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A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

Hu, Yingyao and Shum, Matthew and Tan, Wei and Xiao, Ruli (2017) A Simple Estimator for Dynamic Models with Serially Correlated Unobservables. Journal of Econometric Methods, 6 (1). ISSN 2156-6674. https://resolver.caltech.edu/CaltechAUTHORS:20160329-103204775

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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:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1515/jem-2015-0011DOIArticle
https://www.degruyter.com/view/j/jem.2017.6.issue-1/jem-2015-0011/jem-2015-0011.xmlPublisherArticle
http://resolver.caltech.edu/CaltechAUTHORS:20160330-124651115Related ItemWorking Paper
ORCID:
AuthorORCID
Shum, Matthew0000-0002-6262-915X
Additional Information:© 2017 Walter de Gruyter GmbH. Published Online: 2015-11-06. Completed: September 27, 2015.
Subject Keywords:dynamic models; serially correlated unobservables; unobserved state variable
Issue or Number:1
Record Number:CaltechAUTHORS:20160329-103204775
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160329-103204775
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65738
Collection:CaltechAUTHORS
Deposited By: Susan Vite
Deposited On:30 Mar 2016 19:44
Last Modified:03 Oct 2019 09:50

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