Penalized Convex Estimation in Dynamic Location-Scale models

Reda Alami Chentoufi

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces a two-step procedure for convex penalized estimation in dynamic location-scale models. The method uses a consistent, non-sparse first-step estimator to construct a convex Weighted Least Squares (WLS) optimization problem compatible with the Least Absolute Shrinkage and Selection Operator (LASSO), addressing challenges associated with non-convexity and enabling efficient, sparse estimation. The consistency and asymptotic distribution of the estimator are established, with finite-sample performance evaluated through Monte Carlo simulations. The method's practical utility is demonstrated through an application to electricity prices in France, Belgium, the Netherlands, and Switzerland, effectively capturing seasonal patterns and external covariates while ensuring model sparsity.

Keywords: Weighted LSE; LASSO estimation; variable selection; GARCH models (search for similar items in EconPapers)
JEL-codes: C01 C22 C51 C52 C58 (search for similar items in EconPapers)
Date: 2024-12
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:123283

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