Probabilistic Quantile Factor Analysis
This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of \emph{low}, \emph{medium}, and \emph{high} economic policy uncertainty, as well as \emph{loose}, \emph{median}, and \emph{tight} financial conditions. We show that the high uncertainty and tight financial conditions indexes have superior predictive ability for various measures of economic activity. In a high-dimensional exercise involving about 1000 daily financial series, we find that quantile factors also provide superior out-of-sample information compared to mean or median factors.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An Empirical Analysis of Constrained Support Vector Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power
Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas poin…
Decision MakingFormPrediction Intervalsquantile regression+1Quantile Factor Models
Quantile Factor Models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike Approximate Factor Models (AFM), where only location-shifting factors can be extracted, QFM also allow to recove…
Model Selectionquantile regressionvalidSmooth Pinball Neural Network for Probabilistic Forecasting of Wind Power
Uncertainty analysis in the form of probabilistic forecasting can significantly improve decision making processes in the smart power grid for better integrating renewable energy sources such as wind. Whereas point foreca…
Decision MakingFormPrediction Intervalsquantile regression+1Robust Quantile Factor Analysis
We propose a factor model and an estimator of the factors and loadings that are robust to weak factors. The factors can have an arbitrarily weak influence on the mean or quantile of the outcome variable at most quantile …
Composite Quantile Factor Model
This paper introduces the method of composite quantile factor model for factor analysis in high-dimensional panel data. We propose to estimate the factors and factor loadings across multiple quantiles of the data, allowi…
model