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Testing linear restrictions on cointegration vectors: Sizes and powers of Wald tests in finite samples

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  • Haug, Alfred A.

Abstract

The Wald test for linear restrictions on cointegrating vectors is compared infinite samples using the Monte Carlo method. The Wald test within the vector error-correction based methods of Bewley et al (1994) and of Johansen (1991), the canonical cointegration method of Park (1992) the dynamic ordinary least squares method of Phillips and Loretan (1991), Saikkonen (1991) and Stock and Watson (1993) the fully modified ordinary least squares method of Phillips and Hansen (1990) and the band spectral techniques of Phillips (1991) are considered. In terms of test size Johansen’s method seems to be preferred and in terms of test power it is Park’s and Phillips’. However the relatively poor results in the context of cointegrating regressions suggest that improvements on the performance of the Wald tests considered here are needed. --

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Paper provided by Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen in its series Technical Reports with number 1999,04.

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Date of creation: 1999
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Handle: RePEc:zbw:sfb475:199904

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  1. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
  2. Xiao, Zhijie & Phillips, Peter C. B., 1998. "Higher-order approximations for frequency domain time series regression," Journal of Econometrics, Elsevier, Elsevier, vol. 86(2), pages 297-336, June.
  3. Park, Joon Y, 1992. "Canonical Cointegrating Regressions," Econometrica, Econometric Society, Econometric Society, vol. 60(1), pages 119-43, January.
  4. Ho, Mun S & Sorensen, Bent E, 1996. "Finding Cointegration Rank in High Dimensional Systems Using the Johansen Test: An Illustration Using Data Based Monte Carlo Simulations," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 726-32, November.
  5. Phillips, Peter C B, 1994. "Some Exact Distribution Theory for Maximum Likelihood Estimators of Cointegrating Coefficients in Error Correction Models," Econometrica, Econometric Society, Econometric Society, vol. 62(1), pages 73-93, January.
  6. Bewley, Ronald & Orden, David & Yang, Minxian & Fisher, Lance A., 1994. "Comparison of Box--Tiao and Johansen canonical estimators of cointegrating vectors in VEC(1) models," Journal of Econometrics, Elsevier, Elsevier, vol. 64(1-2), pages 3-27.
  7. Bossaerts, Peter, 1988. "Common nonstationary components of asset prices," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 12(2-3), pages 347-364.
  8. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, Econometric Society, vol. 59(3), pages 817-58, May.
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  14. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, Econometric Society, vol. 60(4), pages 953-66, July.
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  17. Kitamura, Yuichi & Phillips, Peter C.B., 1995. "Efficient IV Estimation in Nonstationary Regression," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 11(05), pages 1095-1130, October.
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  21. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, Econometric Society, vol. 59(6), pages 1551-80, November.
  22. Toda, Hiro Y., 1995. "Finite Sample Performance of Likelihood Ratio Tests for Cointegrating Ranks in Vector Autoregressions," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 11(05), pages 1015-1032, October.
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