Non-stationarity and ARIMA(p,d,q) Processes

  • John D. Levendis
Part of the Springer Texts in Business and Economics book series (STBE)


Up until now we have been looking at time series whose means did not exhibit long-run growth. It is time to drop this assumption. After all, many economic and financial time series do not have a constant mean. Examples include: the US GDP per capita, the US CPI, the Dow-Jones Industrial Index, and the share price of Google.


  1. Durlauf, S. N., & Phillips, P. C. (1988). Trends versus random walks in time series analysis. Econometrica: Journal of the Econometric Society, 56, 1333–1354.CrossRefGoogle Scholar
  2. Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120.CrossRefGoogle Scholar
  3. Makridakis, S., & Hibon, M. (2000). The m3-competition: Results, conclusions and implications. International Journal of Forecasting, 16(4), 451–476.CrossRefGoogle Scholar
  4. Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., et al. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111–153.CrossRefGoogle Scholar
  5. Makridakis, S., Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K., et al. (1993). The m2-competition: A real-time judgmentally based forecasting study. International Journal of Forecasting, 9(1), 5–22.CrossRefGoogle Scholar
  6. Makridakis, S., Hibon, M., & Moser, C. (1979). Accuracy of forecasting: An empirical investigation. Journal of the Royal Statistical Society. Series A (General), 142, 97–145.CrossRefGoogle Scholar
  7. Meese, R. A., & Rogoff, K. (1983a). Empirical exchange rate models of the seventies: Do they fit out of sample? Journal of International Economics, 14(1–2), 3–24.CrossRefGoogle Scholar
  8. Meese, R. A., & Rogoff, K. (1983b). The out-of-sample failure of empirical exchange rate models: Sampling error or misspecification? In Exchange rates and international macroeconomics (pp. 67–112). Chicago: University of Chicago Press.Google Scholar
  9. Nelson, C. R. (1972). The prediction performance of the FRB-MIT-Penn model of the US economy. American Economic Review, 62(5), 902–917.Google Scholar
  10. Phillips, P. C. (1986). Understanding spurious regressions in econometrics. Journal of Econometrics, 33(3), 311–340.CrossRefGoogle Scholar
  11. Plosser, C. I., & Schwert, G. W. (1977). Estimation of a non-invertible moving average process: The case of overdifferencing. Journal of Econometrics, 6(2), 199–224.CrossRefGoogle Scholar
  12. Vigen, T. (2015). Spurious correlations. New York: Hachette Books.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • John D. Levendis
    • 1
  1. 1.Department of EconomicsLoyola University New OrleansNew OrleansUSA

Personalised recommendations