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Sigma Point Filters For Dynamic Nonlinear Regime Switching Models

Binning, Andrew; Maih, Junior
Working paper
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URI
http://hdl.handle.net/11250/2364625
Date
2015
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  • Centre for Applied Macro- and Petroleum economics (CAMP) [104]
Abstract
In this paper we take three well known Sigma Point Filters, namely the Unscented Kalman

Filter, the Divided Difference Filter, and the Cubature Kalman Filter, and extend them to

allow for a very general class of dynamic nonlinear regime switching models. Using both

a Monte Carlo study and real data, we investigate the properties of our proposed filters

by using a regime switching DSGE model solved using nonlinear methods. We find that

the proposed filters perform well. They are both fast and reasonably accurate, and as

a result they will provide practitioners with a convenient alternative to Sequential Monte

Carlo methods. We also investigate the concept of observability and its implications in the

context of the nonlinear filters developed and propose some heuristics. Finally, we provide in the RISE toolbox, the codes implementing these three novel filters.
Publisher
BI Norwegian Business School
Series
CAMP Working Papers Series;4/2015

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