02100cam a22002417 4500001000600000003000500006005001700011008004100028100002100069245018700090260006600277490005100343500001800394520105600412530006101468538007201529538003601601700002001637710004201657830008601699856003701785856003601822t0092NBER20180521223247.0180521s1990 mau||||fs|||| 000 0 eng d1 aCumby, Robert E.10aTesting The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressionsh[electronic resource] /cRobert E. Cumby, John Huizinga. aCambridge, Mass.bNational Bureau of Economic Researchc1990.1 aNBER technical working paper seriesvno. t0092 aOctober 1990.3 aThis 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. aHardcopy version available to institutional subscribers. aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web.1 aHuizinga, John.2 aNational Bureau of Economic Research. 0aTechnical Working Paper Series (National Bureau of Economic Research)vno. t0092.4 uhttp://www.nber.org/papers/t009241uhttp://dx.doi.org/10.3386/t0092