scholarly journals Entropic uncertainty measures for large dimensional hydrogenic systems

2017 ◽  
Vol 58 (10) ◽  
pp. 103302 ◽  
Author(s):  
D. Puertas-Centeno ◽  
N. M. Temme ◽  
I. V. Toranzo ◽  
J. S. Dehesa
Keyword(s):  
Uncertainty Measures

scholarly journals Asymmetric dynamics between uncertainty and unemployment flows in the United States

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ali Ahmed ◽  
Mark Granberg ◽  
Victor Troster ◽  
Gazi Salah Uddin
Keyword(s):  
United States ◽  
Business Cycle ◽  
Negative Impact ◽  
The United States ◽  
Large Uncertainty ◽  
Monthly Data ◽  
Uncertainty Measures ◽  
Linear And Nonlinear ◽  
The Business Cycle ◽  
The U.S

AbstractThis paper examines how different uncertainty measures affect the unemployment level, inflow, and outflow in the U.S. across all states of the business cycle. We employ linear and nonlinear causality-in-quantile tests to capture a complete picture of the effect of uncertainty on U.S. unemployment. To verify whether there are any common effects across different uncertainty measures, we use monthly data on four uncertainty measures and on U.S. unemployment from January 1997 to August 2018. Our results corroborate the general predictions from a search and matching framework of how uncertainty affects unemployment and its flows. Fluctuations in uncertainty generate increases (upper-quantile changes) in the unemployment level and in the inflow. Conversely, shocks to uncertainty have a negative impact on U.S. unemployment outflow. Therefore, the effect of uncertainty is asymmetric depending on the states (quantiles) of U.S. unemployment and on the adopted unemployment measure. Our findings suggest state-contingent policies to stabilize the unemployment level when large uncertainty shocks occur.

scholarly journals Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
No-Wook Park
Keyword(s):  
Environmental Variables ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Predictive Performance ◽  
Environmental Modeling ◽  
Fine Scale ◽  
Coarse Scale ◽  
Uncertainty Measures ◽  
Trmm Precipitation

A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM) data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM) and normalized difference vegetation index (NDVI), the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.

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