A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

Yingyao Hu, Matthew Shum (), Tan Wei and Xiao Ruli
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Tan Wei: Compass-Lexecon, 1101 K Street NW, 8 th Floor, Washington, DC 20005, USA
Xiao Ruli: College of Arts & Sciences, Department of Economics, Wylie Hall, 100 South Woodlawn Avenue, Bloomington, Indiana 47405-7104, USA

Journal of Econometric Methods, 2017, vol. 6, issue 1, 16

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.

Keywords: dynamic models; serially correlated unobservables; unobserved state variable (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: A Simple Estimator for Dynamic Models with Serially Correlated Unobservables (2015)
Working Paper: A Simple Estimator for Dynamic Models with Serially Correlated Unobservables (2010)
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DOI: 10.1515/jem-2015-0011

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