Journal ArticleDOI
Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables
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In this paper, it is shown that the asymptotic distribution of the serial correlation coefficient calculated from the least-squares residuals differs from that of the true disturbances in a regression model where some of the regressors are lagged dependent variables.Abstract:
The construction of tests of model specification is considered from a general point of view. The results are applied to testing the serial independence of the disturbances in a regression model where some of the regressors are lagged dependent variables. It is shown that the asymptotic distribution of the lag-1 serial correlation coefficient calculated from the least-squares residuals differs from that of the coefficient calculated from the true disturbances. A consequence of this is that tests of serial independence based on the residuals from regression on fixed regressors are invalid when applied to models containing lagged dependent variables even when the null hypothesis of serial independence is true. Tests which are asymptotically valid for the large-sample case are suggested.read more
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References
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Book
Testing statistical hypotheses
TL;DR: The general decision problem, the Probability Background, Uniformly Most Powerful Tests, Unbiasedness, Theory and First Applications, and UNbiasedness: Applications to Normal Distributions, Invariance, Linear Hypotheses as discussed by the authors.
Journal ArticleDOI
On the Statistical Treatment of Linear Stochastic Difference Equations
Henry B. Mann,Abraham Wald +1 more
Journal ArticleDOI
Use of the durbin-watson statistic in inappropriate situations
Marc Nerlove,Kenneth F. Wallis +1 more
TL;DR: In this paper, Malinvaud showed that the Durbin-watson statistic is asymptotically biased towards 2 (the value which it should have if no serial correlation is in fact present).
Distribution of residual autocorrelations in integrated autoregressive-moving average time series models.
George E. P. Box,David A. Pierce +1 more
TL;DR: It is shown that to a close approximation the residuals from any moving average or mixed autoregressive - moving average process will be the same as those from a suitably chosen autore progressive process.
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