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 …

In this paper long-run forecasting of multicointegrating variables is investigated. Multicointegration typically occurs …" restriction on out-of-sample valuation and forecasting accuracy of such variables is of interest. In particular, we compare the … long-run forecasting performance of the multicointegrated variables between a model that correctly imposes the "common …

market volatility by macroeconomic and financial variables. We go beyond forecasting stock market volatility (by large the …

This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the …