Nonlinear transfer functions

Rong Chen; Ruey S. Tsay

doi:10.1080/10485259608832671

Nonlinear transfer functions

Rong Chen
, Ruey S. Tsay

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

This paper is concerned with nonlinear transfer function analysis. We propose a test statistic to detect nonlinearity of a transfer function. Simulation study shows that the test works reasonably well in finite samples for the nonlinear models commonly used in the literature. We then discuss nonlinear transfer function modeling and the contribution of nonlinear components in a given model. For illustration, we apply the test to several data sets and use a real example to show that substantial gain may result from using simple nonlinear transfer functions.

Original language	English (US)
Pages (from-to)	193-204
Number of pages	12
Journal	Journal of Nonparametric Statistics
Volume	6
Issue number	2-3
DOIs	https://doi.org/10.1080/10485259608832671
State	Published - 1996
Externally published	Yes

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

Keywords

Likelihood ratio
Nonlinearity
Riverflow
Time series

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10.1080/10485259608832671

Cite this

@article{e798a8d51a7f48b78ae845be38d53be1,

title = "Nonlinear transfer functions",

abstract = "This paper is concerned with nonlinear transfer function analysis. We propose a test statistic to detect nonlinearity of a transfer function. Simulation study shows that the test works reasonably well in finite samples for the nonlinear models commonly used in the literature. We then discuss nonlinear transfer function modeling and the contribution of nonlinear components in a given model. For illustration, we apply the test to several data sets and use a real example to show that substantial gain may result from using simple nonlinear transfer functions.",

keywords = "Likelihood ratio, Nonlinearity, Riverflow, Time series",

author = "Rong Chen and Tsay, \{Ruey S.\}",

note = "Funding Information: Rong Chen's research is supported in part by the National Science Foundation grant DMS-9301193 and the Department of Statistics, Texas A \& M University. Ruey S. Tsay's research is support in part by the National Science Foundation grant DMS-9305045 and the Graduate School of Business, University of Chicago. The authors thank Professor T. Ozaki for providing the :-i:,erflow data and a referee for helpful comments.",

year = "1996",

doi = "10.1080/10485259608832671",

language = "English (US)",

volume = "6",

pages = "193--204",

journal = "Journal of Nonparametric Statistics",

issn = "1048-5252",

publisher = "Taylor and Francis Ltd.",

number = "2-3",

}

TY - JOUR

T1 - Nonlinear transfer functions

AU - Chen, Rong

AU - Tsay, Ruey S.

N1 - Funding Information: Rong Chen's research is supported in part by the National Science Foundation grant DMS-9301193 and the Department of Statistics, Texas A & M University. Ruey S. Tsay's research is support in part by the National Science Foundation grant DMS-9305045 and the Graduate School of Business, University of Chicago. The authors thank Professor T. Ozaki for providing the :-i:,erflow data and a referee for helpful comments.

PY - 1996

Y1 - 1996

N2 - This paper is concerned with nonlinear transfer function analysis. We propose a test statistic to detect nonlinearity of a transfer function. Simulation study shows that the test works reasonably well in finite samples for the nonlinear models commonly used in the literature. We then discuss nonlinear transfer function modeling and the contribution of nonlinear components in a given model. For illustration, we apply the test to several data sets and use a real example to show that substantial gain may result from using simple nonlinear transfer functions.

AB - This paper is concerned with nonlinear transfer function analysis. We propose a test statistic to detect nonlinearity of a transfer function. Simulation study shows that the test works reasonably well in finite samples for the nonlinear models commonly used in the literature. We then discuss nonlinear transfer function modeling and the contribution of nonlinear components in a given model. For illustration, we apply the test to several data sets and use a real example to show that substantial gain may result from using simple nonlinear transfer functions.

KW - Likelihood ratio

KW - Nonlinearity

KW - Riverflow

KW - Time series

UR - https://www.scopus.com/pages/publications/3042918771

UR - https://www.scopus.com/pages/publications/3042918771#tab=citedBy

U2 - 10.1080/10485259608832671

DO - 10.1080/10485259608832671

M3 - Article

AN - SCOPUS:3042918771

SN - 1048-5252

VL - 6

SP - 193

EP - 204

JO - Journal of Nonparametric Statistics

JF - Journal of Nonparametric Statistics

IS - 2-3

ER -

Nonlinear transfer functions

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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