Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions

By Laurent Callot, Anders Bredahl Kock. 2012

31 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics

How researchers see this paper

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

In Demography

1. Laurent Callot
2. Anders Bredahl Kock
3. Marcelo C. Medeiros
4. Eduardo F. Mendes
5. Ricardo P. Masini

In Economics and Econometrics

1. Ricardo P. Masini
2. Eduardo F. Mendes
3. Marcelo C. Medeiros
4. Laurent Callot
5. Anders Bredahl Kock
6. Bruno Ferman
7. Michael Mcaleer
8. P. R. Hansen

Citations in the corpus

• Machine Learning Advances for Time Series Forecasting (2020, background): “In high-dimensional settings, where the dimension of the series increase with the number of observations, Kock and Callot (2015) and Basu and Michailidis (2015) show oracle bounds and model selection consistency for the LASSO in Gaussian 𝑉𝐴𝑅(𝑝) models, extending previous works.”
• Oracle Inequalities for High Dimensional Vector Autoregressions (2012, methodology): “…many dependent random covariates. vi) similar results for autoregressions follow as a special case by simply setting k T = 1 in our theorems. vii) in Callot and Kock (2014) we show how the theory put forward in this paper can be used to analyze and forecast a large macroeconomic data set; using…”