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John Fitts (1973)
Testing for autocorrelation in the autoregressive moving average error modelJournal of Econometrics, 1
Durbin Durbin (1970)
Testing for serial correlation in least‐squares regression when some of the regressors are lagged dependent variablesEconometrica, 38
Author Silvey (1959)
The Lagrangian Multiplier TestAnnals of Mathematical Statistics, 30
P. Schmidt (1972)
A GENERALIZATION OF THE DURBIN-WATSON TEST*, 11
Box Box, Pierce Pierce (1970)
Distribution of residual autocorrelations in autoregressive‐integrated moving average time series modelsJournal of the American Statistical Association, 65
Wallis Wallis (1972)
Testing for fourth order autocorrelation in quarterly regression equationsEconometrica, 40
Durbin Durbin, Watson Watson (1950)
Testing for serial correlation in least‐squares regression, I and IIBiometrika., 37
Aitchison Aitchison, Silvey Silvey (1960)
Maximum‐likelihood estimation procedures and associated tests of significanceJournal of the Royal Statistical Society (B), 22
J. Aitchison, S. Silvey (1958)
Maximum-Likelihood Estimation of Parameters Subject to RestraintsAnnals of Mathematical Statistics, 29
M. Hatanaka (1974)
An efficient two-step estimator for the dynamic adjustment model with autoregressive errorsJournal of Econometrics, 2
If the disturbances of a linear model are autocorrelated, ordinary .>ast squares ( LS) estimates of the coefficient parameters are inefficient but unbiased. However, in a dynamic equation where lagged values of the dependent variable appear as regressors, least squares estimates are biased and generally inconsistent. For this reason it is important to have available tests against autocorrelation, particularly when it is a dynamic model which is proposed to be estimated by OLS. But the standard tests based on OLS residuals, notably that of Durbin and Watson [4] and the extensions by Schmidt [ 101 and Wallis [ 121 , are invalid when some of the regressors are lagged values of the dependent variable. These tests are attractive because they avoid the obvious approach of estimating the model with the disturbance process incorporated explicitly and making inference from estimates of parameters in the disturbance process. This latter approach is available in the case of dynamic models but is not widely practised because of the relatively heavy computational requirement to obtain maximum likelihood (ML) estimates of the parameters. Thus the importance of the seminal contribution of Durbin [S] who showed that it was possible to construct (asymptotically) valid tests
Australian Economic Papers – Wiley
Published: Dec 1, 1978
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