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 …

We model a large panel of time series as a vector autoregression where the autoregressive
matrices and the inverse covariance matrix of the system innovations are assumed to be …

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 asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-
dimensional, linear time-series models. The adaLASSO is a one-step implementation of the …

The vector autoregressive (VAR) model is a powerful tool in learning complex time series
and has been exploited in many fields. The VAR model poses some unique challenges to …

Realized volatility computed from high-frequency data is an important measure for many
applications in finance, and its dynamics have been widely investigated. Recent notable …

Explanatory variables in a predictive regression typically exhibit low signal strength and
various degrees of persistence. Variable selection in such a context is of great importance …

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 …

The speed with which a learning algorithm converges as it is presented with more data is a
central problem in machine learning—a fast rate of convergence means less data is needed …

Return predictability has been one of the central research questions in finance for many
decades. This paper proposes a predictive regression with multiple structural changes to …