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

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  • Mohitosh Kejriwal

    (Department of Economics, Boston University)

  • Pierre Perron

    (Department of Economics, Boston University)

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.

Suggested Citation

  • Mohitosh Kejriwal & Pierre Perron, 2007. "Data Dependent Rules for the Selection of the Number of Leads and Lags in the Dynamic OLS Cointegrating Regression," Boston University - Department of Economics - Working Papers Series WP2007-018, Boston University - Department of Economics.
    • Handle: RePEc:bos:wpaper:wp2007-018
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