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Critical Values for the Cusumsq Statistic in Medium and Large Sized Samples

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  • Edgerton, David
  • Wells, Curt

Abstract

Data series containing more than 200 observations are common in financial economics. The usefulness of the cusumsq test in such medium-sized samples has been hampered by the lack of tabulated confidence bounds and by the inaccuracy of asymptotic approximations. In this paper, the authors extend Durbin's table to encompass all practical sample sizes. They have empirically calculated asymptotic limits, which prove to be extremely accurate for sample sizes greater than sixty. A convenient algorithm for calculating P-values, which is useful even in small samples, is also referred to. Copyright 1994 by Blackwell Publishing Ltd

Suggested Citation

  • Edgerton, David & Wells, Curt, 1994. "Critical Values for the Cusumsq Statistic in Medium and Large Sized Samples," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(3), pages 355-365, August.
  • Handle: RePEc:bla:obuest:v:56:y:1994:i:3:p:355-65
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    References listed on IDEAS

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    1. Zeileis, Achim & Leisch, Friedrich & Hornik, Kurt & Kleiber, Christian, 2002. "strucchange: An R Package for Testing for Structural Change in Linear Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i02).
    2. Kramer, Walter & Ploberger, Werner & Alt, Raimund, 1988. "Testing for Structural Change in Dynamic Models," Econometrica, Econometric Society, vol. 56(6), pages 1355-1369, November.
    3. Ploberger, Werner & Kramer, Walter, 1992. "The CUSUM Test with OLS Residuals," Econometrica, Econometric Society, vol. 60(2), pages 271-285, March.
    4. Kramer, Walter & Schotman, Peter, 1992. "Range vs. maximum in the OLS-based version of the CUSUM test," Economics Letters, Elsevier, vol. 40(4), pages 379-381, December.
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    Cited by:

    1. Patrick McGlenchy & Paul Kofman, 2004. "Structurally Sound Dynamic Index Futures Hedging," Econometric Society 2004 Australasian Meetings 80, Econometric Society.
    2. Zeileis, Achim, 2006. "Implementing a class of structural change tests: An econometric computing approach," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 2987-3008, July.
    3. Matteo Barigozzi & Antonio M. Conti & Matteo Luciani, 2014. "Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(5), pages 693-714, October.
    4. Geoffrey Ngene & Charles Lambert & Ali Darrat, 2015. "Testing Long Memory in the Presence of Structural Breaks: An Application to Regional and National Housing Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 50(4), pages 465-483, May.
    5. Goodwin, Barry K. & Piggott, Nicholas E., 2012. "Modeling Acreage Response in a New Market Environment," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124730, Agricultural and Applied Economics Association.
    6. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    7. repec:spr:stpapr:v:58:y:2017:i:3:d:10.1007_s00362-015-0722-y is not listed on IDEAS
    8. Bruns, Stephan B. & Csereklyei, Zsuzsanna & Stern, David I., 2018. "A multicointegration model of global climate change," Center for European, Governance and Economic Development Research Discussion Papers 336, University of Goettingen, Department of Economics.
    9. repec:spr:stpapr:v:58:y:2017:i:3:d:10.1007_s00362-015-0712-0 is not listed on IDEAS
    10. David I. Stern, 2004. "A Multicointegration Model of Global Climate Change," Rensselaer Working Papers in Economics 0406, Rensselaer Polytechnic Institute, Department of Economics.
    11. Johansson, Martin & Jönsson, Kristian, 2003. "Public debt and the effects of government expenditure on private consumption - A Kalman filter analysis of the Swedish experience 1970-1997," Working Papers 2003:3, Lund University, Department of Economics.
    12. Vanessa Berenguer-Rico & Bent Nielsen, 2015. "Cumulated sum of squares statistics for non-linear and non-stationary regressions," Economics Papers 2015-W09, Economics Group, Nuffield College, University of Oxford.
    13. Krämer, Jörg W., 1996. "Determinants of the expected real long-term interest rates in the G7-countries," Kiel Working Papers 751, Kiel Institute for the World Economy (IfW).
    14. Makram El-Shagi & Sebastian Giesen, 2013. "Testing for Structural Breaks at Unknown Time: A Steeplechase," Computational Economics, Springer;Society for Computational Economics, vol. 41(1), pages 101-123, January.
    15. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    16. J. Andrew Coutts & Terence Mills & Jennifer Roberts, 1997. "Time series and cross-section parameter stability in the market model: the implications for event studies," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 243-259.
    17. Kurt Hornik & Friedrich Leisch & Christian Kleiber & Achim Zeileis, 2005. "Monitoring structural change in dynamic econometric models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 99-121.
    18. Meligkotsidou, Loukia & Vrontos, Ioannis D., 2008. "Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2471-2481, November.

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