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A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

Hu, Yingyao and Shum, Matthew and Tan, Wei (2010) A Simple Estimator for Dynamic Models with Serially Correlated Unobservables. Social Science Working Paper, 1324. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20160330-124651115

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Abstract

We present a method for estimating Markov dynamic models with unobserved state variables which can be serially correlated over time. We focus on the case where all the model variables have discrete support. Our estimator is simple to compute because it is noniterative, and involves only elementary matrix manipulations. Our estimation method is nonparametric, in that no parametric assumptions on the distributions of the unobserved state variables or the laws of motions of the state variables are required. Monte Carlo simulations show that the estimator performs well in practice, and we illustrate its use with a dataset of doctors’ prescription of pharmaceutical drugs.


Item Type:Report or Paper (Working Paper)
Related URLs:
URLURL TypeDescription
http://resolver.caltech.edu/CaltechAUTHORS:20160329-103204775Related ItemUpdated version published as journal article
ORCID:
AuthorORCID
Shum, Matthew0000-0002-6262-915X
Additional Information:May 2010.
Group:Social Science Working Papers
Subject Keywords:dynamic models, serially-correlated unobservables, identification
Classification Code:JEL: C13, C33, C51
Record Number:CaltechAUTHORS:20160330-124651115
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20160330-124651115
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65765
Collection:CaltechAUTHORS
Deposited By: Katherine Johnson
Deposited On:30 Mar 2016 19:54
Last Modified:11 Sep 2017 22:28

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