The use of large-dimensional factor models in forecasting has received much attention in the literature with the … model which is better suited for forecasting compared to the traditional principal components (PC) approach.We provide an … asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic …

-sample forecasting regressions. The predictive power of the model stays high at longer horizons. The estimated factors are strongly …

variable selection and estimation in one step. We evaluate the forecasting accuracy of these estimators for a large set of …

Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic … simply screen datasets prior to estimation and remove anomalous observations.We investigate whether forecasting performance … Carlo simulation studies. Finally, we apply our proposed estimator in a simulated real-time forecasting exercise to test its …

We address the issue of modelling and forecasting macroeconomic variables using medium and large datasets, by adopting …

, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances … over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit …

reverse yield gap, the credit quality spread) for fore-casting real economic activity and inflation in the euro area. A … quarterly Bayesian vector autoregression model is used to assess the marginal forecasting power of financial spreads for real …

on the state of the Italian economy in the 1990s, taking as a benchmark the forecasting errors generated by the quarterly … forecasting errors in monetary and real variables, thereby taking into account links that may not be accounted for by the modelï …