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Michael Sørensen

Personal Details

First Name:Michael
Middle Name:
Last Name:Sørensen
Suffix:
RePEc Short-ID:psr28
http://www.math.ku.dk/~michael/

Affiliation

(in no particular order)

Københavns Universitet, Institut for Matematiske Fag

(University of Copenhagen, Department of Mathematical Sciences) http://www.math.ku.dk/
Denmark, Copenhagen

Center for Research in Econometric Analysis of Time Series (CREATES)
Institut for Økonomi (Department of Economics and Business)
Aarhus Universitet (University of Aarhus)

Aarhus, Denmark
http://www.creates.au.dk/



Building 1322, DK-8000 Aarhus C
RePEc:edi:creaudk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Michael Sørensen, 2011. "Prediction-based estimating functions: review and new developments," CREATES Research Papers 2011-05, Department of Economics and Business Economics, Aarhus University.
  2. Fernando Baltazar-Larios & Michael Sørensen, 2010. "Maximum likelihood estimation for integrated diffusion processes," CREATES Research Papers 2010-33, Department of Economics and Business Economics, Aarhus University.
  3. Mogens Bladt & Michael Sørensen, 2010. "Simple simulation of diffusion bridges with application to likelihood inference for diffusions," CREATES Research Papers 2010-32, Department of Economics and Business Economics, Aarhus University.
  4. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.
  5. Bent Jesper Christensen & Michael Sørensen, 2008. "Optimal inference in dynamic models with conditional moment restrictions," CREATES Research Papers 2008-51, Department of Economics and Business Economics, Aarhus University.
  6. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
  7. Michael Sørensen & Julie Lyng Forman, 2007. "The Pearson diffusions: A class of statistically tractable diffusion processes," CREATES Research Papers 2007-28, Department of Economics and Business Economics, Aarhus University.

Articles

  1. Uwe Küchler & Michael Sørensen, 2010. "A simple estimator for discrete-time samples from affine stochastic delay differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 13(2), pages 125-132, June.
  2. Gloter, Arnaud & Sørensen, Michael, 2009. "Estimation for stochastic differential equations with a small diffusion coefficient," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 679-699, March.
  3. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465.
  4. Kristian Stegenborg Larsen & Michael Sørensen, 2007. "Diffusion Models For Exchange Rates In A Target Zone," Mathematical Finance, Wiley Blackwell, vol. 17(2), pages 285-306.
  5. Mogens Bladt & Michael Sørensen, 2005. "Statistical inference for discretely observed Markov jump processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 395-410.
  6. Mathieu Kessler & Michael Sørensen, 2005. "On Time-Reversibility and Estimating Functions for Markov Processes," Statistical Inference for Stochastic Processes, Springer, vol. 8(1), pages 95-107, January.
  7. Susanne Ditlevsen & Michael Sørensen, 2004. "Inference for Observations of Integrated Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(3), pages 417-429.
  8. Michael Sørensen, 2000. "Prediction-based estimating functions," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 123-147.
  9. Uwe Küchler & Michael Sørensen, 1996. "Curved exponential families of stochastic processes and their envelope families," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(1), pages 61-74, March.
  10. Barndorff-Nielsen, O. E. & Sorensen, M., 1991. "Information quantities in non-classical settings," Computational Statistics & Data Analysis, Elsevier, vol. 12(2), pages 143-158, September.
  11. Sørensen, Michael, 1990. "On quasi likelihood for semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 35(2), pages 331-346, August.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Michael Sørensen, 2011. "Prediction-based estimating functions: review and new developments," CREATES Research Papers 2011-05, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. 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.
    2. 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.

  2. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Kengo Kamatani & Masayuki Uchida, 2015. "Hybrid multi-step estimators for stochastic differential equations based on sampled data," Statistical Inference for Stochastic Processes, Springer, vol. 18(2), pages 177-204, July.

  3. Bent Jesper Christensen & Michael Sørensen, 2008. "Optimal inference in dynamic models with conditional moment restrictions," CREATES Research Papers 2008-51, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465.
    2. Michael Sørensen, 2008. "Efficient estimation for ergodic diffusions sampled at high frequency," CREATES Research Papers 2007-46, Department of Economics and Business Economics, Aarhus University.
    3. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo Group Munich.

