Time series models are often adopted for forecasting because of their simplicity and good performance. The number of … potentially useful when forecasting. Hence, in this Paper we construct a large macroeconomic data-set for the UK, with about 80 …

aggregating weighted forecasts of the sub-component price indices, versus forecasting the aggregate consumer price index itself … aggregate CPI forecasts, and also offers substantial gains over forecasting using benchmark naïve models. The analysis also … contributes an improved understanding of sectoral inflationary pressures. This forecasting method should be more robust to the …

speed at which interest rates should fall, damaging growth. Our forecasting models for the new measure of producer price …, terms of trade shocks, collapsing oil, food and other commodity prices, and other shocks. Our US and SA forecasting models …

, are estimated for 1974-99 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting …

model forecast should become the benchmark for forecasting horse races. We compare the real-time forecasting accuracy of the … forecasting models is efficient. Our second finding is that there is no single best forecasting method. For example, typically … simple AR models are most accurate at short horizons and DSGE models are most accurate at long horizons when forecasting …

While forecasting is a common practice in academia, government and business alike, practitioners are often left … wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we … time series, and the forecasting performance is often quite sensitive to the choice of such window size. In this paper, we …

produce large reductions in the out-of-sample prediction mean squared error and provides a useful alternative to forecasting …