Predictive Quantile Regression with Mixed Roots and Increasing Dimensions: The ALQR Approach
In this paper we propose the adaptive lasso for predictive quantile regression (ALQR). Reflecting empirical findings, we allow predictors to have various degrees of persistence and exhibit different signal strengths. The number of predictors is allowed to grow with the sample size. We study regularity conditions under which stationary, local unit root, and cointegrated predictors are present simultaneously. We next show the convergence rates, model selection consistency, and asymptotic distributions of ALQR. We apply the proposed method to the out-of-sample quantile prediction problem of stock returns and find that it outperforms the existing alternatives. We also provide numerical evidence from additional Monte Carlo experiments, supporting the theoretical results.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Selectionquantile regressionregressionVariable SelectionSimilar Papers 제목 키워드 기반
Estimating Conditional Value-at-Risk with Nonstationary Quantile Predictive Regression Models
This paper develops an asymptotic distribution theory for an endogenous instrumentation approach in quantile predictive regressions when both generated covariates and persistent predictors are used. The generated covaria…
regressionBayesian quantile additive regression trees
Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much atten…
Binary ClassificationGeneral Classificationquantile regressionregressionOn Quantile Regression Forests for Modelling Mixed-Frequency and Longitudinal Data
The aim of this thesis is to extend the applications of the Quantile Regression Forest (QRF) algorithm to handle mixed-frequency and longitudinal data. To this end, standard statistical approaches have been exploited to …
quantile regressionregressionUnified Inference for Dynamic Quantile Predictive Regression
This paper develops unified asymptotic distribution theory for dynamic quantile predictive regressions which is useful when examining quantile predictability in stock returns under possible presence of nonstationarity.
regressionInference in Predictive Quantile Regressions
This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and …
quantile regressionregressionUnity