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DATA DEPENDENT RULES FOR SELECTION OF THE NUMBER OF LEADS AND LAGS IN THE DYNAMIC OLS COINTEGRATING REGRESSION

Mohitosh Kejriwal and Pierre Perron ()

Econometric Theory, 2008, vol. 24, issue 05, 1425-1441

Abstract: Saikkonen (1991, Econometric Theory 7, 1–21) developed an asymptotic optimality theory for the estimation of cointegrated regressions. He proposed the dynamic ordinary least squares (OLS) estimator obtained by augmenting the static cointegrating regression with leads and lags of the first differences of the I(1) regressors. However, the assumptions imposed preclude the use of information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC) to select the number of leads and lags. We show that his results remain valid under weaker conditions that permit the use of such data dependent rules. Simulations show that, relative to sequential general to specific testing procedures, the use of such information criteria can indeed produce estimates with smaller mean squared errors and confidence intervals with better coverage rates.

Date: 2008
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Related works:
Working Paper: Data Dependent Rules for the Selection of the Number of Leads and Lags in the Dynamic OLS Cointegrating Regression* (2007)
Working Paper: Data Dependent Rules for the Selection of the Number of Leads and Lags in the Dynamic OLS Cointegrating Regression (2006)
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