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

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.     

Interrelated Factor Demands and Technological Change: An Empirical Assessment of Adjustment for Sectors of the Dutch Economy

Using a translog cost functional form, a formal operational model with an adjustment process according to a first-order autoregressive scheme is presented, that allows the simultaneous determination of factor demands and of technological change in an input–output system. The analysis is carried out for sectors of the Dutch economy over the period 1954–83 and it reveals evidence of the dependence of factor demands on relative prices and on technological change.     

Decomposition of Technological Change and Factor Bias in Indian Power Sector: An Unbalanced Panel Data Approach

Technological change and factor bias in the Indian power sector are analyzed using a translog cost function. Various components of technological progress and factor bias are identified and estimated, using a 21 year unbalanced panel data of Indian states and union territories. Heterogeneity across states is incorporated in the model using a variance component model. Appropriate corrections are made for unbalanced panel data. Empirical results show that the annual average rate of technological progress has been 2.4% for the country as a whole. Accumulation of knowledge and increasing scale are found to be the major factors contributing to technological progress. In contrast, the effects of factor price changes and fixed capital accumulation on technological progress have been unfavorable. Pure factor bias measure indicate saving in the use of fuel and labor, and increased use of materials. Tests are performed to check the curvature properties of the underlying technology.     

Structural Models of Factor Demands and Technological Change: An Empirical Assessment of Dynamic Adjustment Specifications for Sectors of the Dutch Economy

In this paper, we combine a translog cost functional form with an adjustment process according to the error correction mechanism to explain the simultaneous determination of factor demands and technological change. To save degrees of freedom in the estimation procedure, we also consider the imposition of restrictions on the matrices of lag parameters and/or the covariance matrix of the disturbances. Using a model selection strategy based on a combination of economic-theoretical considerations and a formal model selection criterion, a model is selected for each of 17 sectors of the Dutch economy. It turns out that, for 14 of the 17 sectors under consideration, a model is chosen that allows the imposition of restrictions with respect to the matrices of lag parameters. A comparison of the present results with those obtained by Lesuis and de Boer reveals that the application of more general dynamic structures leads to results that are more in accordance with economic theory.     

Restricted forecasting with VAR models: An analysis of a test for joint compatibility between restrictions and forecasts

A restricted forecasting compatibility test for Vector Autoregressive Error Correction models is analyzed in this work. It is shown that a variance–covariance matrix associated with the restrictions can be used to cancel out model dynamics and interactions between restrictions. This allows us to interpret the joint compatibility test as a composition of the corresponding single restriction compatibility tests. These tests are useful for appreciating the contribution of each and every restriction to the joint compatibility between the whole set of restrictions and the unrestricted forecasts. An estimated process adjustment for the test is derived and the resulting feasible joint compatibility test turns out to have better performance than the original one. An empirical illustration of the usefulness of the proposed test makes use of Mexican macroeconomic data and the targets proposed by the Mexican Government for the year 2003.