Robust Nonnested Testing for Ordinary Least Squares Regression When Some of the Regressors are Lagged Dependent Variables
The problem of testing nonnested regression models that include lagged values of the dependent variable as regressors is discussed. It is argued that it is essential to test for error autocorrelation if ordinary least squares and the associated J and F tests are to be used. A heteroskedasticity-robust joint test against a combination of the artificial alternatives used for autocorrelation and nonnested hypothesis tests is proposed. Monte Carlo results indicate that implementing this joint test using a wild bootstrap method leads to a well-behaved procedure and gives better control of finite sample significance levels than asymptotic critical values.
|Date of creation:||Oct 2010|
|Date of revision:|
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- Gonçalves, Sílvia & Kilian, Lutz, 2002.
"Bootstrapping autoregressions with conditional heteroskedasticity of unknown form,"
Working Paper Series
0196, European Central Bank.
- Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
- Kilian, Lutz & Gonçalves, Sílvia, 2002. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Discussion Paper Series 1: Economic Studies 2002,26, Deutsche Bundesbank, Research Centre.
- Gonçalves, Sílvia & KILIAN, Lutz, 2003. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Cahiers de recherche 01-2003, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- GONÇALVES, Silvia & KILIAN, Lutz, 2003. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Cahiers de recherche 2003-01, Universite de Montreal, Departement de sciences economiques.
- Sílvia Gonçalves & Lutz Kilian, 2003. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," CIRANO Working Papers 2003s-17, CIRANO.
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