Abstract
Although originally designed to detect AR(1) disturbances in the linear-regression model, the Durbin-Watson test is known to have good power against other forms of disturbance behavior. In this paper, we identify disturbance processes involving any number of parameters against which the Durbin-Watson test is approximately locally best invariant uniformly in a range of directions from the null hypothesis. Examples include the sum of q independent ARMA(1,1) processes, certain spatial autocorrelation processes involving up to four parameters, and a stochastic cycle model.
Original language | English |
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Pages (from-to) | 509-516 |
Number of pages | 8 |
Journal | Econometric Theory |
Volume | 4 |
Issue number | 3 |
DOIs | |
Publication status | Published - Dec 1988 |