EconStor >
The Johns Hopkins University, Baltimore, Md. >
Department of Economics, The Johns Hopkins University >
Working Papers, Department of Economics, The Johns Hopkins University >

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10419/49872
  
Title:A simple estimator for dynamic models with serially correlated unobservables PDF Logo
Authors:Hu, Yingyao
Shum, Matthew
Tan, Wei
Issue Date:2010
Series/Report no.:Working papers // the Johns Hopkins University, Department of Economics 558
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.
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

Files in This Item:
File Description SizeFormat
635250888.pdf254.36 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:http://hdl.handle.net/10419/49872

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.