  4. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Yuichi Nagahara, 2008. "A Method of Calculating the Downside Risk by Multivariate Nonnormal Distributions," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 15(3), pages 175-184, December.

  5. Michael Sørensen & Julie Lyng Forman, 2007. "The Pearson diffusions: A class of statistically tractable diffusion processes," CREATES Research Papers 2007-28, Department of Economics and Business Economics, Aarhus University.

    Cited by:

    1. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    2. Damir Filipovi'c & Martin Larsson, 2017. "Polynomial Jump-Diffusion Models," Papers 1711.08043, arXiv.org.
    3. Nina Munkholt Jakobsen & Michael Sørensen, 2015. "Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval," CREATES Research Papers 2015-33, Department of Economics and Business Economics, Aarhus University.
    4. Yuichi Nagahara, 2011. "Using Nonnormal Distributions to Analyze the Relationship Between Stock Returns in Japan and the US," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 18(4), pages 429-443, November.
    5. Hanson, Gordon H. & Lind, Nelson & Muendler, Marc-Andreas, 2015. "The Dynamics of Comparative Advantage," CAGE Online Working Paper Series 252, Competitive Advantage in the Global Economy (CAGE).
    6. Pierre Blanc & Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Quadratic Hawkes processes for financial prices," Papers 1509.07710, arXiv.org.
    7. Bercu, Bernard & Richou, Adrien, 2017. "Large deviations for the Ornstein–Uhlenbeck process without tears," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 45-55.
    8. Ernstsen, Rune Ramsdal & Boomsma, Trine Krogh, 2018. "Valuation of power plants," European Journal of Operational Research, Elsevier, vol. 266(3), pages 1153-1174.
    9. Damir Filipović & Martin Larsson, 2016. "Polynomial diffusions and applications in finance," Finance and Stochastics, Springer, vol. 20(4), pages 931-972, October.
    10. Aleksandar Mijatovic & Paul Schneider, 2009. "Empirical asset pricing with nonlinear risk premia," Papers 0911.0928, arXiv.org.
    11. Leonenko, N.N. & Papić, I. & Sikorskii, A. & Šuvak, N., 2017. "Heavy-tailed fractional Pearson diffusions," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3512-3535.
    12. Christa Cuchiero & Martin Keller-Ressel & Josef Teichmann, 2012. "Polynomial processes and their applications to mathematical finance," Finance and Stochastics, Springer, vol. 16(4), pages 711-740, October.
    13. Filipović, Damir & Mayerhofer, Eberhard & Schneider, Paul, 2013. "Density approximations for multivariate affine jump-diffusion processes," Journal of Econometrics, Elsevier, vol. 176(2), pages 93-111.
    14. Yuichi Nagahara, 2008. "A Method of Calculating the Downside Risk by Multivariate Nonnormal Distributions," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 15(3), pages 175-184, December.
    15. Larsson, Martin & Pulido, Sergio, 2017. "Polynomial diffusions on compact quadric sets," Stochastic Processes and their Applications, Elsevier, vol. 127(3), pages 901-926.
    16. 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.
    17. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    18. Giorgos Sermaidis & Omiros Papaspiliopoulos & Gareth O. Roberts & Alexandros Beskos & Paul Fearnhead, 2013. "Markov Chain Monte Carlo for Exact Inference for Diffusions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(2), pages 294-321, June.

Articles

  1. Uwe Küchler & Michael Sørensen, 2010. "A simple estimator for discrete-time samples from affine stochastic delay differential equations," Statistical Inference for Stochastic Processes, Springer, vol. 13(2), pages 125-132, June.

    Cited by:

    1. Becker, Christoph & Schmidt, Wolfgang M., 2013. "Stressing correlations and volatilities — A consistent modeling approach," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 174-194.

  2. Gloter, Arnaud & Sørensen, Michael, 2009. "Estimation for stochastic differential equations with a small diffusion coefficient," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 679-699, March.

