scholarly journals The Impact of China's Electricity Deregulation on Coal and Power Industries: Two-Stage Game Modeling Approach

2018 ◽  
Author(s):  
Huihui Liu ◽  
ZhongXiang Zhang ◽  
ZhanMing Chen ◽  
DeSheng Dou
Keyword(s):  
Two Stage ◽  
Modeling Approach ◽  
Electricity Deregulation ◽  
Stage Game ◽  
The Impact

The Impact of Ideology on Disinvestment Decisions of a Coalition

2016 ◽  
Vol 46 (5) ◽  
pp. 821-849
Author(s):  
Ritika Jain ◽  
Shubhro Sarkar
Keyword(s):  
The State ◽  
Theoretic Model ◽  
Two Stage ◽  
Game Theoretic ◽  
Stage Game ◽  
Game Theoretic Model ◽  
The Impact ◽  
Ideological Difference

We build a two-stage game theoretic model to capture the effect of ideologies of parties in a coalition on disinvestment decisions. We focus on three specific aspects of ideology—ideology score of the coalition, ideology dispersion of the coalition, and ideology difference between the center and the state where the enterprise is located. The benchmark two-party coalition predicts that a left government prefers less disinvestment than a right one more often than not. However, there may be a case where moving toward the left end of the ideology spectrum may raise disinvestment incidence. Similarly, a coalition with ideologically similar parties favors privatization more frequently than one in which parties are more diverse. However, for a narrow parametric range, the effect may be reversed. Low ideological difference between the center and the state in which the enterprise is located improves disinvestment incidence. Finally, we extend the model to three-party coalitions.

The impact of China's electricity price deregulation on coal and power industries: Two-stage game modeling

Energy Policy ◽  
2019 ◽  
Vol 134 ◽  
pp. 110957
Author(s):  
HuiHui Liu ◽  
ZhongXiang Zhang ◽  
Zhan-Ming Chen ◽  
DeSheng Dou
Keyword(s):  
Electricity Price ◽  
Two Stage ◽  
Stage Game ◽  
The Impact

A VERTICALLY DIFFERENTIATED DUOPOLY WITH MARGINAL COST DIFFERENTIALS

2003 ◽  
Vol 05 (01) ◽  
pp. 73-81 ◽  
Author(s):  
PAMELA M. SCHMITT
Keyword(s):  
Marginal Cost ◽  
Market Quality ◽  
High Quality ◽  
Two Stage ◽  
Marginal Costs ◽  
Duopoly Model ◽  
Stage Game ◽  
The Impact ◽  
Cost Differentials

A vertically differentiated duopoly model analyzes the impact of marginal cost differentials on price and quality. Firms play a two-stage game, first simultaneously choosing quality and then simultaneously choosing price. When the firm producing the high quality good faces an increase in its marginal cost, both firms increase price and upgrade quality. When the firm producing the low quality good faces an increase in its marginal cost, both firms decrease price and downgrade quality. When a first firm enters the market, quality position decisions are examined under the assumption that both the firm-specific and the quality-specific marginal costs are higher when a firm produces a higher quality good. The results show that the first entrant produces the low quality good and earns higher profits.

The Impact of School Environment and Grade Level on Student Delinquency: A Multilevel Modeling Approach

2020 ◽  
Author(s):  
Celia C. Lo ◽  
Young S. Kim ◽  
Thomas Allen ◽  
Andrea Allen ◽  
P. Allison Minugh ◽  
...  
Keyword(s):  
Multilevel Modeling ◽  
School Environment ◽  
Grade Level ◽  
Modeling Approach ◽  
The Impact

scholarly journals Robust Inferences from a Before-and-After Study with Multiple Unaffected Control Groups

2013 ◽  
Vol 1 (2) ◽  
pp. 209-234 ◽  
Author(s):  
Pengyuan Wang ◽  
Mikhail Traskin ◽  
Dylan S. Small
Keyword(s):  
Sensitivity Analysis ◽  
Control Groups ◽  
Two Stage ◽  
Time Effects ◽  
Unaffected Control ◽  
Group Time ◽  
Before And After ◽  
Group Variation ◽  
The Impact ◽  
Before And After Study

