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Using the dglars Package to Estimate a Sparse Generalized Linear Model Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Robust Estimation of Regime Switching Models

Authors : Luigi Grossi, Fany Nan

Published in: Advances in Statistical Models for Data Analysis

Publisher: Springer International Publishing

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Abstract

It is well known that generalized-M (GM) estimators for linear models are consistent and lead to a small loss of efficiency with respect to least squares (LS) estimator. When they are extended to threshold models the consistency of GM estimators is guaranteed only under certain objective functions. In this paper we explore, in a simulation experiment, the loss of consistency of GM-SETAR estimator under different objective functions, time-series length, parameter combinations and type of contaminations. Finally the best robust estimator is applied to study the dynamic of electricity prices where regime switching and high spikes are widely observed features.

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Footnotes
1
For an overview about GM estimators see [1, Chap. 4] and [12, Chap. 8].
 
2
Different starting values have been chosen deliberately to keep the first method as it was originally suggested by Chan and Cheung [3].
 
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Metadata
Title
Robust Estimation of Regime Switching Models
Authors
Luigi Grossi
Fany Nan
Copyright Year
2015
Book
Advances in Statistical Models for Data Analysis

Print ISBN: 978-3-319-17376-4

Electronic ISBN: 978-3-319-17377-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-17377-1_14

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