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A Gaussian Test for Cointegration

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  • Gulasekaran Rajaguru

    (School of Business, Bond University, Australia)

  • Tilak Abeysinghe

    ()
    (Department of Economics, National University of Singapore)

Abstract

We use a mixed-frequency regression technique to develop a test for cointegration under the null of stationarity of the deviations from a long-run relationship. What is noteworthy about this MA unit root test, based on a variance-difference, is that, instead of having to deal with non-standard distributions, it takes the testing back to the normal distribution and offers a way to increase power without having to increase the sample size substantially. Monte Carlo simulations show minimal size distortions even when the AR root is close to unity and that the test offers substantial gains in power against near-null alternatives in moderate size samples. An empirical exercise illustrates the relative usefulness of the test further.

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Bibliographic Info

Paper provided by National University of Singapore, Department of Economics, SCAPE in its series SCAPE Policy Research Working Paper Series with number 0905.

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Length: 32 pages
Date of creation: Dec 2009
Date of revision:
Handle: RePEc:sca:scaewp:0905

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Web page: http://www.fas.nus.edu.sg/ecs/scape/index.html
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Keywords: Null of stationarity; MA unit root; mixed-frequency regression; variance difference; normal distribution; power.;

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  1. Rajaguru GULASEKARAN & Tilak ABEYSINGHE, 2002. "The Distortionary Effects Of Temporal Aggregation On Granger Causality," Departmental Working Papers wp0204, National University of Singapore, Department of Economics.
  2. Andrew W. Lo & Craig A. MacKinlay, . "The Size and Power of the Variance Ratio Test in Finite Samples: A Monte Carlo Investigation," Rodney L. White Center for Financial Research Working Papers 28-87, Wharton School Rodney L. White Center for Financial Research.
  3. Choi, In, 1994. "Residual-Based Tests for the Null of Stationarity with Applications to U.S. Macroeconomic Time Series," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 720-746, August.
  4. Peter C.B. Phillips, 1998. "New Unit Root Asymptotics in the Presence of Deterministic Trends," Cowles Foundation Discussion Papers 1196, Cowles Foundation for Research in Economics, Yale University.
  5. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-44, January.
  6. Pantula, Sastry G. & Hall, Alastair, 1991. "Testing for unit roots in autoregressive moving average models : An instrumental variable approach," Journal of Econometrics, Elsevier, vol. 48(3), pages 325-353, June.
  7. Abeysinghe, Tilak, 2000. "Modeling variables of different frequencies," International Journal of Forecasting, Elsevier, vol. 16(1), pages 117-119.
  8. Markku Lanne & Helmut Lutkepohl & Pentti Saikkonen, 2003. "Test Procedures for Unit Roots in Time Series with Level Shifts at Unknown Time," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(1), pages 91-115, February.
  9. Campbell, J.Y. & Perron, P., 1991. "Pitfalls and Opportunities: What Macroeconomics should know about unit roots," Papers 360, Princeton, Department of Economics - Econometric Research Program.
  10. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-36, July.
  11. Denis Kwiatkowski & Peter C.B. Phillips & Peter Schmidt, 1991. "Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?," Cowles Foundation Discussion Papers 979, Cowles Foundation for Research in Economics, Yale University.
  12. Hwang, Jaeyoun & Schmidt, Peter, 1996. "Alternative methods of detrending and the power of unit root tests," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 227-248.
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