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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.

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Bibliographic Info

Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number WP2006-035.

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Length: 18 pages
Date of creation: Jun 2006
Date of revision:
Handle: RePEc:bos:wpaper:wp2006-035

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Cited by:
  1. Eiji Kurozumi & Kazuhiko Hayakawa, 2006. "Asymptotic Properties of the Efficient Estimators for Cointegrating Regression Models with Serially Dependent Errors," Hi-Stat Discussion Paper Series d06-197, Institute of Economic Research, Hitotsubashi University.
  2. Kejriwal, Mohitosh & Perron, Pierre, 2008. "The limit distribution of the estimates in cointegrated regression models with multiple structural changes," Journal of Econometrics, Elsevier, vol. 146(1), pages 59-73, September.
  3. Beyer, Andreas & Haug, Alfred A. & Dewald, William G., 2009. "Structural breaks, cointegration and the Fisher effect," Working Paper Series 1013, European Central Bank.
  4. Francesco VENTURINI, 2006. "The Long-Run Impact of ICT," Working Papers 254, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.

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