。 需要使用新的 技术将提供给其他研究人员。