IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v22y2003i3p217-237.html
   My bibliography  Save this article

Statistical Adequacy and the Testing of Trend Versus Difference Stationarity

Author

Listed:
  • Elena Andreou
  • Aris Spanos

Abstract

The debate on whether macroeconomic series are trend or difference stationary, initiated by Nelson and Plosser [Nelson, C. R.; Plosser, C. I. (1982). Trends and random walks in macroeconomic time series: some evidence and implications. Journal of Monetary Economics 10:139-162] remains unresolved. The main objective of the paper is to contribute toward a resolution of this issue by bringing into the discussion the problem of statistical adequacy . The paper revisits the empirical results of Nelson and Plosser [Nelson, C. R.; Plosser, C. I. (1982). Trends and random walks in macroeconomic time series: some evidence and implications. Journal of Monetary Economics 10:139-162] and Perron [Perron, P. (1989). The great crash, the oil price shock, and the unit root hypothesis. Econometrica 57:1361-1401] and shows that several of their estimated models are misspecified. Respecification with a view to ensuring statistical adequacy gives rise to heteroskedastic AR( k ) models for some of the price series. Based on estimated models which are statistically adequate, the main conclusion of the paper is that the majority of the data series are trend stationary.

Suggested Citation

  • Elena Andreou & Aris Spanos, 2003. "Statistical Adequacy and the Testing of Trend Versus Difference Stationarity," Econometric Reviews, Taylor & Francis Journals, vol. 22(3), pages 217-237, January.
  • Handle: RePEc:taf:emetrv:v:22:y:2003:i:3:p:217-237 DOI: 10.1081/ETC-120023897
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1081/ETC-120023897
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, pages 673-700.
    2. Jeffrey T. LaFrance, 1999. "Inferring the Nutrient Content of Food With Prior Information," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 728-734.
    3. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
    4. Diebold, Francis X. & Lamb, Russell L., 1997. "Why are estimates of agricultural supply response so variable?," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 357-373.
    5. Zellner, Arnold, 1978. "Estimation of functions of population means and regression coefficients including structural coefficients : A minimum expected loss (MELO) approach," Journal of Econometrics, Elsevier, vol. 8(2), pages 127-158, October.
    6. Zellner, Arnold, 1996. "Models, prior information, and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 75(1), pages 51-68, November.
    7. Zellner, Arnold, 1980. "A Note on the Relationship of Minimum Expected Loss (MELO) and Other Structural Coefficient Estimates," The Review of Economics and Statistics, MIT Press, pages 482-484.
    8. Shen, Edward Z. & Perloff, Jeffrey M., 2001. "Maximum entropy and Bayesian approaches to the ratio problem," Journal of Econometrics, Elsevier, vol. 104(2), pages 289-313, September.
    9. Zellner, Arnold, 1988. "Bayesian analysis in econometrics," Journal of Econometrics, Elsevier, vol. 37(1), pages 27-50, January.
    10. Arnold Zellner, 1997. "Bayesian Analysis in Econometrics and Statistics," Books, Edward Elgar Publishing, number 825, September.
    11. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers Archive 1488, Iowa State University, Department of Economics.
    12. Sawa, Takamitsu, 1972. "Finite-Sample Properties of the k-Class Estimators," Econometrica, Econometric Society, vol. 40(4), pages 653-680, July.
    13. Zellner, A., 1988. "Optimal Information-Processing And Bayes' Theorem," Papers m8803, Southern California - Department of Economics.
    14. Park, Soo-Bin, 1982. "Some sampling properties of minimum expected loss (MELO) estimators of structural coefficients," Journal of Econometrics, Elsevier, vol. 18(3), pages 295-311, April.
    15. Zellner, Arnold, 2002. "Information processing and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 41-50, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Vicente Martínez, Eva, 2006. "Properties of two U.S. inflation measures (1985-2005)," DES - Working Papers. Statistics and Econometrics. WS ws066818, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Bårdsen, Gunnar & Nymoen, Ragnar, 2006. "U.S. natural rate dynamics reconsidered," Memorandum 13/2006, Oslo University, Department of Economics.
    3. Q. Farooq Akram & Ragnar Nymoen, 2009. "Model Selection for Monetary Policy Analysis: How Important is Empirical Validity?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 35-68, February.
    4. Charles, Amélie & Darné, Olivier, 2012. "Trends and random walks in macroeconomic time series: A reappraisal," Journal of Macroeconomics, Elsevier, pages 167-180.
    5. Olivier Darné & Amélie Charles, 2011. "Large shocks in U.S. macroeconomic time series: 1860-1988," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 5(1), pages 79-100, January.
    6. Abadir, Karim M. & Caggiano, Giovanni & Talmain, Gabriel, 2013. "Nelson–Plosser revisited: The ACF approach," Journal of Econometrics, Elsevier, pages 22-34.
    7. Maria Heracleous & Andreas Koutris & Aris Spanos, 2006. "Testing for Structural Breaks and other forms of Non-stationarity: a Misspecification Perspective," Computing in Economics and Finance 2006 493, Society for Computational Economics.
    8. Q. Farooq Akram & Ragnar Nymoen, 2006. "Model selection for monetary policy analysis – Importance of empirical validity," Working Paper 2006/13, Norges Bank.
    9. Atiq-ur-Rehman, 2011. "Impact of Model Specification Decisions on Unit Root Tests," International Econometric Review (IER), Econometric Research Association, vol. 3(2), pages 22-33, September.
    10. Spanos, Aris, 2010. "Akaike-type criteria and the reliability of inference: Model selection versus statistical model specification," Journal of Econometrics, Elsevier, vol. 158(2), pages 204-220, October.
    11. Iolanda Lo Cascio & Stephen Pollock, 2007. "Comparative Economic Cycles," Working Papers 599, Queen Mary University of London, School of Economics and Finance.
    12. Fæhn, Taran & Gómez-Plana, Antonio G. & Kverndokk, Snorre, 2009. "Can a carbon permit system reduce Spanish unemployment?," Energy Economics, Elsevier, pages 595-604.
    13. Solveig Erlandsen & Ragnar Nymoen, 2008. "Consumption and population age structure," Journal of Population Economics, Springer;European Society for Population Economics, pages 505-520.
    14. Pohl, Walter & Schmedders, Karl & Wilms, Ole, 2016. "Asset prices with non-permanent shocks to consumption," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 152-178.
    15. Solveig Erlandsen & Ragnar Nymoen, 2008. "Consumption and population age structure," Journal of Population Economics, Springer;European Society for Population Economics, pages 505-520.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:emetrv:v:22:y:2003:i:3:p:217-237. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://www.tandfonline.com/LECR20 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.