Complete Subset Averaging for Quantile Regressions
We propose a novel conditional quantile prediction method based on complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified and the dimension of regressors goes to infinity as the sample size increases. Since we average over the complete subsets, the number of models is much larger than the usual model averaging method which adopts sophisticated weighting schemes. We propose to use an equal weight but select the proper size of the complete subset based on the leave-one-out cross-validation method. Building upon the theory of Lu and Su (2015), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987). We check the finite sample performance via Monte Carlo simulations and empirical applications.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Forecasting With Factor-Augmented Quantile Autoregressions: A Model Averaging Approach
This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across mo…
quantile regressionProbabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter
Parametric quantile regressions are a useful tool for creating probabilistic energy forecasts. Nonetheless, since classical quantile regressions are trained using a non-differentiable cost function, their creation using …
Self-Normalized Inference in (Quantile, Expected Shortfall) Regressions for Time Series
This paper proposes valid inference tools, based on self-normalization, in time series expected shortfall regressions and, as a corollary, also in quantile regressions. Extant methods for such time series regressions, ba…
quantile regressionregressionTime SeriesvalidA Simple Estimator for Quantile Panel Data Models Using Smoothed Quantile Regressions
Canay (2011)'s two-step estimator of quantile panel data models, due to its simple intuition and low computational cost, has been widely used in empirical studies in recent years. In this paper, we revisit the estimator …
validUnified 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.
regression