Titlebook: Nonlinear Filters; Estimation and Appli Hisashi Tanizaki Book 19931st edition Springer-Verlag Berlin Heidelberg 1993 Kalman Filter.Nichtlin

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Lecture Notes in Economics and Mathematical Systemshttp://image.papertrans.cn/n/image/667500.jpg

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An Application of Nonlinear Filters: Estimation of Permanent Consumption,mic research has been devoted to various aspects of the life cycle permanent income hypothesis under rational expectations. See Hall (1978), Hall and Mishkin (1982), Mankiw and Shapiro (1985), Campbell (1987), Campbell and Mankiw (1987), and West (1988). For the most concise and useful survey, see Diebold and Nerlove (1989).

Anal-Canal

Nonlinear Filters Based on Density Approximation,wn that Kaiman filter models based on normality assumption are non-robust (see Meinhold and Singpurwalla (1989)). Therefore, density approximation, rather than function approximation, has to be investigated in order to obtain less biased filtering estimates of the state-variables.

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Book 19931st editionrecursive Kalman filteralgorithm directly to the linearized nonlinear measurementand transition equations. First, it is discussed that theTaylor series expansion approach gives us thebiasedestimators. Next, a Monte-Carlo simulation filter isproposed, where each expectation of the nonlinear functions

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0075-8442 al linear recursive Kalman filteralgorithm directly to the linearized nonlinear measurementand transition equations. First, it is discussed that theTaylor series expansion approach gives us thebiasedestimators. Next, a Monte-Carlo simulation filter isproposed, where each expectation of the nonlinear

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Comparison of Nonlinear Filters: Monte-Carlo Experiments,r functions. One set of data y. and α. for t=l,...,T is artificially simulated and, given y., each filtering estimate of α. is compared with the artificially simulated α.. This procedure is performed 1000 times (i.e., 1000 sets of data are generated) and BIAS and RMSE between the estimated α. and the simulated one are computed for each time t.