Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Göbel, Maximilian"'
Machine learning predictions are typically interpreted as the sum of contributions of predictors. Yet, each out-of-sample prediction can also be expressed as a linear combination of in-sample values of the predicted variable, with weights correspondi
Externí odkaz:
http://arxiv.org/abs/2412.13076
Autor:
Andorra, Alexandre, Göbel, Maximilian
Evaluating a soccer player's performance can be challenging due to the high costs and small margins involved in recruitment decisions. Raw observational statistics further complicate an accurate individual skill assessment as they do not abstract fro
Externí odkaz:
http://arxiv.org/abs/2412.05911
Timely monetary policy decision-making requires timely core inflation measures. We create a new core inflation series that is explicitly designed to succeed at that goal. Precisely, we introduce the Assemblage Regression, a generalized nonnegative ri
Externí odkaz:
http://arxiv.org/abs/2404.05209
When it comes to stock returns, any form of predictability can bolster risk-adjusted profitability. We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictab
Externí odkaz:
http://arxiv.org/abs/2306.05568
We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice. We first use them to evaluate the simple feature-engineered linear regr
Externí odkaz:
http://arxiv.org/abs/2206.10721
Autor:
Diebold, Francis X., Rudebusch, Glenn D., Goebel, Maximilian, Coulombe, Philippe Goulet, Zhang, Boyuan
Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the
Externí odkaz:
http://arxiv.org/abs/2203.04040
Autor:
Göbel, Maximilian, Tavares, Nuno
Extraordinary fiscal and monetary interventions in response to the COVID-19 pandemic have revived concerns about zombie prevalence in advanced economies. Within a sample of publicly listed U.S. companies, we find zombie prevalence and zombie-lending
Externí odkaz:
http://arxiv.org/abs/2201.10524
Stips, Macias, Coughlan, Garcia-Gorriz, and Liang (2016, Nature Scientific Reports) use information flows (Liang, 2008, 2014) to establish causality from various forcings to global temperature. We show that the formulas being used hinges on a simplif
Externí odkaz:
http://arxiv.org/abs/2103.10605
Autor:
Diebold, Francis X., Rudebusch, Glenn D., Göbel, Maximilian, Goulet Coulombe, Philippe, Zhang, Boyuan
Publikováno v:
In Journal of Econometrics February 2024 239(1)
On September 15th 2020, Arctic sea ice extent (SIE) ranked second-to-lowest in history and keeps trending downward. The understanding of how feedback loops amplify the effects of external CO2 forcing is still limited. We propose the VARCTIC, which is
Externí odkaz:
http://arxiv.org/abs/2005.02535