In this paper we develop valid inference for high-dimensional time series. We extend the
desparsified lasso to a time series setting under Near-Epoch Dependence (NED) …

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 …

The concepts of sparsity, and regularised estimation, have proven useful in many high-
dimensional statistical applications. Dynamic factor models (DFMs) provide a parsimonious …

In this paper we establish that conditional mean functions associated with h-step ahead
forecasting equations implied by a factor augmented vector autoregressions (FAVARs) can …

We propose a novel estimation approach for the covariance matrix based on the l 1-
regularized approximate factor model (AFM). Our sparse approximate factor (SAF) …

Technical Appendix: Consistent Estimation, Variable Selection, and Forecasting in Factor-Augmented
VAR Models∗ Page 1 Technical Appendix: Consistent Estimation, Variable Selection, and …

In the context of latent factor models that are widely used in economics, a common
assumption made is one of factor pervasiveness, which implies that all available predictor or …

The classical sample covariance estimator lacks desirable properties such as consistency
and suffers from eigenvalue spreading in high-dimensional settings. Improved estimators …

Inference: α| A, NVAR (p, q), q> 1 yτ= α1Ayτ− 1+...+ αpAyτ− p+ uτ= Xτ (A) α+ uτ, τ= 1: Tτ,
yτ/q= yτ if τ/q∈ N,• Data augmentation. But: point ID not guaranteed; eg for q= 2, p= 1, can …

This paper studies the principal component (PC) method-based estimation of weak factor
models with sparse loadings. We uncover an intrinsic near-sparsity preservation property for …