AbstractThe before-and-after study with multiple unaffected control groups is widely applied to study treatment effects. The current methods usually assume that the control groups’ differences between the before and after periods, i.e. the group time effects, follow a normal distribution. However, there is usually no strong a priori evidence for the normality assumption, and there are not enough control groups to check the assumption. We propose to use a flexible skew-t distribution family to model group time effects, and consider a range of plausible skew-t distributions. Based on the skew-t distribution assumption, we propose a robust-t method to guarantee nominal significance level under a wide range of skew-t distributions, and hence make the inference robust to misspecification of the distribution of group time effects. We also propose a two-stage approach, which has lower power compared to the robust-t method, but provides an opportunity to conduct sensitivity analysis. Hence, the overall method of analysis is to use the robust-t method to test for the overall hypothesized range of shapes of group variation; if the test fails to reject, use the two-stage method to conduct a sensitivity analysis to see if there is a subset of group variation parameters for which we can be confident that there is a treatment effect. We apply the proposed methods to two datasets. One dataset is from the Current Population Survey (CPS) to study the impact of the Mariel Boatlift on Miami unemployment rates between 1979 and 1982.The other dataset contains the student enrollment and grade repeating data in West Germany in the 1960s with which we study the impact of the short school year in 1966–1967 on grade repeating rates.

scholarly journals An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels

2020 ◽  
Author(s):  
Chih-Hsiang Yang ◽  
Jaclyn P Maher ◽  
Aditya Ponnada ◽  
Eldin Dzubur ◽  
Rachel Nordgren ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Wearable Sensors ◽  
Activity Levels ◽  
Two Stage ◽  
Modeling Approach ◽  
Source Program ◽  
Modeling Strategy ◽  
Stage Modeling

Abstract People differ from each other to the extent to which momentary factors, such as context, mood, and cognitions, influence momentary health behaviors. However, statistical models to date are limited in their ability to test whether the association between two momentary variables (i.e., subject-level slopes) predicts a subject-level outcome. This study demonstrates a novel two-stage statistical modeling strategy that is capable of testing whether subject-level slopes between two momentary variables predict subject-level outcomes. An empirical case study application is presented to examine whether there are differences in momentary moderate-to-vigorous physical activity (MVPA) levels between the outdoor and indoor context in adults and whether these momentary differences predict mean daily MVPA levels 6 months later. One hundred and eight adults from a multiwave longitudinal study provided 4 days of ecological momentary assessment (during baseline) and accelerometry data (both at baseline and 6 month follow-up). Multilevel data were analyzed using an open-source program (MixWILD) to test whether momentary strength between outdoor context and MVPA during baseline was associated with average daily MVPA levels measured 6 months later. During baseline, momentary MVPA levels were higher in outdoor contexts as compared to indoor contexts (b = 0.07, p < .001). Participants who had more momentary MVPA when outdoors (vs. indoors) during baseline (i.e., a greater subject-level slope) had higher daily MVPA at the 6 month follow-up (b = 0.09, p < .05). This empirical example shows that the subject-level momentary association between specific context (i.e., outdoors) and health behavior (i.e., physical activity) may contribute to overall engagement in that behavior in the future. The demonstrated two-stage modeling approach has extensive applications in behavioral medicine to analyze intensive longitudinal data collected from wearable sensors and mobile devices.

scholarly journals A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Magdalena Murawska ◽  
Dimitris Rizopoulos ◽  
Emmanuel Lesaffre
Keyword(s):  
Empirical Bayes ◽  
Cox Model ◽  
Data Sets ◽  
Two Stage ◽  
Nonlinear Mixed Effects Model ◽  
Resistance Evolution ◽  
Longitudinal Response ◽  
Longitudinal Measurements ◽  
Subject Specific ◽  
The Impact

In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.

The impact of two-stage discharging on the exergoeconomic performance of a storage-type domestic water-heater

Energy ◽  
2015 ◽  
Vol 83 ◽  
pp. 379-386 ◽  
Author(s):  
U. Atikol ◽  
L.B.Y. Aldabbagh
Keyword(s):  
Water Heater ◽  
Domestic Water ◽  
Two Stage ◽  
The Impact ◽  
Storage Type

Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates

2021 ◽  
Vol 38 (3) ◽  
pp. 415-428
Author(s):  
Florian Simon ◽  
Elodie Gautier-Veyret ◽  
Aurélie Truffot ◽  
Marylore Chenel ◽  
Léa Payen ◽  
...  
Keyword(s):  
Modeling Approach ◽  
The Impact
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