The power of the Durbin-Watson test for regressions without an intercept

DOI10.1016/0304-4076(85)90005-3zbMath0581.62097OpenAlexW1982878780MaRDI QIDQ1067739

Walter Kramer

Publication date: 1985

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4076(85)90005-3

zbMATH Keywords

serial correlationlimiting power of the Durbin-Watson testlinear regression model without an interceptregressor matrix

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03)

Related Items (17)

The limiting power of the durbin-watson test ⋮ The limiting power of point optimal autocorrelation tests ⋮ Higher order generalisation of first order autoregressive tests ⋮ On the power of the durbin-watson test under high autocorrelation ⋮ The power of autocorrelation tests near the unit root in models with possibly mis-specified linear restrictions ⋮ HOW TO AVOID THE ZERO-POWER TRAP IN TESTING FOR CORRELATION ⋮ On the robustness of the F-test to autocorrelation among disturbances ⋮ ON THE POWER OF INVARIANT TESTS FOR HYPOTHESES ON A COVARIANCE MATRIX ⋮ On the sensitivity of the usual \(t\)- and \(F\)-tests to covariance misspecification ⋮ On the sensitivity of the restricted least squares estimators to covariance misspecification ⋮ POWER PROPERTIES OF INVARIANT TESTS FOR SPATIAL AUTOCORRELATION IN LINEAR REGRESSION ⋮ Finite sample power of linear regression autocorrelation tests ⋮ The power of unit root tests under local-to-finite variance errors ⋮ Finite‐sample power of the Durbin–Watson test against fractionally integrated disturbances ⋮ The sensitivity of OLS when the variance matrix is (partially) unknown ⋮ Optimal invariant tests for the autocorrelation coefficient in linear regressions with stationary or nonstationary AR(1) errors ⋮ The exact powers of some autocorrelation tests when the disturbances are heteroscedastic

Cites Work

• Fourth-order autocorrelation: Further significance points for the Wallis test
• On choosing the optimal level of significance for the Durbin-Watson test and the Bayesian alternative
• Finite Sample Efficiency of Ordinary Least Squares in the Linear Regression Model with Autocorrelated Errors
• The Power of the Durbin-Watson Test
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