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Through Monte Carlo experiments, it is shown that (1) for a small number of random draws (or nodes) our simulation\u2010based density estimator using Monte Carlo integration (SDE) performs better than Kitagawa's numerical integration procedure (KNI), and (2) SDE and KNI give less biased parameter estimates than the extended Kalman filter (EKF). Finally, an estimation of per capita final consumption data is taken as an application to the non\u2010linear filtering problem.<\/jats:p>","DOI":"10.1002\/jae.3950090204","type":"journal-article","created":{"date-parts":[[2007,7,12]],"date-time":"2007-07-12T17:20:01Z","timestamp":1184260801000},"page":"163-179","source":"Crossref","is-referenced-by-count":30,"title":["Prediction, filtering and smoothing in non\u2010linear and non\u2010normal cases using Monte Carlo integration"],"prefix":"10.1002","volume":"9","author":[{"given":"H.","family":"Tanizaki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. S.","family":"Mariano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2006,11,7]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.1972.1100034"},{"key":"e_1_2_1_3_1","volume-title":"Optimal Filtering","author":"Anderson B. D. O.","year":"1979"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1992.10475231"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-1889(79)80017-2"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/apmc.1988.051620106"},{"key":"e_1_2_1_7_1","volume-title":"Economic Complexity: Chaos, Sunspots, Bubbles, and Non\u2010linearity","author":"Geweke J.","year":"1989"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.2307\/1913710"},{"key":"e_1_2_1_9_1","volume-title":"Forecasting, Structural Time Series Models and the Kalman Filter","author":"Harvey A. 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