In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

Bridging factor and sparse models Page 1 The Annals of Statistics 2023, Vol. 51, No. 4,
1692–1717 https://doi.org/10.1214/23-AOS2304 © Institute of Mathematical Statistics, 2023 …

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) …

We develop an LM test for Granger causality in high-dimensional (HD) vector autoregressive
(VAR) models based on penalized least squares estimations. To obtain a test retaining the …

Abstract We propose the Factor Augmented (sparse linear) Regression Model (FARM) that
not only admits both the latent factor regression and sparse linear regression as special …

We propose FNETS, a methodology for network estimation and forecasting of high-
dimensional time series exhibiting strong serial-and cross-sectional correlations. We …

In this paper, we estimate impulse responses by local projections in high-dimensional
settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local …

This paper proposes a penalized Bayesian computational algorithm as an improvement to
the LASSO approach for economic forecasting in multivariate time series. Methodologically …

The success of large-scale models in recent years has increased the importance of
statistical models with numerous parameters. Several studies have analyzed over …

Independent or iid innovations is an essential assumption in the literature for analyzing a
vector time series. However, this assumption is either too restrictive for a real-life time series …