An Introduction to Time Series Modeling and Forecasting

  • John B. GuerardJr.
Chapter

Abstract

An important aspect of financial decision making may depend on the forecasting effectiveness of the composite index of leading economic indicators, LEI. The leading indicators can be used as an input to a transfer function model of real Gross Domestic Product, GDP. The previous chapter employed four quarterly lags of the LEI series to estimate regression models of association between current rates of growth of real US GDP and the composite index of LEI. This chapter asks the question as to whether changes in forecasted economic indexes help forecast changes in real economic growth. The transfer function model forecasts are compared to several naïve models in terms of testing which model produces the most accurate forecast of real GDP. No-change (NoCH) forecasts of real GDP and random walk with drift (RWD) models may be useful forecasting benchmarks (Mincer and Zarnowitz 1969; Granger and Newbold 1977). Economists have constructed LEI series to serve as a business barometer of the changing US economy since the time of Mitchell (1913). The purpose of this study is to examine the time series forecasts of composite economic indexes produced by The Conference Board (TCB), and test the hypothesis that the leading indicators are useful as an input to a time series model to forecast real output in the United States.

Keywords

Covariance Income Autocorrelation 

References

  1. Box, G.E.P. and D.R. Cox. 1964. “An Analysis of Transformations.” Journal of the Royal Statistical Society B 26, 211–243.Google Scholar
  2. Box, G.E.P. and G.M. Jenkins. 1970. Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.Google Scholar
  3. Burns, A. F. and W.C. Mitchell. 1946. Measuring Business Cycles. New York, NBER.Google Scholar
  4. Fisher, I. 1911. The Purchasing Power of Money, New York: Macmillan.Google Scholar
  5. Friedman, M. and A. Schwartz. 1963. “Money and Business Cycles.” Review of Economics and Statistics 45, pp. 32–64.CrossRefGoogle Scholar
  6. Granger, C.W.J. and P. Newbold. 1977. Forecasting Economic Time Series. New York Academic Press.Google Scholar
  7. Guerard, J. B., Jr. 2004. “The Forecasting Effectiveness of the U.S. Leading Economic Indicators: Further Evidence and Initial G7 Results.” In P. Dua, Ed., Business Cycles and Economic Growth: An Analysis Using Leading Indicators. New York: Oxford University Press, 174–187.Google Scholar
  8. Guerard, J. B., Jr. 2001. “A Note on the Forecasting Effectiveness of the U.S. Leading Economic Indicators.” Indian Economic Review 36, pp. 251–268.Google Scholar
  9. Hamilton, J.D. 1994. Time Series Analysis, Princeton, Princeton University Press.Google Scholar
  10. Jenkins, G.M. 1979. Practical Experiences with Modelling and Forecasting Time Series. Jersey, Channel Islands: a GJP Publication.Google Scholar
  11. Ljung, G.M. and G.E.P. Box. 1978. “On a measure of lack of fit in time series models.” Biometrika 65, 297–303.CrossRefGoogle Scholar
  12. McCracken, M. 2000. “Robust out-of-sample inference.” Journal of Econometrics 39, 195–223.CrossRefGoogle Scholar
  13. McLeod, A.I. and W.K. Li. 1983. “Diagnostic checking ARMA time series models using squared residual autocorrelations.” Journal of Time Series Analysis 4, 269–273.CrossRefGoogle Scholar
  14. Mitchell, W.C. 1913. Business Cycles. New York, Burt Franklin reprint.Google Scholar
  15. Mitchell, W.C. 1951. What Happens During Business Cycles: A Progress Report. New York, NBER.Google Scholar
  16. Moore, G.H. 1961. Business Cycle Indicators. 2 volumes, Princeton, Princeton University Press.Google Scholar
  17. Montgomery, A.L., V. Zarnowitz, R.S. Tsay, and G.C. Tiao. 1998. “Forecasting the U.S. Unemployment Rate,” Journal of the American Statistical Association 93, 478-493.CrossRefGoogle Scholar
  18. Nelson, C.R. and C.I. Plosser. 1982. “Trends and Random Walks in Macroeconomic Time Series.” Journal of Monetary Economics 10, 139–162.CrossRefGoogle Scholar
  19. Nelson, C. R. 1973. Applied Time Series Analysis for Managerial Forecasts. San Francisco: Holden-Day. Chapters 5 and 6.Google Scholar
  20. Pack, D. J. 1982. “A Practical Overview of ARIMA Models for Time Series Forecasting”, in S. Makridakis and S. C. Wheelwright, Eds. The Handbook of Forecasting: A Manager’s Guide. New York: John Wiley & Sons.Google Scholar
  21. Theil, H. 1966. Applied Economic Forecasting. Amsterdam, North-Holland.Google Scholar
  22. Thomakos, D. and J. Guerard. 2004. “Naïve, ARIMA, Transfer Function, and VAR Models: A Comparison of Forecasting Performance.” The International Journal of Forecasting 20, 53–67.CrossRefGoogle Scholar
  23. West, K. and M. McCracken. 1998. “Regression-based Tests of Predictive Ability.” International Economic Review 39, 817–840.CrossRefGoogle Scholar
  24. Zarnowitz, V. 2004. “The Autonomy of Recent US Growth and Business Cycles”. In P. Dua, Ed., Business Cycles and Economic Growth: An Analysis Using Leading Indicators. New York: Oxford University Press, 44–82.Google Scholar
  25. Zarnowitz, V. 2001. “The Old and the New in the U.S. Economic Expansion.” The Conference Board. EPWP #01–01.Google Scholar
  26. Zarnowitz, V. and A. Ozyildirim. 2001. “On the Measurement of Business Cycles and Growth Cycles.” Indian Economic Review 36, 34–54.Google Scholar
  27. Zarnowitz, V. 1992. Business Cycles: Theory, History, Indicators, and Forecasting. Chicago, University of Chicago Press.Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • John B. GuerardJr.
    • 1
  1. 1.McKinley Capital Management, LLCAnchorageUSA

Personalised recommendations