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Time Series Modeling

  • Phoebus Dhrymes
Chapter
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Abstract

In this chapter, the reader is introduced to time series modeling. The science (and art) of time series modeling reflects the times series models of Professors Box and Jenkins, Granger, and Hendry. Many economic time series follow near-random walks or random walk with drift processes. This chapter uses the time series modeling of real Gross Domestic product, GDP, as a time series of interest. Time series models can be univariate, where a time series is modeled only by its past values, or multivariate, in which an input series leads an output series, such as a composite index of leading economic indicators, LEI, that can be used as an input to a transfer function model of real Gross Domestic Product, GDP. Economic indicators are descriptive and anticipatory time-series data can be used to analyze and forecast changing business conditions. Cyclical indicators are comprehensive series that are systematically related to the business cycle. Business cycles are recurrent sequences of expansions and contractions in aggregate economic activity. Coincident indicators have cyclical movements that approximately correspond with the overall business cycle expansions and contractions. Leading indicators reach their turning points before the corresponding business cycle turns. The lagging indicators reach their turning points after the corresponding turns in the business cycle.

References

  1. 9.
    Arnott, R. (1985). The use and misuse of consensus earnings. Journal of Portfolio Management, 12, 18–27.CrossRefGoogle Scholar
  2. 10.
    Ashley, R. A., Granger, C. W. J., & Schmalensee, R. L. (1980). Advertising and aggregate consumption: An analysis of causality. Econometrica, 48, 1149–1168.CrossRefGoogle Scholar
  3. 15.
    Ashley, R., & Ye, H. (2012). On the granger causality between median inflation and price dispersion. Applied Economics, 44, 4221–4238.CrossRefGoogle Scholar
  4. 35.
    Bollerslev, T. (1986). Generialized autoregressive conitional heteroskedasticity. Journal of Econometrics, 31, 307–327.CrossRefGoogle Scholar
  5. 36.
    Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26(2), 211–252.Google Scholar
  6. 37.
    Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Oakland: Holden-Day.Google Scholar
  7. 46.
    Burns, A. F., & Mitchell, W. C. (1946). Measuring business cycles. New York: NBER.Google Scholar
  8. 47.
    Castle, J., & Shepard, N. (2009). The methodology and practice of econometrics. Oxford: Oxford University Press.CrossRefGoogle Scholar
  9. 86.
    Dhrymes, P. J., & Guerard Jr., J. B. (2017). Returns, risk, portfolio selection, and evaluation. In J. Guerard (Ed.), Portfolio construction, Measurment, and efficiency: Essays in honor of Jack Tteynor. New York: Springer.Google Scholar
  10. 91.
    Doornik, J. A., & Hendry, D. F. (2009a). Empirical Econometric Modelling. London: Timberlake Consultants, Ltd..Google Scholar
  11. 92.
    Doornik, J. A., & Hendry, D. F. (2009b). Modelling dynamic systems. London: Timberlake Consultants, Ltd.Google Scholar
  12. 93.
    Doornik, J. A. (2009). Autometrics. In J. Castle & N. Shepard (Eds.), The methodology and practice of econometrics. Oxford: Oxford University Press.Google Scholar
  13. 94.
    Doornik, J. A., & Hendry, D. F. (2015). Statistical model selection with “big data”. Cogent Economics & Finance, 3, 1–15.CrossRefGoogle Scholar
  14. 97.
    Dua, P. (2004). Business cycles and economic growth. New Delhi: Oxford University Press in India.Google Scholar
  15. 107.
    Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzman, W. N. (2007). Modern portfolio theory and investment analysis (7th ed.). New York: Wiley.Google Scholar
  16. 109.
    Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimatea of the variance in United Kingdom inflation. Econometrica, 50, 987–1006.CrossRefGoogle Scholar
  17. 110.
    Engle, R. F. (1995). ARCH: Selected readings. New York: Oxford University Press.Google Scholar
  18. 131.
    Fox, A. J. (1972). Outliers in time series. Journal of the Royal Statistical Society,Series B, 34, 350–363.Google Scholar
  19. 132.
    Friedman, M., & Schwartz, A. (1963). Money and business cycles. Review of Economics and Statistics, 45, 32–64.CrossRefGoogle Scholar
  20. 139.
    Granger, C. W. J. (1969). Investigating casual relations by economic models and cross-spectral methods. Econometrica, 37, 424–438.CrossRefGoogle Scholar
