全球智库信息集成服务系统(G2TT): Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/t0092
来源ID	Technical Working Paper 0092
	Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions
	Robert E. Cumby; John Huizinga
发表日期	1990-10-01
出版年	1990
语种	英语
摘要	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.
主题	Econometrics
URL	https://www.nber.org/papers/t0092
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/560750
推荐引用方式 GB/T 7714	Robert E. Cumby,John Huizinga. Testing The Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions. 1990.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
t0092.pdf（2015KB）	智库出版物		限制开放	CC BY-NC-SA	浏览