A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

Yingyao Hu, Matthew Shum (), Wei Tan () and Ruli Xiao ()
Additional contact information
Matthew Shum: Caltech
Wei Tan: Compass-Lexecon
Ruli Xiao: Indiana University
Authors registered in the RePEc Author Service: Matthew Shum ()

No 2015-017, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

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

Pages: 25 pages
Date: 2015-09
New Economics Papers: this item is included in nep-dcm and nep-ets
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: A Simple Estimator for Dynamic Models with Serially Correlated Unobservables (2017)
Working Paper: A Simple Estimator for Dynamic Models with Serially Correlated Unobservables (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:inu:caeprp:2015017

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