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Robust inference on parameters via particle filters and sandwich covariance matrices

  • 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.)

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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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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  1. Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez & Manuel S. Santos, 2004. "Convergence properties of the likelihood of computed dynamic models," FRB Atlanta Working Paper No. 2004-27, Federal Reserve Bank of Atlanta.
  2. Jesús Fernández-Villaverde & Juan Francisco Rubio-Ramírez, 2004. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," FRB Atlanta Working Paper No. 2004-3, Federal Reserve Bank of Atlanta.
  3. 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.
  4. Sungbae An & Frank Schorfheide, 2006. "Bayesian analysis of DSGE models," Working Papers 06-5, Federal Reserve Bank of Philadelphia.
  5. Kim, Sangjoon & Shephard, Neil & Chib, Siddhartha, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Wiley Blackwell, vol. 65(3), pages 361-93, July.
  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. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(05), pages 933-956, October.
  8. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
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