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Quasi-likelihood estimation for semimartingales


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  • Hutton, James E.
  • Nelson, Paul I.
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    A technique of parameter estimation for a semimartingale based on the maximization of a likelihood type function is proposed. This technique is shown to be optimal in the sense of Godambe within a certain class of estimating equations. The resulting estimators are shown to be consistent and asymptotically normally distributed on certain events under relatively weak assumptions.

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    Bibliographic Info

    Article provided by Elsevier in its journal Stochastic Processes and their Applications.

    Volume (Year): 22 (1986)
    Issue (Month): 2 (July)
    Pages: 245-257

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    Handle: RePEc:eee:spapps:v:22:y:1986:i:2:p:245-257

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    Keywords: consistent estimation semimartingale birth and death process quasi-likelihood;


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    Cited by:
    1. Thavaneswaran, A. & Peiris, Shelton, 1998. "Hypothesis testing for some time-series models: a power comparison," Statistics & Probability Letters, Elsevier, vol. 38(2), pages 151-156, June.
    2. Peter C. B. Phillips & Jun Yu, 2006. "A Two-Stage Realized Volatility Approach to Estimation of Diffusion Processes with Discrete," Macroeconomics Working Papers 22472, East Asian Bureau of Economic Research.
    3. Kim, Yoon Tae, 1999. "Parameter estimation in infinite-dimensional stochastic differential equations," Statistics & Probability Letters, Elsevier, vol. 45(3), pages 195-204, November.
    4. Phillips, Peter C.B. & Yu, Jun, 2009. "A two-stage realized volatility approach to estimation of diffusion processes with discrete data," Journal of Econometrics, Elsevier, vol. 150(2), pages 139-150, June.
    5. Peter C.B. Phillips & Jun Yu, 2005. "A Two-Stage Realized Volatility Approach to the Estimation for Diffusion Processes from Discrete Observations," Cowles Foundation Discussion Papers 1523, Cowles Foundation for Research in Economics, Yale University.
    6. Teo Sharia, 2010. "Recursive parameter estimation: asymptotic expansion," Annals of the Institute of Statistical Mathematics, Springer, vol. 62(2), pages 343-362, April.


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