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Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings.
Author:	Ribeiro, Tiago.
Description:	58 p.
Notes:	Chair: Daniel L. McFadden.
Notes:	Source: Dissertation Abstracts International, Volume: 64-09, Section: A, page: 3423.
Contained By:	Dissertation Abstracts International64-09A.
Subject:	Economics, Theory.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3105348
ISBN:	0496529153

Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings.
Ribeiro, Tiago.

Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings. [electronic resource] - 58 p.

Chair: Daniel L. McFadden.

Thesis (Ph.D.)--University of California, Berkeley, 2003.

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.

ISBN: 0496529153Subjects--Topical Terms:

212740
Economics, Theory.

Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings.
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245 1 0 $a Hidden Markov models and their applications to estimation, forecasting and policy analysis in panel data settings. $h [electronic resource]
300 $a 58 p.
500 $a Chair: Daniel L. McFadden.
500 $a Source: Dissertation Abstracts International, Volume: 64-09, Section: A, page: 3423.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2003.
520 # $a 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.
520 # $a 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.
520 # $a 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.
590 $a School code: 0028.
650 # 0 $a Economics, Theory. $3 212740
650 # 0 $a Statistics. $3 182057
650 # 0 $a Health Sciences, Public Health. $3 212500
710 0 # $a University of California, Berkeley. $3 212474
773 0 # $g 64-09A. $t Dissertation Abstracts International
790 $a 0028
790 1 0 $a McFadden, Daniel L., $e advisor
791 $a Ph.D.
792 $a 2003
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