Robustness of nonlinearity and chaos tests to measurement error, inference method, and sample size

William A. Barnett; A. Ronald Gallant; Melvin J. Hinich; Jochen A. Jungeilges; Daniel T. Kaplan; Mark J. Jensen

doi:10.1016/0167-2681(94)00082-P

Robustness of nonlinearity and chaos tests to measurement error, inference method, and sample size

William A. Barnett, A. Ronald Gallant, Melvin J. Hinich, Jochen A. Jungeilges, Daniel T. Kaplan, Mark J. Jensen

Economics

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

55 Scopus citations

Abstract

Interest has been growing in testing for nonlinearity and chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We apply five tests for nonlinearity or chaos to various monetary aggregate data series. We find that the inferences vary across tests for the same data, and within tests for varying sample sizes and various methods of aggregation of the data. Robustness of inferences in this area of research seems to be low and may account for the controversies surrounding empirical claims of nonlinearity and chaos in economics.

Original language	English (US)
Pages (from-to)	301-320
Number of pages	20
Journal	Journal of Economic Behavior and Organization
Volume	27
Issue number	2
DOIs	https://doi.org/10.1016/0167-2681(94)00082-P
State	Published - Jul 1995

All Science Journal Classification (ASJC) codes

Economics and Econometrics
Organizational Behavior and Human Resource Management

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10.1016/0167-2681(94)00082-P

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title = "Robustness of nonlinearity and chaos tests to measurement error, inference method, and sample size",

abstract = "Interest has been growing in testing for nonlinearity and chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We apply five tests for nonlinearity or chaos to various monetary aggregate data series. We find that the inferences vary across tests for the same data, and within tests for varying sample sizes and various methods of aggregation of the data. Robustness of inferences in this area of research seems to be low and may account for the controversies surrounding empirical claims of nonlinearity and chaos in economics.",

author = "Barnett, {William A.} and {Ronald Gallant}, A. and Hinich, {Melvin J.} and Jungeilges, {Jochen A.} and Kaplan, {Daniel T.} and Jensen, {Mark J.}",

note = "Funding Information: * This research was partially supported by the National Science Foundation in the U.S. (Barnett, Gallant), the Offke of Naval Research in the U.S. (Hinich), The Medical Research Council in Canada (Kaplan), and the German Science Foundation in Germany (Jungeilges). Mark Jensen was employed as a research assistant under Barnett{\textquoteright}s NSF grant. l Corresponding author",

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AU - Barnett, William A.

AU - Ronald Gallant, A.

AU - Hinich, Melvin J.

AU - Jungeilges, Jochen A.

AU - Kaplan, Daniel T.

AU - Jensen, Mark J.

N1 - Funding Information: * This research was partially supported by the National Science Foundation in the U.S. (Barnett, Gallant), the Offke of Naval Research in the U.S. (Hinich), The Medical Research Council in Canada (Kaplan), and the German Science Foundation in Germany (Jungeilges). Mark Jensen was employed as a research assistant under Barnett’s NSF grant. l Corresponding author

PY - 1995/7

Y1 - 1995/7

N2 - Interest has been growing in testing for nonlinearity and chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We apply five tests for nonlinearity or chaos to various monetary aggregate data series. We find that the inferences vary across tests for the same data, and within tests for varying sample sizes and various methods of aggregation of the data. Robustness of inferences in this area of research seems to be low and may account for the controversies surrounding empirical claims of nonlinearity and chaos in economics.

AB - Interest has been growing in testing for nonlinearity and chaos in economic data, but much controversy has arisen about the available results. This paper explores the reasons for these empirical difficulties. We apply five tests for nonlinearity or chaos to various monetary aggregate data series. We find that the inferences vary across tests for the same data, and within tests for varying sample sizes and various methods of aggregation of the data. Robustness of inferences in this area of research seems to be low and may account for the controversies surrounding empirical claims of nonlinearity and chaos in economics.

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Robustness of nonlinearity and chaos tests to measurement error, inference method, and sample size

Abstract

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