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Efficient learning via simulation: A marginalized resample-move approach

  • Fulop, Andras
  • Li, Junye
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    In state–space models, parameter learning is practically difficult and is still an open issue. This paper proposes an efficient simulation-based parameter learning method. First, the approach breaks up the interdependence of the hidden states and the static parameters by marginalizing out the states using a particle filter. Second, it applies a Bayesian resample-move approach to this marginalized system. The methodology is generic and needs little design effort. Different from batch estimation methods, it provides posterior quantities necessary for full sequential inference and recursive model monitoring. The algorithm is implemented both on simulated data in a linear Gaussian model for illustration and comparison and on real data in a Lévy jump stochastic volatility model and a structural credit risk model.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0304407613001164
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    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 176 (2013)
    Issue (Month): 2 ()
    Pages: 146-161

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    Handle: RePEc:eee:econom:v:176:y:2013:i:2:p:146-161
    Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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    1. Pitt, Michael K. & Silva, Ralph dos Santos & Giordani, Paolo & Kohn, Robert, 2012. "On some properties of Markov chain Monte Carlo simulation methods based on the particle filter," Journal of Econometrics, Elsevier, vol. 171(2), pages 134-151.
    2. Walter R. Gilks & Carlo Berzuini, 2001. "Following a moving target-Monte Carlo inference for dynamic Bayesian models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 127-146.
    3. Nicolas Chopin, 2000. "A Sequential Particle Filter Method for Static Models," Working Papers 2000-45, Centre de Recherche en Economie et Statistique.
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    5. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    6. Duan, Jin-Chuan & Fulop, Andras, 2009. "Estimating the structural credit risk model when equity prices are contaminated by trading noises," Journal of Econometrics, Elsevier, vol. 150(2), pages 288-296, June.
    7. Shirley J. Huang & Jun Yu, 2009. "Bayesian Analysis of Structural Credit Risk Models with Microstructure Noises," Finance Working Papers 23054, East Asian Bureau of Economic Research.
    8. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-43.
    9. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-70, May.
    10. Haitao Li & Martin T. Wells & Cindy L. Yu, 2008. "A Bayesian Analysis of Return Dynamics with Lévy Jumps," Review of Financial Studies, Society for Financial Studies, vol. 21(5), pages 2345-2378, September.
    11. Michael S. Johannes & Nicholas G. Polson & Jonathan R. Stroud, 2009. "Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 22(7), pages 2559-2599, July.
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    15. Cox, John C & Ingersoll, Jonathan E, Jr & Ross, Stephen A, 1985. "A Theory of the Term Structure of Interest Rates," Econometrica, Econometric Society, vol. 53(2), pages 385-407, March.
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