Jump-detection-based estimation in time-varying coefficient models and empirical applications

Jump-detection-based estimation in time-varying coefficient models and empirical applications Time-varying coefficient models are very important tools to explore the hidden structure between the response variable and its predictors. In some applications, the coefficient curves have singularities, including jump points at some unknown positions, representing structural changes of the related processes. Detection of such singularities is important for understanding the structural changes. In this paper, an alternative jump-detection procedure is proposed based on the first-order and second-order derivatives of the coefficient curves. Based on the detected jump points, a coefficient curve estimation procedure is also proposed, which can preserve the jump structure well when the noise level is small. Further, the implementation of turning parameters is discussed. Under some mild conditions, the asymptotic properties of the proposed estimators are established not only in the continuous regions of coefficient functions, but also in the neighborhoods of the jump points. Finally, we demonstrate, using both simulation and empirical examples, that the proposed methodologies perform well. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png TEST Springer Journals

Jump-detection-based estimation in time-varying coefficient models and empirical applications

Loading next page...
 
/lp/springer_journal/jump-detection-based-estimation-in-time-varying-coefficient-models-and-Ep5F0quy5j
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Sociedad de Estadística e Investigación Operativa
Subject
Statistics; Statistics, general; Statistical Theory and Methods
ISSN
1133-0686
eISSN
1863-8260
D.O.I.
10.1007/s11749-017-0525-7
Publisher site
See Article on Publisher Site

Abstract

Time-varying coefficient models are very important tools to explore the hidden structure between the response variable and its predictors. In some applications, the coefficient curves have singularities, including jump points at some unknown positions, representing structural changes of the related processes. Detection of such singularities is important for understanding the structural changes. In this paper, an alternative jump-detection procedure is proposed based on the first-order and second-order derivatives of the coefficient curves. Based on the detected jump points, a coefficient curve estimation procedure is also proposed, which can preserve the jump structure well when the noise level is small. Further, the implementation of turning parameters is discussed. Under some mild conditions, the asymptotic properties of the proposed estimators are established not only in the continuous regions of coefficient functions, but also in the neighborhoods of the jump points. Finally, we demonstrate, using both simulation and empirical examples, that the proposed methodologies perform well.

Journal

TESTSpringer Journals

Published: Feb 10, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off