FISCAL READJUSTMENTS IN THE UNITED STATES: A NONLINEAR TIME-SERIES ANALYSIS

We analyze the fiscal adjustment process in the United States using a multivariate threshold vector error regression model. The shift from single-equation to multivariate setting adds value both in terms of our economic understanding of the fiscal adjustment process and the forecasting performance of nonlinear models. We find evidence that fiscal authorities intervene to reduce real per capita deficit only when it reaches a certain threshold and that fiscal adjustment takes place primarily by cutting government expenditure. The results of out-of-sample density forecast and probability forecasts suggest that a shift from a univariate autoregressive model to a multivariate model improves forecast performance. ( JEL C32, C53, E62)     

Choosing a Model Selection Strategy

An important problem in statistical practice is the selection of a suitable statistical model. Several model selection strategies are available in the literature, having different asymptotic and small sample properties, depending on the characteristics of the data generating mechanism. These characteristics are difficult to check in practice and there is a need for a data-driven adaptive procedure to identify an appropriate model selection strategy for the data at hand. We call such an identification a model metaselection, and we base it on the analysis of recursive prediction residuals obtained from each strategy with increasing sample sizes. Graphical tools are proposed in order to study these recursive residuals. Their use is illustrated on real and simulated data sets. When necessary, an automatic metaselection can be performed by simply accumulating predictive losses. Asymptotic and small sample results are presented.     

Who Leaves Social Work?

Summary Turnover and wastage rates among social work staff are highand subject to marked variation between grades, locations andlocal authorities. Such high rates have a number of deleteriouseffects on the quality of social work, and consequently on clients,on the costs of delivering personal social services and on thegeneral administration and organization of social care. Employingdata on 13,750 basic grade field social workers employed byEnglish and Welsh local authority social services departmentsin 1974–75, turnover and wastage rates were predictedusing information on the personal characteristics of employeesand the workplace locations. The analyses revealed the importanceof age, sex, educational background, social work qualifications,length of service and workplace in determining the probabilitiesof changing social work jobs and leaving social work completely.