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1 – 8 of 8Feng Yao, Qinling Lu, Yiguo Sun and Junsen Zhang
The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the…
Abstract
The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.
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Yiguo Sun, Raymond J. Carroll and Dingding Li
We consider the problem of estimating a varying coefficient panel data model with fixed-effects (FE) using a local linear regression approach. Unlike first-differenced estimator…
Abstract
We consider the problem of estimating a varying coefficient panel data model with fixed-effects (FE) using a local linear regression approach. Unlike first-differenced estimator, our proposed estimator removes FE using kernel-based weights. This results a one-step estimator without using the backfitting technique. The computed estimator is shown to be asymptotically normally distributed. A modified least-squared cross-validatory method is used to select the optimal bandwidth automatically. Moreover, we propose a test statistic for testing the null hypothesis of a random-effects varying coefficient panel data model against an FE one. Monte Carlo simulations show that our proposed estimator and test statistic have satisfactory finite sample performance.
Identification and inference are central to applied analysis, and two papers examine these issues, the first being theoretical in nature and the second being simulation based.
During the centennial anniversary of Xinhai Revolution in 2011, the Chinese People’s Political Consultative Conference and the State Administration of Radio, Film, and Television…
Abstract
Purpose
During the centennial anniversary of Xinhai Revolution in 2011, the Chinese People’s Political Consultative Conference and the State Administration of Radio, Film, and Television supported the production of 1911 for celebrating such an important event that lead to the rise of the Republic of China in the contemporary Chinese history. This paper aims to reflect upon this film in relation to China’s propagation of “Greater China” for the Empire-building project.
Design/methodology/approach
By scrutinizing the film text and following the strait controversies over the film, this paper demonstrates how the Chinese Communist agents employed the coproduction model with Hong Kong for globalizing a cinematic discourse of Greater China in part of their Empire-building project.
Findings
The study challenges how contemporary Chinese history is ideologically and politically manipulated for advancing the Chinese Communist propaganda over Taiwan. The overall objective is to reflect upon the longstanding historical divergences that stand on the current geopolitical envision and strategy of China for reunification.
Originality/value
This paper provides an interdisciplinary reflection upon the intricate post-Cold War politics in part of the contemporary Chinese cinema under the China–Hong Kong coproduction model. The findings advance novel and timely insights into China’s current envision and strategy for reunification.
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Zicheng Zhang, Xinyue Lin, Shaonan Shan and Zhaokai Yin
This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore…
Abstract
Purpose
This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.
Design/methodology/approach
In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.
Findings
The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.
Originality/value
The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.
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Tushar Vikas Bhaskarwar, Sumit Suhas Aole and Rajan Hari Chile
The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with…
Abstract
Purpose
The purpose of this paper is to provide benefits for companies or organizations, which deal with fewer input-outputs and wanted to control their industrial processes remotely with a robust control strategy.
Design/methodology/approach
In this paper, an active disturbance rejection control (ADRC) strategy is used for the two tank level process plant and it is remotely monitored with the industrial internet of things technology. The disturbances in a primary and secondary loop of the cascade process, which are affecting the overall settling time (ts) of the process, are eliminated by using the proposed, ADRC-ADRC structure in the cascade loop. The stability of the proposed controller is presented with Hurwitz’s stability criteria for selecting gains of observers. The results of the proposed controller are compared with the existing active disturbance rejection control-proportional (ADRC-P) and proportional-integral derivative-proportional (PID-P)-based controller by experimental validation.
Findings
It is observed that the settling time (ts) in the case of the proposed controller is improved by 60% and 55% in comparison to PID-P and ADRC-P, respectively. The level process is interfaced with an industrial controller and real-time data acquired in matrix laboratory (MATLAB), which acted as a remote monitoring platform for the cascade process.
Originality/value
The proposed controller is designed to provide robustness against disturbance and parameter uncertainty. This paper provides an alternate way for researchers who are using MATLAB and ThingSpeak cloud server as a tool for the implementation.
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