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Robust inference on parameters via particle filters and sandwich covariance matrices

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  • Neil Shephard
  • Arnaud Doucet

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.economics.ox.ac.uk/materials/papers/12037/paper606.pdf
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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 606.

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Date of creation: 01 Jun 2012
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Handle: RePEc:oxf:wpaper:606

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Keywords: Quasi-likelihood; Particle filter; Sandwich matrix; Sequential Monte Carlo;

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  1. Sungbae An & Frank Schorfheide, 2006. "Bayesian analysis of DSGE models," Working Papers 06-5, Federal Reserve Bank of Philadelphia.
  2. Jesus Fernandez-Villaverde & Juan Rubio & Manuel Santos, 2005. "Convergence Properties of the Likelihood of Computed Dynamic Models," NBER Technical Working Papers 0315, National Bureau of Economic Research, Inc.
  3. 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.
  4. 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.
  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. Juan F. Rubio-Ramirez & Jesus Fernández-Villaverde, 2005. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 891-910.
  7. Jason R. Blevins, 2011. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Working Papers 11-01, Ohio State University, Department of Economics.
  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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