Data for: The judiciary system as a productivity factor; the European experience.

The dataset includes the data for the paper " The judiciary system as a productivity factor; the European experience."THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

On the directional accuracy of inflation forecasts: evidence from South African survey data

We study the information content of South African inflation survey data by determining the directional accuracy of both short-term and long-term forecasts. We use relative operating characteristic (ROC) curves, which have been applied in a variety of fields including weather forecasting and radiology, to ascertain the directional accuracy of the forecasts. A ROC curve summarizes the directional accuracy of forecasts by comparing the rate of true signals (sensitivity) with the rate of false signals (one minus specifity). A ROC curve goes beyond market-timing tests widely studied in earlier research as this comparison is carried out for many alternative values of a decision criterion that discriminates between signals (of a rising inflation rate) and nonsignals (of an unchanged or a falling inflation rate). We find consistent evidence that forecasts contain information with respect to the subsequent direction of change of the inflation rate.http://www.tandfonline.com/loi/cjas20hj2019Economic

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.http://www.tandfonline.com/loi/raec202016-11-30hb2016Economic