    Cited by:

    1. Ma, Chunhua & Yang, Xu, 2014. "Small noise fluctuations of the CIR model driven by α-stable noises," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 1-11.
    2. Long, Hongwei & Ma, Chunhua & Shimizu, Yasutaka, 2017. "Least squares estimators for stochastic differential equations driven by small Lévy noises," Stochastic Processes and their Applications, Elsevier, vol. 127(5), pages 1475-1495.
    3. Ma, Chunhua, 2010. "A note on "Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises"," Statistics & Probability Letters, Elsevier, vol. 80(19-20), pages 1528-1531, October.
    4. De Gregorio, A. & Iacus, S.M., 2013. "On a family of test statistics for discretely observed diffusion processes," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 292-316.
    5. Yasutaka Shimizu, 2017. "Threshold Estimation for Stochastic Processes with Small Noise," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 951-988, December.
    6. Long, Hongwei & Shimizu, Yasutaka & Sun, Wei, 2013. "Least squares estimators for discretely observed stochastic processes driven by small Lévy noises," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 422-439.
    7. Yang, Xu, 2017. "Maximum likelihood type estimation for discretely observed CIR model with small α-stable noises," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 18-27.
    8. Guy, Romain & Larédo, Catherine & Vergu, Elisabeta, 2014. "Parametric inference for discretely observed multidimensional diffusions with small diffusion coefficient," Stochastic Processes and their Applications, Elsevier, vol. 124(1), pages 51-80.

  3. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465.
    See citations under working paper version above.
  4. Kristian Stegenborg Larsen & Michael Sørensen, 2007. "Diffusion Models For Exchange Rates In A Target Zone," Mathematical Finance, Wiley Blackwell, vol. 17(2), pages 285-306.

    Cited by:

    1. Hui, Cho-Hoi & Lo, Chi-Fai & Fong, Tom Pak-Wing, 2016. "Swiss franc's one-sided target zone during 2011–2015," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 54-67.
    2. Taiga Saito, 2016. "Pricing Foreign Exchange Options Under Intervention by Absorption Modeling," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(1), pages 85-106, March.
    3. Almut E. D. Veraart & Luitgard A. M. Veraart, 2009. "Stochastic volatility and stochastic leverage," CREATES Research Papers 2009-20, Department of Economics and Business Economics, Aarhus University.
    4. Mordecai Avriel & Jens Hilscher & Alon Raviv, 2012. "Inflation Derivatives Under Inflation Target Regimes," Working Papers 43, Brandeis University, Department of Economics and International Businesss School.
    5. Damir Filipovi'c & Martin Larsson, 2017. "Polynomial Jump-Diffusion Models," Papers 1711.08043, arXiv.org.
    6. Nina Munkholt Jakobsen & Michael Sørensen, 2015. "Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval," CREATES Research Papers 2015-33, Department of Economics and Business Economics, Aarhus University.
    7. Chen, Yu-Fu & Funke, Michael & Glanemann, Nicole, 2012. "The Signalling Channel of Central Bank Interventions: Modelling the Yen/US Dollar Exchange Rate," SIRE Discussion Papers 2012-36, Scottish Institute for Research in Economics (SIRE).
    8. C. F. Lo & C. H. Hui & S. W. Chu & T. Fong, 2012. "A Quasi-Bounded Target Zone Model - Theory and Application to Hong Kong Dollar," Working Papers 282012, Hong Kong Institute for Monetary Research.
    9. Damir Filipović & Martin Larsson, 2016. "Polynomial diffusions and applications in finance," Finance and Stochastics, Springer, vol. 20(4), pages 931-972, October.
    10. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465.
    11. C. H. Hui & C. F. Lo & T. Fong, 2015. "A Quasi-Bounded Model for Swiss Franc's One-Sided Target Zone During 2011-2015," Working Papers 152015, Hong Kong Institute for Monetary Research.
    12. Tim Leung & Xin Li, 2014. "Optimal Mean Reversion Trading with Transaction Costs and Stop-Loss Exit," Papers 1411.5062, arXiv.org, revised May 2015.
    13. Christian Gouriéroux & Eric Renault & Pascale Valery, 2007. "Diffusion Processes with Polynomial Eigenfunctions," Annals of Economics and Statistics, GENES, issue 85, pages 115-130.
    14. Andreas Neuenkirch & Lukasz Szpruch, 2012. "First order strong approximations of scalar SDEs with values in a domain," Papers 1209.0390, arXiv.org.
    15. Bouleau, Nicolas & Chorro, Christophe, 2017. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright–Fisher diffusion process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 379-395.
    16. Larsson, Martin & Pulido, Sergio, 2017. "Polynomial diffusions on compact quadric sets," Stochastic Processes and their Applications, Elsevier, vol. 127(3), pages 901-926.
    17. Jeff Hamrick & Murad Taqqu, 2009. "Testing diffusion processes for non-stationarity," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 509-551, July.
    18. Nicolas Bouleau & Christophe Chorro, 2015. "The impact of randomness on the distribution of wealth: Some economic aspects of the Wright-Fisher diffusion process," Documents de travail du Centre d'Economie de la Sorbonne 15024r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Jul 2015.
    19. Michael Sørensen, 2008. "Parametric inference for discretely sampled stochastic differential equations," CREATES Research Papers 2008-18, Department of Economics and Business Economics, Aarhus University.
    20. Ghonghadze, Jaba & Lux, Thomas, 2016. "Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 1-19.
    21. Zhenxi, Chen & Lux, Thomas, 2015. "Estimation of sentiment effects in financial markets: A simulated method of moments approach," FinMaP-Working Papers 37, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    22. Peter Carr & Zura Kakushadze, 2015. "FX Options in Target Zone," Papers 1512.01527, arXiv.org, revised Jul 2016.
    23. Giorgio Ferrari & Tiziano Vargiolu, 2017. "On the Singular Control of Exchange Rates," Papers 1712.02164, arXiv.org.
    24. Lo, C.F. & Hui, C.H. & Fong, T. & Chu, S.W., 2015. "A quasi-bounded target zone model — Theory and application to Hong Kong dollar," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 1-17.

