A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

26 Pages Posted: 30 Sep 2015

See all articles by Yingyao Hu

Yingyao Hu

Johns Hopkins University - Department of Economics

Matthew Shum

California Institute of Technology

Wei Tan

Compass Lexecon

Ruli Xiao

Indiana University; Indiana University

Date Written: September 28, 2015

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.

Suggested Citation

Hu, Yingyao and Shum, Matthew and Tan, Wei and Xiao, Ruli and Xiao, Ruli, A Simple Estimator for Dynamic Models with Serially Correlated Unobservables (September 28, 2015). Available at SSRN: https://ssrn.com/abstract=2666737 or http://dx.doi.org/10.2139/ssrn.2666737

Yingyao Hu

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

Matthew Shum

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Wei Tan

Compass Lexecon ( email )

United States

Ruli Xiao (Contact Author)

Indiana University ( email )

Wylie Hall
Bloomington, IN 47405-6620
United States

Indiana University ( email )

100 S Woodlawn Ave
Bloomington, IN 47405
United States

HOME PAGE: http://https://sites.google.com/site/iueconomicsrulixiao/

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