Machine learning advances for time series forecasting
RP Masini, MC Medeiros… - Journal of economic …, 2023 - Wiley Online Library
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 …
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …
Bridging factor and sparse models
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 …
1692–1717 https://doi.org/10.1214/23-AOS2304 © Institute of Mathematical Statistics, 2023 …
[HTML][HTML] Lasso inference for high-dimensional time series
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) …
desparsified lasso to a time series setting under Near-Epoch Dependence (NED) …
Granger causality testing in high-dimensional VARs: A post-double-selection procedure
A Hecq, L Margaritella… - Journal of Financial …, 2023 - academic.oup.com
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 …
(VAR) models based on penalized least squares estimations. To obtain a test retaining the …
Are latent factor regression and sparse regression adequate?
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 …
not only admits both the latent factor regression and sparse linear regression as special …
FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series
We propose FNETS, a methodology for network estimation and forecasting of high-
dimensional time series exhibiting strong serial-and cross-sectional correlations. We …
dimensional time series exhibiting strong serial-and cross-sectional correlations. We …
Local projection inference in high dimensions
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 …
settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local …
Penalized Bayesian Approach-Based Variable Selection for Economic Forecasting
A Pacifico, D Pilone - Journal of Risk and Financial Management, 2024 - mdpi.com
This paper proposes a penalized Bayesian computational algorithm as an improvement to
the LASSO approach for economic forecasting in multivariate time series. Methodologically …
the LASSO approach for economic forecasting in multivariate time series. Methodologically …
Benign overfitting in time series linear model with over-parameterization
S Nakakita, M Imaizumi - arXiv preprint arXiv:2204.08369, 2022 - arxiv.org
The success of large-scale models in recent years has increased the importance of
statistical models with numerous parameters. Several studies have analyzed over …
statistical models with numerous parameters. Several studies have analyzed over …
Statistical inference of high-dimensional vector autoregressive time series with non-iid innovations
Y Zhang - arXiv preprint arXiv:2310.07364, 2023 - arxiv.org
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 …
vector time series. However, this assumption is either too restrictive for a real-life time series …