Prediction-based estimating functions: review and new developments
AbstractThe general theory of prediction-based estimating functions for stochastic process models is reviewed and extended. Particular attention is given to optimal estimation, asymptotic theory and Gaussian processes. Several examples of applications are presented. In particular partial observation of a systems of stochastic differential equations is discussed. This includes diffusions observed with measurement errors, integrated diffusions, stochastic volatility models, and hypoelliptic stochastic differential equations. The Pearson diffusions, for which explicit optimal prediction-based estimating functions can be found, are briefly presented.
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Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-05.
Date of creation: 19 Jan 2011
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Web page: http://www.econ.au.dk/afn/
Aasymptotic normality; consistency; diffusion with measurement errors; Gaussian process; integrated diffusion; linear predictors; non-Markovian models; optimal estimating function; partially observed system; Pearson diffusion.;
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- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-01-30 (All new papers)
- NEP-ECM-2011-01-30 (Econometrics)
- NEP-ORE-2011-01-30 (Operations Research)
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- Asger Lunde & Anne Floor Brix, 2013. "Estimating Stochastic Volatility Models using Prediction-based Estimating Functions," CREATES Research Papers 2013-23, School of Economics and Management, University of Aarhus.
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