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Robust inference on parameters via particle filters and sandwich covariance matrices

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  • Arnaud Doucet

    ()
    (Department of Statistics and Oxford-Man Institute, University of Oxford)

  • Neil Shephard

    ()
    (Nuffield College, Dept of Economics and Oxford-Man Institute of Quantitative Finance, University of Oxford.)

Abstract

Likelihood based estimation of the parameters of state space models can be carried out via a particle filter. In this paper we show how to make valid inference on such parameters when the model is incorrect. In particular we develop a simulation strategy for computing sandwich covariance matrices which can be used for asymptotic likelihood based inference. These methods are illustrated on some simulated data.

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File URL: http://www.nuffield.ox.ac.uk/economics/papers/2012/doucet120601.pdf
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Bibliographic Info

Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2012-W05.

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Length: 23 pages
Date of creation: 01 Jun 2012
Date of revision:
Handle: RePEc:nuf:econwp:1205

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Web page: http://www.nuff.ox.ac.uk/economics/

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Keywords: quasi-likelihood; particle filter; sandwich matrix; sequential Monte Carlo.;

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  1. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
  2. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez & Manuel Santos, 2004. "Convergence Properties of the Likelihood of Computed Dynamic Models," PIER Working Paper Archive 04-034, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  3. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
  4. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  5. Creal, D., 2009. "A survey of sequential Monte Carlo methods for economics and finance," Serie Research Memoranda 0018, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  6. Jason R. Blevins, 2011. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Working Papers 11-01, Ohio State University, Department of Economics.
  7. Neil Shephard & Thomas Flury, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," Economics Series Working Papers 413, University of Oxford, Department of Economics.
  8. Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez, 2004. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Working Paper 2004-3, Federal Reserve Bank of Atlanta.
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