This paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a …

We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with … forecasting methods can perform better than forecasts based on individual estimates and demonstrate how gains in predictive … stock returns, we show that no single forecasting approach dominates uniformly. However, forecast combination and shrinkage …

This paper is concerned with problem of variable selection and forecasting in the presence of parameter instability …. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or … variable selection and forecasting stages. In this study, we investigate whether or not we should use weighted observations at …

Italy’s domestic tourism flows during the recent years of economic recession. Indeed, the occurrence of closer tourism … dataset is made of inter-regional tourism flows among Italian regions over the 2000 - -12 period; across this period, 2008 to …

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative … forecasting ability when actions are not observable. Deviations become more tempting, reducing profits, but also uncertainty …

This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both … factor-augmented vector autoregression approach, improves forecasting performance for many variables, particularly at short …

For forecasting and economic analysis many variables are used in logarithms (logs). In time series analysis this … beneficial for forecasting. Forecasts based on the original series are compared to forecasts based on logs. It is found that it … substantial forecasting improvements from taking logs are found if the log transformation actually stabilizes the variance of the …