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 …

Individuals or companies in a large social or financial network often display rather
heterogeneous behaviors for various reasons. In this work, we propose a network vector …

We propose a dynamic network quantile regression model to investigate the quantile
connectedness using a predetermined network information. We extend the existing network …

Covariance regression offers an effective way to model the large covariance matrix with the
auxiliary similarity matrices. In this work, we propose a sparse covariance regression (SCR) …

This article evaluates the forecast performance of model averaging forecasts in a
nonstationary vector autoregression with mixed roots in the vicinity of unity. The deviation …

Maximum likelihood estimation of large Markov-switching vector autoregressions (MS-
VARs) can be challenging or infeasible due to parameter proliferation. To accommodate …

This paper proposes a dynamic spatial autoregressive quantile model. Using predetermined
network information, we study dynamic tail event driven risk using a system of conditional …

Essays on High-dimensional Econometrics Page 1 Essays on High-dimensional
Econometrics by Guan Yun Kenwin Maung Submitted in Partial Fulfillment of the …

High dimensional time series is gaining considerable attention in recent years. The sparse
vector heterogeneous autoregressive (VHAR) model proposed by Baek and Park (2020) …

This paper proposes a dynamic spatial autoregressive quantile model. Using predetermined
network information, we study dynamic tail event driven risk using a system of conditional …