Bootstrapping Cointegrating Regressions

Yoosoon Chang (), Joon Park and Kevin Song
Additional contact information
Kevin Song: Yale U

Working Papers from Rice University, Department of Economics

Abstract: In this paper, we consider bootstrapping cointegrating regressions. It is shown that the method of bootstrap, if properly implemented, generally yields consistent estimators and test statistics for cointegrating regressions. We do not assume any specific data generating process, and employ the sieve bootstrap based on the approximated finite-order vector autoregressions for the regression errors and the firrst differences of the regressors. In particular, we establish the bootstrap consistency for OLS method. The bootstrap method can thus be used to correct for the finite sample bias of the OLS estimator and to approximate the asymptotic critical values of the OLS-based test statistics in general cointegrating regressions. The bootstrap OLS procedure, however, is not efficient. For the efficient estimation and hypothesis testing, we consider the procedure proposed by Saikkonen (1991) and Stock and Watson (1993) relying on the regression augmented with the leads and lags of differenced regressors. The bootstrap versions of their procedures are shown to be consistent, and can be used to do inferences that are asymptotically valid. A Monte Carlo study is conducted to investigate the finite sample performances of the proposed bootstrap methods.

Date: 2002-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.ruf.rice.edu/~econ/papers/2002papers/04Chang.pdf

Related works:
Journal Article: Bootstrapping cointegrating regressions (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecl:riceco:2002-04

Access Statistics for this paper

More papers in Working Papers from Rice University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by ().