Data Dependent Rules for the Selection of the Number of Leads and Lags in the Dynamic OLS Cointegrating Regression*

Mohitosh Kejriwal and Pierre Perron

No WP2007-018, Boston University - Department of Economics - Working Papers Series from Boston University - Department of Economics

Abstract: Saikkonen (1991) developed an asymptotic optimality theory for the estimation of cointegrated regressions. He proposed the dynamic 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 AIC and BIC to select the number of leads and lags. We show that his results remain valid under weaker conditions which 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.

Pages: 18pages
Date: 2007-03
New Economics Papers: this item is included in nep-bec, nep-cfn, nep-com, nep-cse and nep-ind
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Published, Econometric Theory 24 (2008), 1425-1441

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Related works:
Journal Article: DATA DEPENDENT RULES FOR SELECTION OF THE NUMBER OF LEADS AND LAGS IN THE DYNAMIC OLS COINTEGRATING REGRESSION (2008)
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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