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 …

performs well in very general settings. Finally, we consider two applications: in the first one the goal is to forecast …

-sample forecasting regressions. The predictive power of the model stays high at longer horizons. The estimated factors are strongly … compared to both autoregressive benchmarks and computational intensive forecast combination models. …

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

increases. The proposed estimation method, coupled with dynamic model averaging and selection, is adopted to forecast S&P500 …

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 …

changes that are superior to various alternative forecast procedures based on lower frequency data. …

A two-stage forecasting approach for long memory time series is introduced. In the first step we estimate the … and yields good forecasting results. …