On a New Test for Autocorrelation in Least Squares Regression

Abrahamse, A. P. J.; Louter, A. S.

doi:10.2307/2334316

Cited by 5 publications

(10 citation statements)

References 4 publications

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“…Finally, in Section 6, the results of a number of power calculations for five tests, based on this design, are presented. The results appear to affirm the conclusions mentioned in Abrahamse and Louter (1971) and, accordingly, the conclusions drawn by Esperance and Taylor should be revised radically.…”

Section: Introductionsupporting

confidence: 79%

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On typical characteristics of economic time series and the relative qualities of five autocorrelation tests

Dubbelman

Louter

Abrahamse³

1978

Journal of Econometrics

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Section: Introductionsupporting

confidence: 79%

“…For some examples, the powers of an autocorrelation test based on the new estimator have been given in Abrahamse and Louter (1971), and they were ,compared with the corresponding powers of some customary test procedures.…”

Section: Introductionmentioning

confidence: 99%

On typical characteristics of economic time series and the relative qualities of five autocorrelation tests

Dubbelman

Louter

Abrahamse³

1978

Journal of Econometrics

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“…The X matrices used are those comprised of the eigenvectors associated with the fe smallest eigenvalues of the n x n first-differencing matrix, A v Empirical evidence presented by Dubbelman [4] and Dubbelman, Louter, and Abrahamse [5] suggests that in economic time series regression analysis, it is often likely that the space spanned by such eigenvectors is a good approximation to the column space of the X matrix. A similar view has been expressed by Theil and Nagar [20], Hannan and Terrell [10], Abrahamse and Koerts [1], and Abrahamse and Louter [2]. However, it is worth noting that the choice of a "representative" X matrix may not be particularly crucial.…”

Section: The Choice Of J Tmentioning

confidence: 54%

A Point Optimal Test for Moving Average Regression Disturbances

King

1985

Econom. Theory

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This paper reconsiders King's [12] locally optimal test procedure for first-order moving average disturbances in the linear regression model. It recommends two tests, one for problems involving positively correlated disturbances and one for negatively correlated disturbances. Both tests are most powerful invariant at a point in the alternative hypothesis parameter space that is determined by a function involving the sample size and the number of regressors. Selected bounds for the tests' significance points are tabulated and an empirical comparison of powers demonstrates the overall superiority of the new test for positively correlated moving average disturbances.

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“…Our choice of representative X matrices are those comprised of the eigenvectors associated with the k smallest eigenvalues of the n x n matrix Dubbelman (1972) and Dubbelman, Louter and Abrahamse (1978) present empirical evidence which suggests that in economic time-series regression analysis, it is often likely that the space spanned by such eigenvectors is a good approximation to the column space of the X matrix. Similar views have been expressed by Theil and Nagar (1961), Hannan and Terrell (1968), Abrahamse and Koerts (1971) and Abrahamse and Louter (1971). The approximation is likely to be particularly good if the regression has an intercept and if the regressors are few in number and smoothly evolving.…”

mentioning

confidence: 68%

Towards a theory of point optimal testing

King

1987

Econometric Reviews

110

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This paper puts the case for the inclusion of point optimal tests in the econometrician's repertoire. They do not suit every testing situation but the current evidence, which is reviewed here, indicates that they can have extremely useful small-sample power properties. As well as being most powerful at a nominated point in the alternative hypothesis parameter space, they may also have optimum power at a number of other points and indeed be uniformly most powerful when such a test exists. Point optimal tests can also be used to trace out the maximum attainable power envelope for a given testing problem, thus providing a benchmark against which test procedures can be evaluated. In some cases, point optimal tests can be constructed from tests of a simple null hypothesis against a simple alternative. For a wide range Of models of interest to econometricians, this paper shows how one can check whether a point optimal test can be constructed in this way. When it cannot, one may wish to consider approximately point optimal tests. As an illustration, the approach is applied to the non-nested problem of testing for AR(1) disturbances against MA(1) disturbances in the linear regression model.

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On a New Test for Autocorrelation in Least Squares Regression

Cited by 5 publications

References 4 publications

On typical characteristics of economic time series and the relative qualities of five autocorrelation tests

On typical characteristics of economic time series and the relative qualities of five autocorrelation tests

A Point Optimal Test for Moving Average Regression Disturbances

Towards a theory of point optimal testing

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