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Testing Linear Restrictions On Cointegrating Vectors: Sizes And Powers Of Wald And Likelihood Ratio Tests In Finite Samples

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  • Haug, Alfred A.

Abstract

The Wald test for linear restrictions on cointegrating vectors is compared in finite samples using the Monte Carlo method. The Wald test is calculated within the vector error-correction based estimation methods of Bewley, Orden, Yang, and Fisher (1994, Journal of Econometrics 64, 3–27) and of Johansen (1991, Econometrica 59, 1551–1580), the canonical cointegration method of Park (1992, Econometrica 60, 119–143), the dynamic ordinary least squares method of Phillips and Loretan (1991, Review of Economic Studies 58, 407–436), Saikkonen (1991, Econometric Theory 7, 1–21), and Stock and Watson (1993, Econometrica 61, 783–820), the fully modified ordinary least squares method of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125), and the band spectral techniques of Phillips (1991, in W. Barnett, J. Powell, & G. E. Tauchen (eds.), Nonparametric and Semiparametric Methods in Economics and Statistics, pp. 413–435). The Wald test performance is also compared to that of the likelihood ratio test suggested by Johansen and Juselius (1990, Oxford Bulletin of Economics and Statistics 52, 169–210) and to a Bartlett correction of that test as proposed by Johansen (1998, A Small Sample Test for Tests of Hypotheses on Cointegrating Vectors, European University Institute).

Suggested Citation

  • Haug, Alfred A., 2002. "Testing Linear Restrictions On Cointegrating Vectors: Sizes And Powers Of Wald And Likelihood Ratio Tests In Finite Samples," Econometric Theory, Cambridge University Press, vol. 18(2), pages 505-524, April.
    • Handle: RePEc:cup:etheor:v:18:y:2002:i:02:p:505-524_18
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    Cited by:

    1. Agnolucci, Paolo & De Lipsis, Vincenzo & Arvanitopoulos, Theodoros, 2017. "Modelling UK sub-sector industrial energy demand," Energy Economics, Elsevier, vol. 67(C), pages 366-374.
    2. Haug Alfred A & Beyer Andreas & Dewald William, 2011. "Structural Breaks and the Fisher Effect," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-31, May.
    3. Beyer, Andreas & Dewald, William G. & Haug, Alfred A., 2009. "Structural breaks, cointegration and the Fisher effect," Working Paper Series 1013, European Central Bank.
    4. Hautsch, Nikolaus & Huang, Ruihong, 2012. "The market impact of a limit order," Journal of Economic Dynamics and Control, Elsevier, vol. 36(4), pages 501-522.
    5. Eriksson , Åsa, 2004. "Testing Structural Hypotheses on Cointegration Vectors: A Monte Carlo Study," Working Papers 2004:29, Lund University, Department of Economics.
    6. Canepa, Alessandra, 2020. "Bootstrap Bartlett Adjustment for Hypotheses Testing on Cointegrating Vectors," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202006, University of Turin.
    7. Canepa Alessandra, 2022. "Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors," Journal of Time Series Econometrics, De Gruyter, vol. 14(1), pages 51-85, January.
    8. Norman J. Morin, 2006. "Likelihood ratio tests on cointegrating vectors, disequilibrium adjustment vectors, and their orthogonal complements," Finance and Economics Discussion Series 2006-21, Board of Governors of the Federal Reserve System (U.S.).
    9. Ahmed Raza ul MUSTAFA* & Mohammad NISHAT**, 2017. "ROLE OF SOCIAL PROTECTION IN POVERTY REDUCTION IN PAKISTAN: A Quantitative Approach," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 27(1), pages 67-88.
    10. Alfred A. Haug & Julie Tam, 2001. "A Closer Look at Long Run Money Demand," Working Papers 2002_09, York University, Department of Economics, revised Sep 2002.
    11. Canepa, Alessandra, 2020. "Improvement on the LR Test Statistic on the Cointegrating Relations in VAR Models: Bootstrap Methods and Applications," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202007, University of Turin.
    12. Canepa, Alessandra, 2016. "A note on Bartlett correction factor for tests on cointegrating relations," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 296-304.
    13. Alfred A. Haug & Julie Tam, 2007. "A Closer Look At Long‐Run U.S. Money Demand: Linear Or Nonlinear Error‐Correction With M0, M1, Or M2?," Economic Inquiry, Western Economic Association International, vol. 45(2), pages 363-376, April.

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