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The role of seasonality in economic time series reinterpreting money-output causality in U.S. data

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  • Lee, Hahn Shik
  • Siklos, Pierre 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.
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  • Lee, Hahn Shik & Siklos, Pierre L., 1997. "The role of seasonality in economic time series reinterpreting money-output causality in U.S. data," International Journal of Forecasting, Elsevier, vol. 13(3), pages 381-391, September.
  • Handle: RePEc:eee:intfor:v:13:y:1997:i:3:p:381-391
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    Cited by:

    1. 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.
    2. 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.
    3. Albertson, Kevin & Aylen, Jonathan, 2003. "Forecasting the behaviour of manufacturing inventory," International Journal of Forecasting, Elsevier, vol. 19(2), pages 299-311.
    4. Lof, Marten & Hans Franses, Philip, 2001. "On forecasting cointegrated seasonal time series," International Journal of Forecasting, Elsevier, vol. 17(4), pages 607-621.
    5. Darne, Olivier, 2004. "Seasonal cointegration for monthly data," Economics Letters, Elsevier, vol. 82(3), pages 349-356, March.
    6. 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.
    7. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707.
    8. Geoffrey R. Dunbar, 2014. "Demographics and the Demand for Currency," Staff Working Papers 14-59, Bank of Canada.
    9. Rothman, P. & van Dijk, D.J.C. & Franses, Ph.H.B.F., 1999. "A multivariate STAR analysis of the relationship between money and output," Econometric Institute Research Papers EI 9945-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. 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.

    More about this item

    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

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