282 research outputs found
Distributional Regression for Data Analysis
Flexible modeling of how an entire distribution changes with covariates is an
important yet challenging generalization of mean-based regression that has seen
growing interest over the past decades in both the statistics and machine
learning literature. This review outlines selected state-of-the-art statistical
approaches to distributional regression, complemented with alternatives from
machine learning. Topics covered include the similarities and differences
between these approaches, extensions, properties and limitations, estimation
procedures, and the availability of software. In view of the increasing
complexity and availability of large-scale data, this review also discusses the
scalability of traditional estimation methods, current trends, and open
challenges. Illustrations are provided using data on childhood malnutrition in
Nigeria and Australian electricity prices.Comment: Accepted for publication in Annual Review of Statistics and its
Applicatio
Does Monetary Policy Affect Stock Market Uncertainty? – Empirical Evidence from the United States
This paper investigates the response of US stock market uncertainty to monetary policy of the Federal Reserve Bank. It can be shown that monetary policy significantly Granger-causes stock market confidence. By using monthly closing prices of the VIX as a stock market uncertainty proxy and a copula-based Markov approach the stable nonlinear relation between confidence and uncertainty is demonstrated. The monetary policy effect on stock market uncertainty is therefore separable into a linear and nonlinear part.Stock market confi dence; temporal dependence; copula
Nonparametric Econometric Methods and Application
The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few
A review of probabilistic forecasting and prediction with machine learning
Predictions and forecasts of machine learning models should take the form of
probability distributions, aiming to increase the quantity of information
communicated to end users. Although applications of probabilistic prediction
and forecasting with machine learning models in academia and industry are
becoming more frequent, related concepts and methods have not been formalized
and structured under a holistic view of the entire field. Here, we review the
topic of predictive uncertainty estimation with machine learning algorithms, as
well as the related metrics (consistent scoring functions and proper scoring
rules) for assessing probabilistic predictions. The review covers a time period
spanning from the introduction of early statistical (linear regression and time
series models, based on Bayesian statistics or quantile regression) to recent
machine learning algorithms (including generalized additive models for
location, scale and shape, random forests, boosting and deep learning
algorithms) that are more flexible by nature. The review of the progress in the
field, expedites our understanding on how to develop new algorithms tailored to
users' needs, since the latest advancements are based on some fundamental
concepts applied to more complex algorithms. We conclude by classifying the
material and discussing challenges that are becoming a hot topic of research.Comment: 83 pages, 5 figure
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