  5. Mogens Bladt & Michael Sørensen, 2005. "Statistical inference for discretely observed Markov jump processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 395-410.

    Cited by:

    1. Yasunari Inamura, 2006. "Estimating Continuous Time Transition Matrices From Discretely Observed Data," Bank of Japan Working Paper Series 06-E-7, Bank of Japan.
    2. Jia, Chen, 2016. "A solution to the reversible embedding problem for finite Markov chains," Statistics & Probability Letters, Elsevier, vol. 116(C), pages 122-130.
    3. Voß, Sebastian & Weißbach, Rafael, 2014. "A score-test on measurement errors in rating transition times," Journal of Econometrics, Elsevier, vol. 180(1), pages 16-29.
    4. Alexander Kremer & Rafael Weißbach, 2013. "Consistent estimation for discretely observed Markov jump processes with an absorbing state," Statistical Papers, Springer, vol. 54(4), pages 993-1007, November.
    5. Greig Smith & Goncalo dos Reis, 2017. "Robust and Consistent Estimation of Generators in Credit Risk," Papers 1702.08867, arXiv.org, revised Oct 2017.
    6. R. A. Hubbard & L. Y. T. Inoue & J. R. Fann, 2008. "Modeling Nonhomogeneous Markov Processes via Time Transformation," Biometrics, The International Biometric Society, vol. 64(3), pages 843-850, September.
    7. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    8. Huang, Jia-Ping & Sumita, Ushio, 2015. "Development of computational algorithms for pricing European bond options under the influence of macro-economic conditions," Applied Mathematics and Computation, Elsevier, vol. 251(C), pages 453-468.
    9. Kremer, Alexander & Weißbach, Rafael, 2014. "Asymptotic normality for discretely observed Markov jump processes with an absorbing state," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 136-139.

  6. Susanne Ditlevsen & Michael Sørensen, 2004. "Inference for Observations of Integrated Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 31(3), pages 417-429.

    Cited by:

    1. Comte, F. & Genon-Catalot, V. & Rozenholc, Y., 2009. "Nonparametric adaptive estimation for integrated diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 811-834, March.
    2. Jean Jacod & Mark Podolskij, 2012. "A Test for the Rank of the Volatility Process: The Random Perturbation Approach," Global COE Hi-Stat Discussion Paper Series gd12-268, Institute of Economic Research, Hitotsubashi University.
    3. Song, Yuping & Lin, Zhengyan, 2013. "Empirical likelihood inference for the second-order jump-diffusion model," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 184-195.
    4. Jean Jacod & Mark Podolskij, 2012. "A test for the rank of the volatility process: the random perturbation approach," CREATES Research Papers 2012-57, Department of Economics and Business Economics, Aarhus University.
    5. Julie Lyng Forman & Michael Sørensen, 2008. "The Pearson Diffusions: A Class of Statistically Tractable Diffusion Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 438-465.
    6. Nicolau, João, 2008. "Modeling financial time series through second-order stochastic differential equations," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2700-2704, November.
    7. Samson, Adeline & Thieullen, Michèle, 2012. "A contrast estimator for completely or partially observed hypoelliptic diffusion," Stochastic Processes and their Applications, Elsevier, vol. 122(7), pages 2521-2552.
    8. Friedrich Hubalek & Petra Posedel, 2008. "Asymptotic analysis for a simple explicit estimator in Barndorff-Nielsen and Shephard stochastic volatility models," Papers 0807.3479, arXiv.org.
    9. Blanke, Delphine & Vial, Céline, 2008. "Assessing the number of mean square derivatives of a Gaussian process," Stochastic Processes and their Applications, Elsevier, vol. 118(10), pages 1852-1869, October.
    10. Yunyan Wang & Lixin Zhang & Mingtian Tang, 2012. "Re-weighted functional estimation of second-order diffusion processes," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(8), pages 1129-1151, November.

