IDEAS home Printed from https://ideas.repec.org/p/wlu/wpaper/97-1.html

The Role of Seasonality in Economic Time Series: Reinterpretating Money-Output Causality in U.S. Data

Author

Listed:
  • Lee, H.S.
  • Siklos, P.L.

Abstract

While empirical evidence on the relationship between money and income has mainly been presented using seasonally adjusted data, seasonally unadjusted data are used in this paper to examine the time series behaviour of money, real GNP, and industrial production, at both the seasonal and zero frequencies based on tests of cointegration and seasonal cointegration. Two important conclusions are reached in the paper. First, although the univariate time series properties of M1 and real GNP appear to be very similar at both the seasonal and zero frequencies, seasonal comovements of M1 and real GNP turn out to be different from long- run comovements. Second, when seasonally unadjusted data are used, there appears to be no long-run relationship between money (M1 or M2) and output in the sense that the null of no cointegration cannot be rejected.

Suggested Citation

  • Lee, H.S. & Siklos, P.L., 1997. "The Role of Seasonality in Economic Time Series: Reinterpretating Money-Output Causality in U.S. Data," Working Papers 97-1, Wilfrid Laurier University, Department of Economics.
    • Handle: RePEc:wlu:wpaper:97-1
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Philip Rothman & Dick van Dijk & Philip Hans Franses, 1999. "A Multivariate STAR Analysis of the Relationship Between Money and Output," Working Papers 9913, East Carolina University, Department of Economics.
    2. Omar A Mendoza Lugo, 2008. "The differential impact of real interest rates and credit availability on private investment: evidence from Venezuela," BIS Papers chapters, in: Bank for International Settlements (ed.), Transmission mechanisms for monetary policy in emerging market economies, volume 35, pages 501-537, Bank for International Settlements.
    3. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    4. Darne, Olivier, 2004. "Seasonal cointegration for monthly data," Economics Letters, Elsevier, vol. 82(3), pages 349-356, March.
    5. Geoffrey R. Dunbar, 2019. "Demographics and the demand for currency," Empirical Economics, Springer, vol. 57(4), pages 1375-1409, October.
    6. Nikolaos Giannellis & Minoas Koukouritakis, 2011. "Behavioural equilibrium exchange rate and total misalignment: evidence from the euro exchange rate," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(4), pages 555-578, November.
    7. Lee TongHung & Hwang Hoyoung, 2001. "Money, Interest Rate and Foreign Exchange Rate As Indicator Variables Of Monetary Policy," International Economic Journal, Taylor & Francis Journals, vol. 15(2), pages 77-98, June.
    8. Lof, Marten & Hans Franses, Philip, 2001. "On forecasting cointegrated seasonal time series," International Journal of Forecasting, Elsevier, vol. 17(4), pages 607-621.
    9. Polemis, Dionysios & Bentsos, Christos, 2024. "Seasonality patterns in LNG shipping spot and time charter freight rates," Journal of Commodity Markets, Elsevier, vol. 35(C).
    10. Albertson, Kevin & Aylen, Jonathan, 2003. "Forecasting the behaviour of manufacturing inventory," International Journal of Forecasting, Elsevier, vol. 19(2), pages 299-311.
    11. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, January.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    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:wlu:wpaper:97-1. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Glen Stewart The email address of this maintainer does not seem to be valid anymore. Please ask Glen Stewart to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/sbwluca.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.