Results 121 to 130 of about 6,156 (248)
Mixing It Up: Inflation at Risk
Abstract Understanding how risk factors shape the economic outlook is essential for guiding policy decisions. This paper develops a flexible framework that decomposes distributional risk forecasts of macro‐economic variables into underlying contributions and supports the construction of interpretable risk measures.
MAXIMILIAN SCHRÖDER
wiley +1 more source
ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley +1 more source
Aim: This study examines how management reports affect stock liquidity and compares the responses of stocks with low, medium, and high liquidity. Methodology: Stocks were classified into three liquidity groups based on the median Amihud (ILLIQ) ratio ...
Tohid Zeinali, Jan Makary Fryczak
doaj +1 more source
Density‐Valued ARMA Models by Spline Mixtures
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source
Testing Distributional Granger Causality With Entropic Optimal Transport
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses.
Shuanghua Luo, Yuxin Yan, Cheng-yi Zhang
doaj +1 more source
Solving Stochastic Climate‐Economy Models: A Deep Least‐Squares Monte Carlo Approach
ABSTRACT Stochastic versions of recursive integrated climate‐economy assessment models are essential for studying and quantifying policy decisions under uncertainty. However, as the number of state variables and stochastic shocks increases, solving these models via deterministic grid‐based dynamic programming (e.g., value‐function iteration/projection ...
Aleksandar Arandjelović +4 more
wiley +1 more source
Parameter Estimation of the Partially Linear Quantile Regression Model Under Monotonic Constraints
The paper brings forward the partially linear quantile regression model by incorporating monotonic constraints, which are common in real-world relationships between variables.
Shujin Wu +3 more
doaj +1 more source
Nonuniform temperature and light relationships of moss‐associated nitrogen fixation across climates
Light, moisture, and temperature response of nitrogen fixation in moss–cyanobacteria associations across five climatically contrasting ecosystems. Responses of nitrogen fixation in moss–cyanobacteria associations to light (μmol photons m−2 s−1), moss relative water content (%), and temperature (°C) across five climatically contrasting ecosystems ...
Yunyao Ma, Kathrin Rousk
wiley +1 more source
Powerful nonparametric checks for quantile regression
We address the issue of lack-of-fit testing for a parametric quantile regression. We propose a simple test that involves one-dimensional kernel smoothing, so that the rate at which it detects local alternatives is independent of the number of covariates.
Maistre, Samuel +2 more
openaire +2 more sources