  7. Michael Sørensen, 2000. "Prediction-based estimating functions," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 123-147.

    Cited by:

    1. Comte, F. & Genon-Catalot, V. & Rozenholc, Y., 2009. "Nonparametric adaptive estimation for integrated diffusions," Stochastic Processes and their Applications, Elsevier, vol. 119(3), pages 811-834, March.
    2. A. Hurn & J. Jeisman & K. Lindsay, 2007. "Teaching an Old Dog New Tricks: Improved Estimation of the Parameters of Stochastic Differential Equations by Numerical Solution of the Fokker-Planck Equation," NCER Working Paper Series 9, National Centre for Econometric Research.
    3. Benth, Fred Espen & Kiesel, Rüdiger & Nazarova, Anna, 2012. "A critical empirical study of three electricity spot price models," Energy Economics, Elsevier, vol. 34(5), pages 1589-1616.
    4. Leah Kelly, 2004. "Inference and Intraday Analysis of Diversified World Stock Indices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 24.
    5. Taufer, Emanuele & Leonenko, Nikolai & Bee, Marco, 2011. "Characteristic function estimation of Ornstein-Uhlenbeck-based stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 55(8), pages 2525-2539, August.
    6. 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.
    7. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Teaching an old dog new tricks: Improved estimation of the parameters of SDEs by numerical solution of the Fokker-Planck equation," Stan Hurn Discussion Papers 2006-01, School of Economics and Finance, Queensland University of Technology.
    8. Kallsen Jan & Muhle-Karbe Johannes, 2011. "Method of moment estimation in time-changed Lévy models," Statistics & Risk Modeling, De Gruyter, vol. 28(2), pages 169-194, May.
    9. 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.
    10. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations," Stan Hurn Discussion Papers 2006, School of Economics and Finance, Queensland University of Technology.
    11. Stan Hurn & J.Jeisman & K.A. Lindsay, 2006. "Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations. Working paper #2," NCER Working Paper Series 2, National Centre for Econometric Research.

  8. Barndorff-Nielsen, O. E. & Sorensen, M., 1991. "Information quantities in non-classical settings," Computational Statistics & Data Analysis, Elsevier, vol. 12(2), pages 143-158, September.

    Cited by:

    1. Küchler, Uwe & Sørensen, Michael M., 1998. "A note on limit theorems for multivariate martingales," SFB 373 Discussion Papers 1998,45, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  9. Sørensen, Michael, 1990. "On quasi likelihood for semimartingales," Stochastic Processes and their Applications, Elsevier, vol. 35(2), pages 331-346, August.

    Cited by:

    1. Florens, Danielle & Pham, Huyên, 1998. "Large deviation probabilities in estimation of Poisson random measures," Stochastic Processes and their Applications, Elsevier, vol. 76(1), pages 117-139, August.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (7) 2008-06-27 2008-06-27 2008-06-27 2008-09-29 2010-09-03 2010-09-03 2011-01-30. Author is listed
  2. NEP-ETS: Econometric Time Series (6) 2008-06-27 2008-06-27 2008-06-27 2008-09-29 2010-09-03 2010-09-03. Author is listed
  3. NEP-ORE: Operations Research (3) 2008-06-27 2010-09-03 2011-01-30
  4. NEP-CMP: Computational Economics (1) 2010-09-03
  5. NEP-ICT: Information & Communication Technologies (1) 2008-06-27
  6. NEP-MST: Market Microstructure (1) 2008-06-27

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