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 …

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

forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare …

We propose a parametric state space model with accompanying estimation and forecasting framework that combines long … process, the model consistently belongs to the 10% Model Confidence Set when considering out-of-sample forecasting performance … as the only one among four competing dynamic models for all forecasting horizons when applied to high frequency stock …

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