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Data Dependent Rules For Selection Of The Number Of Leads And Lags In The Dynamic Ols Cointegrating Regression

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Kejriwal, Mohitosh
Perron, Pierre

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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.

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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 24 (2008)
Issue (Month): 05 (October)
Pages: 1425-1441
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Handle: RePEc:cup:etheor:v:24:y:2008:i:05:p:1425-1441_08

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  1. 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. [Downloadable!]
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This page was last updated on 2009-11-24.

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