How Modern Time Series Analysis Emerged?

The regression analysis which is basic tool of econometrics was invented in 1880s by Francis Galton, a cousin of Charles Darwin who is famous for his theory of evolution. Like Darwin, Galton was a biologist interested in the laws of heredity, and he intended to use the regression for the laws of heredity. He used the regression for analysis of cross-sectional data of the heights of fathers and sons. The regression analysis was than adapted and developed by the economists for analysis of economic data without any discrimination of time series and cross-sectional data.

Soon it was discovered that the application of regression analysis to the time series data could produced misleading results. In particular, regression analysis applied to time series data sometimes shows the two series to be highly correlated, when in fact there is no sensible economic relationship between the variables. This phenomenon was termed as ‘spurious regression’.

Yule (1926) wrote a detailed commentary on the spurious regression in time series. He gave number of examples in which two independent time series appear to be highly correlated. One of his examples was the relationship in marriages in Church of England and mortality rate. Obviously, the two variables don’t have any causal connection, but Yule fond 95% correlation between two variables. Yule thought that this phenomenon is because of some missing variable and could be avoided by taking into account all relevant variables. He further assumed that spurious regression would disappear if longer time series are available. This means by increasing the time series length, the chances of spurious regression will gradually diminish. In coming half century, the missing variable was thought as the main reason for the spurious correlation in time series.

In 1974, Granger and Newbold observed that in case of non-stationary time series, the spurious regression may exist even if there is no missing variable. They further found that the probability of spurious regression increases by increasing the time series length, contrary to the perception of Yule who had thought that probability of spurious regression will decrease with the increase in time series length. A few years later in 1982, Nelson and Plossor analyzed a set of time series of the United States and found that most of these series are non-stationary. Many other studies supported the finding of Nelson and Plossor creating a doubt about stationarity of time series.

If one combines the finding of Granger and Newbold with that of Nelson and Plossor, the conclusion would be, ‘most of regressions between economic time series are spurious because of non-stationarity of the underlying time series’. Therefore these studies put a big question mark on the validity of regression analysis for time series data.

In a later study, Engle and Granger (1986) found that regression of non-stationary time series could be genuine, if the underlying series are ‘cointegrated’. This means, if you have a set of  time series variables which are non-stationary, you have to ensure that they are cointegrated as well, in order to insure that the regression is no spurious.

If you are running a regression between time series variables, first you have to check the stationarity of the series because as warned by Nelson and Plossor and predecessors, most of the economic time series are non-stationary. If the series are actually non-stationary, than you have to make sure that the series have cointegration as well, otherwise the regression will be spurious.

Therefore, in order to test the validity of a regression analysis for time series, testing for stationarity and cointegration became the preliminary steps in the analysis of time series.  A new stream of literature emerged on focusing on the testing for stationarity and cointegration which give rise to current tools of time series analysis.

References

Galton, F. (1886). “Regression towards mediocrity in hereditary stature”. The Journal of the Anthropological Institute of Great Britain and Ireland. 15: 246–263.

Engle, Robert F.; Granger, Clive W. J. (1987). “Co-integration and error correction: Representation, estimation and testing”. Econometrica. 55 (2): 251–276.

Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120

Nelson, C. R. and Plosser, C. R. (1982). Trends and random walks in macroeconmic time series: some evidence and implications. Journal of monetary economics, 10(2):139– 162

Yule, G. U. (1926). Why do we sometimes get nonsense-correlations between Time-Series?–a study in sampling and the nature of time-series. Journal of the royal statistical society, 89(1), 1-63.