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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. |
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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 |
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