NDLI: A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

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A Simple Estimator for Dynamic Models with Serially Correlated Unobservables

Content Provider	Scilit
Author	Hu, Yingyao Shum, Matthew Tan, Wei Xiao, Ruli
Copyright Year	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.
Related Links	http://www.hss.caltech.edu/SSPapers/sswp1324.pdf http://www.degruyter.com/downloadpdf/j/jem.2017.6.issue-1/jem-2015-0011/jem-2015-0011.xml
ISSN	21946345
e-ISSN	21566674
DOI	10.1515/jem-2015-0011
Journal	Journal of Econometric Methods
Issue Number	1
Volume Number	6
Language	English
Publisher	Walter de Gruyter GmbH
Publisher Date	2015-11-06
Access Restriction	Open
Subject Keyword	Journal of Econometric Methods Mathematical Social Sciences Dynamic Models Serially Correlated Unobservables Unobserved State Variable Journal: Journal of Econometric Methods, Vol- 6
Content Type	Text
Resource Type	Article

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