scholarly journals Research on the driving mechanism of tourism regional brand development of tea production base based on the difference-in-difference method

2021 ◽  
Vol 1774 (1) ◽  
pp. 012050
Author(s):  
Yanghao Ye ◽  
Qing Wang ◽  
Jie Zhu ◽  
Wei Ma ◽  
Yajuan Huang
Keyword(s):  
Difference Method ◽  
Driving Mechanism ◽  
Difference In Difference ◽  
Brand Development ◽  
Tea Production ◽  
The Difference ◽  
Regional Brand

scholarly journals PCN30 USING THE DIFFERENCE IN DIFFERENCE METHOD TO UNDERSTAND OUTCOMES IN PROSTATE CANCER

2004 ◽  
Vol 7 (3) ◽  
pp. 255
Author(s):  
WC Lee ◽  
CL Pashos ◽  
J Brandman ◽  
Q Wang ◽  
MF Botteman
Keyword(s):  
Prostate Cancer ◽  
Difference Method ◽  
Difference In Difference ◽  
The Difference

scholarly journals The difference-in-difference method: Assessing the selection bias in the effects of neighborhood environment on health

2014 ◽  
Vol 13 ◽  
pp. 20-33 ◽  
Author(s):  
Irina B. Grafova ◽  
Vicki A. Freedman ◽  
Nicole Lurie ◽  
Rizie Kumar ◽  
Jeannette Rogowski
Keyword(s):  
Selection Bias ◽  
Difference Method ◽  
Neighborhood Environment ◽  
Difference In Difference ◽  
The Difference

scholarly journals The search of the anisotropy of the primary cosmic radiation by the difference method

2017 ◽  
Vol 145 ◽  
pp. 03003
Author(s):  
Victor Pavlyuchenko ◽  
Romen Martirosov ◽  
Natalia Nikolskaya ◽  
Anatoly Erlykin
Keyword(s):  
Difference Method ◽  
Cosmic Radiation ◽  
Primary Cosmic Radiation ◽  
The Difference

THE EFFECT OF INDUSTRIAL REVITALIZATION IN DEVELOPED COUNTRIES ON INDUSTRIAL EXPORTS OF CHINA

2021 ◽  
pp. 1-21
Author(s):  
SIZHUO CHEN
Keyword(s):  
Empirical Evidence ◽  
Difference Method ◽  
Developed Countries ◽  
Difference In Difference ◽  
Proxy Variables ◽  
Over Time

This study analyzes the effects of industrial revitalization in developed countries on China’s industrial exports. Using a rich panel dataset and a difference-in-difference method, I find empirical evidence consistent with the hypothesis that industrial revitalization policies in developed countries discourage China’s industrial exports, and these effects have become more apparent over time. This finding is robust to other proxy variables for industrial revitalization policies and robustness checks.

Study and Simulate the Air Pollution Based on the Finite Difference Method

2012 ◽  
Vol 518-523 ◽  
pp. 2820-2824
Author(s):  
Yi Ni Guo ◽  
Yan Zhang ◽  
Jian Wang ◽  
Ye Huang
Keyword(s):  
Finite Element Method ◽  
Air Pollution ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Difference Method ◽  
The Finite Element Method ◽  
Liquid Diffusion ◽  
Continuous Problems ◽  
The Finite Difference Method ◽  
The Difference

The finite difference method that is the finite element method is used to solve the plane continuous problems. In this article, the theory and method of the finite difference method, as well as the application on the boundary problem are introduced. By analyzing the potential flew field equation and liquid diffusion equation, they are discreted using the difference method and the numerical analysis under certain boundary condition is conducted. In air pollution, the smoke in the diffusion is typical planar continuous problems. In this paper, the finite difference method is used to analyse and simulate the spread of the smoke.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

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