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Simulation-based likelihood inference for limited dependent processes

Author

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  • AURORA MANRIQUE
  • NEIL SHEPHARD

Abstract

This paper looks at the problem of performing likelihood inference for limited dependent processes. Throughout we use simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis. A common theme is to develop computationally robust methods which are likely to perform well for any time series problem. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation signal smoother.

Suggested Citation

  • Aurora Manrique & Neil Shephard, 1998. "Simulation-based likelihood inference for limited dependent processes," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 174-202.
    • Handle: RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c174-c202
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    Cited by:

    1. Edwige Burdeau, 2015. "Assessing dynamics of credit supply and demand for French SMEs, an estimation based on the Bank Lending Survey," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
    2. Kristensen, Dennis & Shin, Yongseok, 2012. "Estimation of dynamic models with nonparametric simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 167(1), pages 76-94.
    3. Douc, R. & Fort, G. & Moulines, E. & Priouret, P., 2009. "Forgetting the initial distribution for Hidden Markov Models," Stochastic Processes and their Applications, Elsevier, vol. 119(4), pages 1235-1256, April.
    4. Joel Hasbrouck, 1998. "Liquidity in the Futures Pits: Inferring Market Dynamics from Incomplete Data," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-076, New York University, Leonard N. Stern School of Business-.
    5. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2008. "Determinants of bid and ask quotes and implications for the cost of trading," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 656-678, September.
    6. Luc Bauwens & Michel Lubrano, 2007. "Bayesian Inference in Dynamic Disequilibrium Models: An Application to the Polish Credit Market," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 469-486.
    7. Hasbrouck, Joel, 1999. "Security bid/ask dynamics with discreteness and clustering: Simple strategies for modeling and estimation1," Journal of Financial Markets, Elsevier, vol. 2(1), pages 1-28, February.
    8. Hautsch, Nikolaus & Pohlmeier, Winfried, 2001. "Econometric Analysis of Financial Transaction Data: Pitfalls and Opportunities," CoFE Discussion Papers 01/05, University of Konstanz, Center of Finance and Econometrics (CoFE).
    9. Harvey, Andew & Liao, Yin, 2023. "Dynamic Tobit models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 72-83.
    10. A. E. Brockwell & N. H. Chan & P. K. Lee, 2003. "A class of models for aggregated traffic volume time series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(4), pages 417-430, October.

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