Model Selection Criteria for the Leads-and-Lags Cointegrating Regression
AbstractIn this paper, Mallows'(1973) Cp criterion, Akaike's (1973) AIC, Hurvich and Tsai's (1989) corrected AIC and the BIC of Akaike (1978) and Schwarz (1978) are derived for the leads-and-lags cointegrating regression. Deriving model selection criteria for the leads-and-lags regression is a nontrivial task since the true model is of infinite dimension. This paper justifies using the conventional formulas of those model selection criteria for the leads-and-lags cointegrating regression. The numbers of leads and lags can be selected in scientific ways using the model selection criteria. Simulation results regarding the bias and mean squared error of the long-run coefficient estimates are reported. It is found that the model selection criteria are successful in reducing bias and mean squared error relative to the conventional, fixed selection rules. Among the model selection criteria, the BIC appears to be most successful in reducing MSE, and Cp in reducing bias. We also observe that, in most cases, the selection rules without the restriction that the numbers of the leads and lags be the same have an advantage over those with it.
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Bibliographic InfoPaper provided by Research Institute for Market Economy, Sogang University in its series Working Papers with number 0801.
Length: 33 pages
Date of creation: Apr 2008
Date of revision: Aug 2009
Cointegration; Leads-and-lags regression; AIC; Corrected AIC; BIC; Cp;
Other versions of this item:
- Choi, In & Kurozumi, Eiji, 2012. "Model selection criteria for the leads-and-lags cointegrating regression," Journal of Econometrics, Elsevier, vol. 169(2), pages 224-238.
- Choi, In & Kurozumi, Eiji, 2008. "Model Selection Criteria for the Leads-and-Lags Cointegrating Regression," CCES Discussion Paper Series 6, Center for Research on Contemporary Economic Systems, Graduate School of Economics, Hitotsubashi University.
- In Choi & Eiji Kurozumi, 2008. "Model Selection Criteria for the Leads-and-Lags Cointegrating Regression," Global COE Hi-Stat Discussion Paper Series gd08-006, Institute of Economic Research, Hitotsubashi University.
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