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首页> 外文学位 >Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings.
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Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings.

机译：隐马尔可夫模型及其在面板数据设置中的估计，预测和策略分析中的应用。

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摘要

This dissertation proposes modelling procedures for the joint distribution of a high dimensional vector of discrete variables and of its evolution over time. This work is motivated by problems arising in the use of panel survey data to forecast the simultaneous behavior of certain characteristics of a population.;The models proposed fall under the general classification of Hidden Markov Models (HMM). A dimension reduction of chosen groups of variables is performed, which are each represented by a latent variable whose dynamics is modelled. This combines features of multiple indicator-multiple cause (MIMIC) and LISREL factor-analytic models in the sense that some interpretable structure is imposed on the relations between a given number of latent variables that generate the observations with features of filtering algorithms like the Kalman filter which are used to estimate the dynamic component of the latent variables. Estimation does not require use of simulation to perform numerical integration and can also incorporate directly missing observations hence removing the need for separate imputation procedures. Several specification tests are also discussed.;The methodology developed is applied to analyze the evolution of health conditions over an 8 year period of the HRS population and the effects of availability of Medicare insurance on the dynamics of health.
机译：本文提出了离散变量高维矢量联合分布及其随时间演化的建模程序。这项工作的动机是使用面板调查数据预测人群某些特征的同时行为而产生的问题。所提出的模型属于隐马尔可夫模型（HMM）的一般分类。执行所选变量组的降维，每个变量组都由一个其动态建模的潜在变量表示。它结合了多指标多原因（MIMIC）模型和LISREL因子分析模型的特征，在某种意义上，给定数量的潜在变量之间的关系被强加了一些可解释的结构，这些潜在变量生成了具有卡尔曼滤波器等滤波算法的特征的观测值用于估计潜在变量的动态分量。估计不需要使用模拟来执行数值积分，并且还可以直接合并缺失的观测值，因此不需要单独的插补程序。还讨论了几个规格测试。所开发的方法用于分析HRS人群8年内健康状况的演变以及Medicare保险的可获得性对健康动态的影响。

著录项

  • 作者

    Ribeiro, Tiago.;

  • 作者单位

    University of California, Berkeley.;

  • 授予单位 University of California, Berkeley.;
  • 学科 Statistics.;Health Sciences Public Health.;Economics Theory.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 58 p.
  • 总页数 58
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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