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Parameter Driven Multi-state Duration Models: Simulated vs. Approximate Maximum Likelihood Estimation

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Author Info
André A. Monteiro () (VU University Amsterdam, and University of Western Australia)

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Abstract

Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews, adapts and compares three different approaches for solving this problem. For evaluating the likelihood, two of the methods rely on Monte Carlo integration with importance sampling techniques. The third method, in contrast, is based on fully deterministic numerical procedures. A Monte Carlo study is conducted to illustrate the use of each method, and assess its corresponding finite sample performance.

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Publisher Info
Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 08-021/2.

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Date of creation: 27 Feb 2008
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Handle: RePEc:dgr:uvatin:20080021

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Web page: http://www.tinbergen.nl/

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Related research
Keywords: Multi-state Duration models; Parameter Driven models; Simulated Maximum Likelihood; Importance Sampling;

Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis

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  1. BAUWENS, Luc & VEREDAS, David, 1999. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," CORE Discussion Papers 1999058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
  2. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March. [Downloadable!] (restricted)
  3. Jean-Francois Richard & Wei Zhang, 2007. "Efficient High-Dimensional Importance Sampling," Working Papers 321, University of Pittsburgh, Department of Economics, revised Jan 2007. [Downloadable!]
  4. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, vol. 141(2), pages 1385-1411, December. [Downloadable!] (restricted)
  5. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December. [Downloadable!] (restricted)
  6. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Oxford University Press, vol. 4(3), pages 450-493. [Downloadable!] (restricted)
  7. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
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