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

includes (i) Gaussian shocks with stochastic variance, (ii) jumps up and down in the exchange rate, and (iii) jumps in the … probability of jumps in variance is increasing in the variance but not related to interest rates. Many of the jumps in exchange … rates are associated with macroeconomic and political news, but jumps in variance are not. Overall, jumps account for 25% of …

model. The joint significance of MY and dpt in long-horizon forecasting regressions for market returns explain the mixed …

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

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 …