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 …

Predicting the dependencies between observations from multiple time series is critical for
applications such as anomaly detection, financial risk management, causal analysis, or …

This paper establishes non-asymptotic oracle inequalities for the prediction error and
estimation accuracy of the LASSO in stationary vector autoregressive models. These …

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 study examines high-dimensional forecasting and variable selection via folded-
concave penalized regressions. The penalized regression approach leads to sparse …

We propose a new approach to mixed-frequency regressions in a high-dimensional
environment that resorts to Group Lasso penalization and Bayesian estimation and …

We provide a shrinkage type methodology which allows for simultaneous model selection
and estimation of vector error correction models (VECM) when the dimension is large and …

In this chapter we investigate how the possible presence of unit roots and cointegration
affects forecasting with Big Data. As most macroeoconomic time series are very persistent …

This chapter covers penalized regression in the framework of linear time series models and
reviews the most commonly used penalized estimators in applied work, namely Ridge …

A (conservative) test is constructed to investigate the optimal lag structure for forecasting
realized volatility dynamics. The testing procedure relies on the recent theoretical results that …