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Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models

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  • Flury, Thomas
  • Shephard, Neil

Abstract

Suppose we wish to carry out likelihood based inference but we solely have an unbiased simulation based estimator of the likelihood. We note that unbiasedness is enough when the estimated likelihood is used inside a Metropolis-Hastings algorithm. This result has recently been intro- duced in statistics literature by Andrieu, Doucet, and Holenstein (2007) and is perhaps surprising given the celebrated results on maximum simulated likelihood estimation. Bayesian inference based on simulated likelihood can be widely applied in microeconomics, macroeconomics and financial econometrics. One way of generating unbiased estimates of the likelihood is by the use of a particle filter. We illustrate these methods on four problems in econometrics, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model we can carry out likelihood based inference using its simulations.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 27 (2011)
Issue (Month): 05 (October)
Pages: 933-956

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Handle: RePEc:cup:etheor:v:27:y:2011:i:05:p:933-956_00

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Citations

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Cited by:
  1. Martin M. Andreasen, 2010. "Non-linear DSGE Models and The Optimized Particle Filter," CREATES Research Papers 2010-05, School of Economics and Management, University of Aarhus.
  2. 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.
  3. Neil Shephard & Arnaud Doucet, 2012. "Robust inference on parameters via particle filters and sandwich covariance matrices," Economics Series Working Papers 606, University of Oxford, Department of Economics.
  4. Nima Nonejad, 2013. "Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox," CREATES Research Papers 2013-27, School of Economics and Management, University of Aarhus.
  5. BAUWENS, Luc & DUFAYS, Arnaud & ROMBOUTS, Jeroen V.K., 2011. "Marginal likelihood for Markov-switching and change-point GARCH models," CORE Discussion Papers 2011013, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. Chan, Joshua & Strachan, Rodney, 2012. "Estimation in Non-Linear Non-Gaussian State Space Models with Precision-Based Methods," MPRA Paper 39360, University Library of Munich, Germany.
  7. Angelo Marsiglia Fasolo, 2012. "A Note on Particle Filters Applied to DSGE Models," Working Papers Series 281, Central Bank of Brazil, Research Department.
  8. Rhys Bidder & Matthew E. Smith, 2013. "Doubts and variability: a robust perspective on exotic consumption series," Working Paper Series 2013-28, Federal Reserve Bank of San Francisco.
  9. 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.
  10. Neil Shephard, 2013. "Martingale unobserved component models," Economics Series Working Papers 644, University of Oxford, Department of Economics.
  11. Ron Gallant & Raffaella Giacomini & Giuseppe Ragusa, 2013. "Generalized method of moments with latent variables," CeMMAP working papers CWP50/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  12. Celik, Nurcin & Son, Young-Jun, 2011. "State estimation of a shop floor using improved resampling rules for particle filtering," International Journal of Production Economics, Elsevier, vol. 134(1), pages 224-237, November.

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