Large factor models use a few latent factors to characterize the co-movement of economic
variables in a high-dimensional data set. High dimensionality brings challenges as well as …

We develop a new technique to estimate vector autoregressions with a common factor error
structure by quantile regression. We apply our technique to study credit risk spillovers …

We study the suitability of applying lasso-type penalized regression techniques to macroe-
conomic forecasting with high-dimensional datasets. We consider the performances of lasso …

This article investigates estimation of sparsity-induced weak factor (sWF) models, with large
cross-sectional and time-series dimensions (N and T, respectively). It assumes that the k th …

Abstract Foreign Direct Investment (FDI) is viewed as a primary driving force in shaping the
global economy and receives particular attention in empirical studies. In this paper, we …

The rapid development of information communication technology (ICT), represented by
mobile phones and the Internet, allows capitalizing to a greater extent on the wealth of …

State-space models (SSMs) offer a powerful framework for dynamical system analysis,
wherein the temporal dynamics of the system are assumed to be captured through the …

The outbreak of war and the earlier and ongoing COVID-19 pandemic determined the need
for real-time monitoring of economic activity. The economic activity of a country can be …

In this study, we consider residual‐based bootstrap methods to construct the confidence
interval for structural impulse response functions in factor‐augmented vector …

This paper proposes an identification strategy for international oil price shocks while
accounting for the heterogeneous sources of oil demand from global, developed, and …