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This result has recently been introduced in statistics literature by Andrieu, Doucet, and Holenstein (2010,<jats:italic>Journal of the Royal Statistical Society, Series B<\/jats:italic>, 72, 269\u2013342) and is perhaps surprising given the 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 through a particle filter. We illustrate these methods on four problems, producing rather generic methods. Taken together, these methods imply that if we can simulate from an economic model, we can carry out likelihood\u2013based inference using its simulations.<\/jats:p>","DOI":"10.1017\/s0266466610000599","type":"journal-article","created":{"date-parts":[[2011,5,17]],"date-time":"2011-05-17T08:45:28Z","timestamp":1305621928000},"page":"933-956","source":"Crossref","is-referenced-by-count":103,"title":["BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS"],"prefix":"10.1017","volume":"27","author":[{"given":"Thomas","family":"Flury","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neil","family":"Shephard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2011,5,17]]},"reference":[{"key":"S0266466610000599_ref16","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2006.00650.x"},{"key":"S0266466610000599_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10618600.1996.10474692","article-title":"Monte Carlo filter and smoother for non-Gaussian nonlinear state space models","volume":"5","author":"Kitagawa","year":"1996","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"S0266466610000599_ref29","volume-title":"Numerical Methods in Economics","author":"Judd","year":"1998"},{"key":"S0266466610000599_ref9","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/85.2.347"},{"key":"S0266466610000599_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4684-9393-1"},{"key":"S0266466610000599_ref36","unstructured":"Pitt M. 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