Testing For Common Features
AbstractThis paper introduces a class of statistical tests for the hypothesis that some feature of a data set is common to several variables. A feature is detected in a single series by a hypothesis test where the null is that it is absent, and the alternative is that it is present. Examples are serial correlation, trends, seasonality, heteroskedasticity, ARCH, excess kurtosis and many others. A feature is common to a multivariate data set if a linear combination of the series no longer has the feature. A test for common features can be based on the minimized value of the feature test over all linear combinations of the data. A bound on the distribution for such a test is developed in the paper. For many important cases, an exact asymptotic critical value can be obtained which is simply a test of overidentifying restrictions in an instrumental variable regression.
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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0091.
Date of creation: Oct 1990
Date of revision:
Publication status: published as Journal of Business & Economic Statistics, 1993, vol. 11, issue 4, pages 369-80
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Postal: National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.
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Other versions of this item:
- Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 11(4), pages 369-80, October.
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- Engle, Robert F & Granger, Clive W J, 1987. "Co-integration and Error Correction: Representation, Estimation, and Testing," Econometrica, Econometric Society, Econometric Society, vol. 55(2), pages 251-76, March.
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