TY - JOUR
AU - Cumby,Robert E.
AU - Huizinga,John
TI - Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions
JF - National Bureau of Economic Research Technical Working Paper Series
VL - No. 92
PY - 1990
Y2 - October 1990
DO - 10.3386/t0092
UR - http://www.nber.org/papers/t0092
L1 - http://www.nber.org/papers/t0092.pdf
N1 - Author contact info:
Robert E. Cumby
Georgetown University
School of Foreign Service
Washington, DC 20057-1045
Tel: 202/687-2990
Fax: 202/687-6102
E-Mail: cumbyr@georgetown.edu
John Huizinga
Graduate School of Business
The University of Chicago
1101 East 58th Street
Chicago, IL 60637
Tel: 773-702-7272; john.huizinga@gsb.uchicago.edu
E-Mail: john.huizinga@ChicagoBooth.edu
AB - 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.
ER -