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

Published online by Cambridge University Press:  09 July 2008

Mohitosh Kejriwal  and
Pierre Perron
Mohitosh Kejriwal*
Affiliation:
Purdue University
Pierre Perron
Affiliation:
Boston University
*
Address correspondence to Mohitosh Kejriwal, Krannert School of Management, Purdue University, 403 West State Street, West Lafayette, IN 47907, USA; e-mail: [email protected]
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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.

Type
Research Article
Information
Econometric Theory , Volume 24 , Issue 5 , October 2008 , pp. 1425 - 1441
Copyright
Copyright © Cambridge University Press 2008

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