ℓ1-regularization of high-dimensional time-series models with non-Gaussian and heteroskedastic errors

By Marcelo C. Medeiros, Eduardo F. Mendes. 2016

130 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1a43e3fe6e46adeedc7791708f0b687f11e4b0c6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods (2019, background): “Medeiros & Mendes (2016) provided evidence that LASSO-based models outperform both factor and AR benchmarks in forecasting US CPI.”
Machine Learning Advances for Time Series Forecasting (2020, methodology): “In sparse, high-dimensional, stationary univariate time-series settings, where 𝑛 → ∞ at some rate faster than 𝑇 , Medeiros and Mendes (2016, 2017) show model selection consistency and oracle property of a large set of linear time-series models with difference martingale, strong mixing, and…”
On LASSO for predictive regression (2018, methodology): “In addition to including lags of yi, it is common to accommodate lags of predictors in predictive regressions, for example Medeiros and Mendes (2016). The stationary dependent variable in the following equation is generated from the ARDL model”
NETS: Network estimation for time series (2019, unknown): “This paper is also related to the literature on the estimation of sparse VARs (see Basu & Michailidis, 2015; Davis et al., 2016; Kock & Callot, 2015; Medeiros & Mendes, 2016; Song & Bickel, 2011).”
Estimation and Selection of Spatial Weight Matrix in a Spatial Lag Model (2019, background): “See also Medeiros and Mendes (2016).”
High‐dimensional macroeconomic forecasting and variable selection via penalized regression (2019, background): “For theoretical details on the oracle property for time series models, see Medeiros and Mendes (2016).”