Testing for autocorrelation in the presence of lagged dependent variables

DOI10.1016/0304-4076(94)90046-9zbMath0800.62808OpenAlexW1530522398MaRDI QIDQ1318997

Jerry G. Thursby, Hashem Dezhbakhsh

Publication date: 12 April 1994

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

Full work available at URL: https://doi.org/10.1016/0304-4076(94)90046-9

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric hypothesis testing (62F03)

Related Items (2)

Hausman tests for autocorrelation in the presence of lagged dependent variables. Some further results ⋮ The wild bootstrap and heteroskedasticity-robust tests for serial correlation in dynamic regression models

Cites Work

• Finite-sample power of tests for autocorrelation in models containing lagged dependent variables
• A point optimal test for autoregressive disturbances
• The small sample bias of Durbin's tests for serial correlation when one of the regressors is the lagged dependent variable and the null hypothesis is true
• A note on an efficient two-step estimator
• Equivalence properties of the Hausman statistic based on a generalized inverse
• An efficient two-step estimator for the dynamic adjustment model with autoregressive errors
• A New Test for Autocorrelation in the Disturbances of the Dynamic Linear Regression Model
• A Remark on Hausman's Specification Test
• On the Rigour of Some Misspecification Tests for Modelling Dynamic Relationships
• Some Small Sample Properties of Durbin's Tests for Serial Correlation in Regression Models Containing Lagged Dependent Variables
• Tests for Serial Correlation in Regression Models with Lagged Dependent Variables and Serially Correlated Errors
• An Alternative Derivation of Durbin's h Statistic
• Specification Tests in Econometrics
• Power of the Noncentral F-Test: Effect of Additional Variates on Hotelling's T 2 -Test
• Computing the distribution of quadratic forms in normal variables
• Distribution of the Serial Correlation Coefficient

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