Analysis of Non-Stationary Time-Series Business Data

Analysis of Non-Stationary Time-Series Business Data

George Saridakis (Kingston University, UK) and Grammatoula Papaioannou (Loughborough University, UK)
Copyright: © 2014 |Pages: 8
DOI: 10.4018/978-1-4666-5202-6.ch010
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Background

In time-series analysis of business and economic data (e.g. stock index data; corporate dividend payments; corporate profits; business start-ups; business survival rates) the statistical concept that has received considerable attention and gained much popularity among applied researchers is the one related to non-stationarity. As discussed in a number of econometrics textbooks (see Verbeek, 2000; Charemza & Deadman, 1997 among others), quantitative analysts are generally concerned with the concept of weak stationarity (or covariance stationarity) i.e. the mean, variances and autocovariances of the series are independent of time; that is E(yt)=c remains constant for all t; var(yt)=E(yt-c)22 remains constant for all t ; and cov(yt, yt+g)=E[(yt-c)(yt+g-c)]g remains constant for all t and g≠0. If one or more of these conditions are not fulfilled the time-series is called non-stationary (this is discussed more analytically in Seddighi et al., 2000).

Key Terms in this Chapter

Error Correction Model: A short-run model that incorporates a mechanism which restores a variable to its long-term relationship from a disequilibrium position.

Econometrics: The quantitative measurement and analysis of actual economic and business phenomena using mathematics and statistical methods.

I(1): A series which becomes stationary after first differencing.

Cointegration: The notion that a linear combination of two I(1) variables is integrated of order zero.

Spurious Regression Problem: A problem that arises when regression analysis indicates a strong relationship between two or more variables when in fact they are totally unrelated.

Stationary Process: A weakly stationary time-series has constant mean, variance and autocovariances over time.

Time Series Data: Data collected over a period such as a month, quarter or year on one or more variables.

Structural Break: An external event which causes a significant alteration to the process.

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