  21. 140.
    Granger, C. W. J., & Newbold, P. (1977). Forecasting economic time series. New York: Academic Press.Google Scholar
  22. 142.
    Granger, C. W. J. (1980b). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2, 329–352.CrossRefGoogle Scholar
  23. 144.
    Granger, C. W. J. (1986). Developmentsin the study of Cointegrated economic variables. Oxford Bulletin of Economics and Statistics, 48, 213–228.CrossRefGoogle Scholar
  24. 145.
    Granger, C. W. J. (1989a). Invited review: Combining forecasts-twenty years later. Journal of Forecasting, 8, 167–173.CrossRefGoogle Scholar
  25. 155.
    Guerard Jr., J. B. (2001). A note on the effectiveness of the U.S. leading economic indicators. Indian Economic Review, 36, 251–268.Google Scholar
  26. 157.
    Guerard Jr., J. B. (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.Google Scholar
  27. 182.
    Hendry, D. F., & Nielsen, B. (2007). Econometric modeling: A likelihood approach. Princeton: Princeton University Press.Google Scholar
  28. 183.
    Hendry, D. F., & Doornik, J. A. (2014). Empirical model discovery and theory evaluation. Cambridge: MIT Press.CrossRefGoogle Scholar
  29. 185.
    Hoerl, A. E. (1959). Optimum solution of many variables equations. Chemical Engineering Progress, 55, 69–78.Google Scholar
  30. 192.
    Jagannathan, R., & Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps. Journal of Finance, 58, 1651–1684.CrossRefGoogle Scholar
  31. 193.
    Jenkins, G. M. (1979). Practical experiences with modelling and forecasting tine series. Jersey/Channel Island: A GJP Publication.Google Scholar
  32. 200.
    Koopmans, T. C., & Hood, W. C. (1953). The estimation of simultaneous linear economic relationships. In W. C. Hood & T. C. Koopmans (Eds.)., Chapter 6 Studies in econometric method. New York: Wiley.Google Scholar
  33. 206.
    Lahiri, K., & Wang, J. G. (2013). Evaluating probability forecasts for GDP deciles using alternative methodologies. International Journal of Forecasting, 29, 175–190.CrossRefGoogle Scholar
  34. 214.
    Lee, J. H., & Stefek, D. (2008). Do risk factors eat alphas? Journal of Portfolio Management, 34(4), 12–25.CrossRefGoogle Scholar
  35. 216.
    Levanon, G., Manini, J.-C., Ozyildirim, A., Schaitkin, B., & Tanchua, J. (2016). Using financial indicators to predict turning points in the business cycle: The case of the leading economic index for the United States. International Journal of Forecasting, 31, 427–445.Google Scholar
  36. 243.
    McCracken, M. W. (2007). Asymptotics for out of sample tests of granger causality. Journal of Econometrics, 140(2), 719–752.CrossRefGoogle Scholar
  37. 246.
    Mills, T. C. (1990). Time series techniques for economists. New York: Cambridge University Press.Google Scholar
  38. 247.
    Mincer, J., & Zarnowitz, V. (1969). The evaluation of economic forecasts. In J. Mincer (Ed.), Economic forecasts and expectations. New York: Columbia University Press.Google Scholar
  39. 248.
    Mitchell, W. C. (1913). Business cycles. New York: Burt Franklin reprint.Google Scholar
  40. 249.
    Mitchell, W. C. (1951). What happens during business cycles: A progress report. New York: NBER.Google Scholar
  41. 251.
    Moore, G. H. (1961). Business cycle indicators. 2 volumes. Princeton: Princeton University Press.Google Scholar
  42. 255.
    Nelson, C. R., & Plosser, C. I. (1982). Trends and random walks in macroeconomic time series. Journal of Monetary Economics, 10, 139–162.CrossRefGoogle Scholar
  43. 303.
    Thomakos, D., & Guerard, J. (2004). Naïve, ARIMA, transfer function, and VAR models: A comparison of forecasting performance. The International Journal of Forecasting, 20, 53–67.CrossRefGoogle Scholar
  44. 310.
    Treynor, J. L. (1999). Toward a theory of market value for risky assets. In R. Korajczyk (Ed.), Asset pricing and portfolio performance. London: Risk Books.Google Scholar
  45. 332.
    Ye, H., Ashley, R. A., & Guerard Jr., J. B. (2015). Comparing the effectiveness of traditional vs. mechanized identification methods in post-sample forecasting for a macroeconomic granger causality analysis. The International Journal of Forecasting, 31, 488–500.CrossRefGoogle Scholar
  46. 337.
    Zarnowitz, V. (1992). Business cycles: Theory, history, indicators, and forecasting. Chicago: University of Chicago Press.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Phoebus Dhrymes
    • 1
  1. 1.New YorkUSA

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