CONSISTENT AND CONSERVATIVE MODEL SELECTION WITH THE ADAPTIVE LASSO IN STATIONARY AND NONSTATIONARY AUTOREGRESSIONS
By Anders Bredahl Kock. Econometric Theory, 2015
53 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3a3e13046a729e007efa4ba9c607e7c7e1768634
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In Economics and Econometrics
- Ricardo P. Masini
- Marcelo C. Medeiros
- Vira Semenova
- Eduardo F. Mendes
- Victor Chernozhukov
- Matt Goldman
- Shinya Tanaka
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In Statistics and Probability
- Vira Semenova
- Matt Taddy
- Matt Goldman
- Marcelo C. Medeiros
- Ricardo P. Masini
- Eduardo F. Mendes
- Victor Chernozhukov
- Shinya Tanaka
Citations in the corpus
- NETS: Network estimation for time series (2019, unknown): “…components of yt, the regressors in Equation 8 are likely to be dependent; therefore we adopt here an adaptive LASSO penalty as originally proposed by Zou (2006) and then studied in a high-dimensional setting by Huang, Ma, and Zhang (2008) for independent data and by Kock (2016) for dependent data.”
- Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels. (2017, background): “…(2001), Hahn and Kuersteiner (2002), Fernandez-Val and Lee (2013), Bonhomme and Manresa (2015), Moon and Weidner (2015), Moon and Weidner (2016), Kock (2016b), Manresa (2016), Lu and Su (2016), Su et al. (2016), Moon et al. (2018), Kock and Tang (2019), Bonhomme et al. (2019a), Bonhomme et al.…”
- Machine Learning Advances for Time Series Forecasting (2020, background): “Sang and Sun (2015) study lag estimation of autoregressive processes with long-memory innovations using general penalties and show model selection consistency and asymptotic distribution for the LASSO and SCAD as particular cases.”
- High‐dimensional macroeconomic forecasting and variable selection via penalized regression (2019, background): “Kock (2016) considers adaptive lasso in not only stationary but also nonstationary autoregressions.”
- On LASSO for predictive regression (2018, unknown): “Caner and Knight (2013) and Kock (2016) studied Alasso’s rate adaptiveness in the pure autoregressive setting with iid error processes.”