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Prediction-based estimating functions: review and new developments

Author

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  • Michael Sørensen

    (University of Copenhagen and CREATES)

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.

Suggested Citation

  • Michael Sørensen, 2011. "Prediction-based estimating functions: review and new developments," CREATES Research Papers 2011-05, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2011-05
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    File URL: https://repec.econ.au.dk/repec/creates/rp/11/rp11_05.pdf
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    Citations

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    Cited by:

    1. Enrico Bibbona & Ilia Negri, 2015. "Higher Moments and Prediction-Based Estimation for the COGARCH(1,1) Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 891-910, December.
    2. Anne Brix & Asger Lunde, 2015. "Prediction-based estimating functions for stochastic volatility models with noisy data: comparison with a GMM alternative," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(4), pages 433-465, October.
    3. Asger Lunde & Anne Floor Brix, 2013. "Estimating Stochastic Volatility Models using Prediction-based Estimating Functions," CREATES Research Papers 2013-23, Department of Economics and Business Economics, Aarhus University.

    More about this item

    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.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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