Nonnested testing for autocorrelation in the linear regression model

DOI10.1016/0304-4076(93)90047-9zbMath0775.62178OpenAlexW2016526537MaRDI QIDQ1260674

Paramsothy Silvapulle, Maxwell L. King

Publication date: 25 August 1993

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

Full work available at URL: https://doi.org/10.1016/0304-4076(93)90047-9

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Hypothesis testing in multivariate analysis (62H15)

Related Items (6)

Most mean powerful invariant test for testing two-dimensional parameter spaces ⋮ A joint test for serial correlation and heteroscedasticity ⋮ Alternative Procedures to Discriminate Non Nested Multivariate Linear Regression Models ⋮ Robustness of the arch tests in the presence of serial correlation ⋮ Nonnested Testing for Competing Autoregressive Dynamic Models Estimated by Instrumental Variables ⋮ COMPARING TESTS OF AUTOREGRESSIVE VERSUS MOVING AVERAGE ERRORS IN REGRESSION MODELS USING BAHADUR’S ASYMPTOTIC RELATIVE EFFICIENCY

Uses Software

• AS 155
• AS 153

Cites Work

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• Useful invariance results for generalized regression models
• Testing for autoregressive against moving average errors in the linear regression model
• Testing AR(1) Against MA(1) Disturbances in the Linear Regression Model: An Alternative Procedure
• Fast Evaluation of the Distribution of the Durbin-Watson and Other Invariant Test Statistics in Time Series Regression
• Further Results on Testing AR (1) Against MA (1) Disturbances in the Linear Regression Model
• Algorithm AS 155: The Distribution of a Linear Combination of χ 2 Random Variables
• Algorithm AS 153: Pan's Procedure for the Tail Probabilities of the Durbin-Watson Statistic
• The Estimation and Use of Models with Moving Average Disturbance Terms: A Survey
• An algorithm for the exact likelihood of a mixed autoregressive-moving average process
• Computing the distribution of quadratic forms in normal variables
• Some tests of separate families of hypotheses in time series analysis
• Testing for Fourth Order Autocorrelation in Quarterly Regression Equations
• Checks of model adequacy for univariate time series models and their application to econometric relationships

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