Advances in the Analysis of Non-stationary Time Series: An Illustration of Cointegration and Error Correction Methods in Research on Crime and Immigration

Abstract

Time series data of interest to social scientists often have the property of random walks in which the statistical properties of the series including means and variances vary over time. Such non-stationary series are by definition unpredictable. Failure to meet the assumption of stationarity in the process of analyzing time series variables may result in spurious and unreliable statistical inferences. This paper outlines the problems of using non-stationary data in regression analysis and identifies innovative solutions developed recently in econometrics. Cointegration and error-correction models have recently received positive attention as remedies to the problems of ``spurious regression'' arising from non-stationary series. In this paper, we illustrate the relevant statistical concepts concerning these methods by referring to similar concepts used in cross-sectional analysis. An historical example is used to demonstrate how such techniques are applied. It illustrates that ``foreign'' immigrants to Canada (1896–1940) experienced elevated levels of social control in areas of high police discretion. ``Foreign'' immigration was unrelated to trends in serious crimes but closely related to vagrancy and drunkenness. The merits of cointegration are compared to traditional approaches to the regression analysis of time series.