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Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions

38 Pages Posted: 27 Jun 2007  

Robert E. Cumby

Georgetown University - Department of Economics; National Bureau of Economic Research (NBER)

John P. Huizinga

University of Chicago - Booth School of Business

Date Written: October 1990

Abstract

This paper derives the asymptotic distribution for a vector of sample autocorrelations of regression residuals from a quite general linear model. The asymptotic distribution forms the basis for a test of the null hypothesis that the regression error follows a moving average of order q [greaterthan or equal] 0 against the general alternative that autocorrelations of the regression error are non-zero at lags greater than q. By allowing for endogenous, predetermined and/or exogenous regressors, for estimation by either ordinary least squares or a number of instrumental variables techniques, for the case q>0, and for a conditionally heteroscedastic error term, the test described here is applicable in a variety of situations where such popular tests as the Box-Pierce (1970) test, Durbin's (1970) h test, and Godfrey's (1978b) Lagrange multiplier test are net applicable. The finite sample properties of the test are examined in Monte Carlo simulations where, with a sample sizes of 50 and 100 observations, the test appears to be quite reliable.

Suggested Citation

Cumby, Robert E. and Huizinga, John P., Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions (October 1990). NBER Working Paper No. t0092. Available at SSRN: https://ssrn.com/abstract=994521

Robert E. Cumby (Contact Author)

Georgetown University - Department of Economics ( email )

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National Bureau of Economic Research (NBER)

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John Huizinga

University of Chicago - Booth School of Business ( email )

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