Information matrix test parameter heterogeneity and arch a synthesis

Anil K. Bera; Sangkyu Lee

doi:10.2307/2297820

Information matrix test parameter heterogeneity and arch a synthesis

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Abstract

We apply the White information matrix (1M) test to the linear regression model with auto correlated errors. A special case of one component of the test is found to be identical to the Engle Lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH). Given Cheshers interpretation of the 1Mtest as a test for parameter heterogeneity, this establishes a connection among the 1Mtest, ARCH and parameter variation. This also enables us to specify conditional heteroskedasticity in a more general and convenient way. Other interesting by products of our analysis are tests for the variation in conditional and static skewness which we call tests for "heterocliticity".

Original language	English (US)
Pages (from-to)	229-240
Number of pages	12
Journal	Review of Economic Studies
Volume	60
Issue number	1
DOIs	https://doi.org/10.2307/2297820
State	Published - Jan 1993

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Economics and Econometrics

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10.2307/2297820

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title = "Information matrix test parameter heterogeneity and arch a synthesis",

abstract = "We apply the White information matrix (1M) test to the linear regression model with auto correlated errors. A special case of one component of the test is found to be identical to the Engle Lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH). Given Cheshers interpretation of the 1Mtest as a test for parameter heterogeneity, this establishes a connection among the 1Mtest, ARCH and parameter variation. This also enables us to specify conditional heteroskedasticity in a more general and convenient way. Other interesting by products of our analysis are tests for the variation in conditional and static skewness which we call tests for {"}heterocliticity{"}.",

author = "Bera, {Anil K.} and Sangkyu Lee",

note = "Acknowledgement. We are grateful to two referees for their very useful comments. Both of them pointed out some errors in our earlier derivation and made many helpful suggestions which improved the exposition of the paper. We also wish to express our appreciation to the participants of the 1988 North American Econometric Society Summer Meeting, Rob Engle, Bruce Hansen, Hal White, Jan Kmenta, Pravin Trivedi, Xiao-Lei Zuo and, in particular, Alastair Hall for constructive comments on an earlier draft of the paper. All errors, of course, remain our own. Financial support from the Research Board and the Bureau of Economic and Business Research of the University of Illinois is gratefully acknowledged.",

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N1 - Acknowledgement. We are grateful to two referees for their very useful comments. Both of them pointed out some errors in our earlier derivation and made many helpful suggestions which improved the exposition of the paper. We also wish to express our appreciation to the participants of the 1988 North American Econometric Society Summer Meeting, Rob Engle, Bruce Hansen, Hal White, Jan Kmenta, Pravin Trivedi, Xiao-Lei Zuo and, in particular, Alastair Hall for constructive comments on an earlier draft of the paper. All errors, of course, remain our own. Financial support from the Research Board and the Bureau of Economic and Business Research of the University of Illinois is gratefully acknowledged.

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N2 - We apply the White information matrix (1M) test to the linear regression model with auto correlated errors. A special case of one component of the test is found to be identical to the Engle Lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH). Given Cheshers interpretation of the 1Mtest as a test for parameter heterogeneity, this establishes a connection among the 1Mtest, ARCH and parameter variation. This also enables us to specify conditional heteroskedasticity in a more general and convenient way. Other interesting by products of our analysis are tests for the variation in conditional and static skewness which we call tests for "heterocliticity".

AB - We apply the White information matrix (1M) test to the linear regression model with auto correlated errors. A special case of one component of the test is found to be identical to the Engle Lagrange multiplier (LM) test for autoregressive conditional heteroskedasticity (ARCH). Given Cheshers interpretation of the 1Mtest as a test for parameter heterogeneity, this establishes a connection among the 1Mtest, ARCH and parameter variation. This also enables us to specify conditional heteroskedasticity in a more general and convenient way. Other interesting by products of our analysis are tests for the variation in conditional and static skewness which we call tests for "heterocliticity".

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Information matrix test parameter heterogeneity and arch a synthesis

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