Selecting the tuning parameter of the \(\ell_1\) trend filter

DOI10.1515/SNDE-2014-0089OpenAlexW2281324406MaRDI QIDQ2691645

Hiroshi Yamada, Gawon Yoon

Publication date: 30 March 2023

Published in: Studies in Nonlinear Dynamics and Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/snde-2014-0089

zbMATH Keywords

Hodrick-Prescott filtersparsityLassotuning parameter\(\ell_1\) trend filter

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (3)

A new method for specifying the tuning parameter of \(\ell_1\) trend filtering ⋮ Linear asymmetric Laplace fuzzy information granule and its application in short-to-medium term prediction for financial time series ⋮ Sparse factor model based on trend filtering

Uses Software

• CVX

Cites Work

• The solution path of the generalized lasso
• Low frequency filtering and real business cycles
• The Hodrick-Prescott filter: a special case of penalized spline smoothing
• Adaptive piecewise polynomial estimation via trend filtering
• $\ell_1$ Trend Filtering
• The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis
• Convex Analysis
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
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