Prediction-based estimating functions: review and new developments

Michael Sørensen ()

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: The 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.

Keywords: Aasymptotic normality; consistency; diffusion with measurement errors; Gaussian process; integrated diffusion; linear predictors; non-Markovian models; optimal estimating function; partially observed system; Pearson diffusion. (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
Pages: 27
Date: 2011-01-19
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (3)

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