[PDF][PDF] Quantile time series regression models revisited
C Katsouris - arXiv preprint arXiv:2308.06617, 2023 - arxiv.org
arXiv:2308.06617v1 [econ.EM] 12 Aug 2023 Quantile Time Series Regression Models
Revisited* Page 1 arXiv:2308.06617v1 [econ.EM] 12 Aug 2023 Quantile Time Series Regression …
Revisited* Page 1 arXiv:2308.06617v1 [econ.EM] 12 Aug 2023 Quantile Time Series Regression …
Complete subset averaging approach for high-dimensional generalized linear models
X Chen, H Li, J Zhang - Economics Letters, 2023 - Elsevier
This study proposes a novel complete subset averaging (CSA) method for high-dimensional
generalized linear models based on a penalized Kullback–Leibler (KL) loss. All models …
generalized linear models based on a penalized Kullback–Leibler (KL) loss. All models …
Complete subset averaging methods in corporate bond return prediction
T Cheng, S Jiang, AB Zhao, Z Jia - Finance Research Letters, 2023 - Elsevier
We investigate the performances of two methods of complete subset averaging—complete
subset linear averaging (CSLA) and complete subset quantile averaging (CSQA)—on the …
subset linear averaging (CSLA) and complete subset quantile averaging (CSQA)—on the …
Complete subset averaging with many instruments
We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-
weighted average over a complete subset with k instruments among K available, which we …
weighted average over a complete subset with k instruments among K available, which we …
Model-averaging-based semiparametric modeling for conditional quantile prediction
C Guo, W Zhang - Science China Mathematics, 2024 - Springer
In real data analysis, the underlying model is frequently unknown. Hence, the modeling
strategy plays a key role in the success of data analysis. Inspired by the idea of model …
strategy plays a key role in the success of data analysis. Inspired by the idea of model …
Jackknife Partially Linear Model Averaging for the Conditional Quantile Prediction
J Lv - arXiv preprint arXiv:2203.10248, 2022 - arxiv.org
Estimating the conditional quantile of the interested variable with respect to changes in the
covariates is frequent in many economical applications as it can offer a comprehensive …
covariates is frequent in many economical applications as it can offer a comprehensive …
Research on high-dimensional complex data monitoring based on quantile regression
Q Lu, X Zi - … on Applied Mathematics, Modelling, and Intelligent …, 2024 - spiedigitallibrary.org
Quantile regression is a technique used to estimate the conditional quantile of the response
variable. Unlike the classical least squares method, it doesn't assume any specific error …
variable. Unlike the classical least squares method, it doesn't assume any specific error …