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

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  • Andr� A. Monteiro

    ()
    (VU University Amsterdam, and University of Western Australia)

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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Bibliographic 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

Related research

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

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  1. Jean-Francois Richard, 2007. "Efficient High-Dimensional Importance Sampling," Working Papers, University of Pittsburgh, Department of Economics 321, University of Pittsburgh, Department of Economics, revised Jan 2007.
  2. Clive Bowsher, 2002. "Modelling Security Market Events in Continuous Time: Intensity based, Multivariate Point Process Models," Economics Papers 2002-W22, Economics Group, Nuffield College, University of Oxford.
  3. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, American Finance Association, vol. 46(1), pages 179-207, March.
  4. Luc Bauwens & Nikolaus Hautsch, 2006. "Stochastic Conditional Intensity Processes," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(3), pages 450-493.
  5. Luc Bauwens & David Veredas, 2004. "The stochastic conditional duration model: a latent factor model for the analysis of financial durations," ULB Institutional Repository 2013/136234, ULB -- Universite Libre de Bruxelles.
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