JIANGSU SHOWCASES THE CPC'S ROLE IN DRIVING INNOVATION AND ECONOMIC TRANSFORMATION

The sound of Suzhou Pingtan,an art form which combines storytelling and ballad singing in the Suzhou dialect,swirled around a hall of the International Department of theCommunist Party of China’s Central Committee(IDCPC)in Beijing on August 31.The singing enchanted the audience,including 400 visiting political party leaders and ambassadors of

Political Parties Have a Role to Play in Innovating upon in Global Economic Governance:Sidelights of "The CPC in Dialogue with the World 2016"

BETWEEN OCTOBER 13 AND 15,2016,hosted by the International Department,the CPC Central Committee and organized by the CPC Chongqing Municipal Committee,the CPC in Dialogue with the World 2016was held in Chongqing.Themed'Innovation in Global Economic Governance:Initiatives and Actions of Political Parties',the annual event was

Information Models for Forecasting Nonlinear Economic Dynamics in the Digital Era

The aim of this study was to develop an adequate mathematical model for long-term forecasting of technological progress and economic growth in the digital age (2020-2050). In addition, the task was to develop a model for forecast calculations of labor productivity in the symbiosis of “man + intelligent machine”, where an intelligent machine (IM) is understood as a computer or robot equipped with elements of artificial intelligence (AI), as well as in the digital economy as a whole. In the course of the study, it was shown that in order to implement its goals the Schumpeter-Kondratiev innovation and cycle theory on forming long waves (LW) of economic development influenced by a powerful cluster of economic technologies engendered by industrial revolutions is most appropriate for a long-term forecasting of technological progress and economic growth. The Solow neoclassical model of economic growth, synchronized with LW, gives the opportunity to forecast economic dynamics of technologically advanced countries with a greater precision up to 30 years, the time which correlates with the continuation of LW. In the information and digital age, the key role among the main factors of growth (capital, labour and technological progress) is played by the latter. The authors have developed an information model which allows for forecasting technological progress basing on growth rates of endogenous technological information in economics. The main regimes of producing technological information, corresponding to the eras of information and digital economies, are given in the article, as well as the Lagrangians that engender them. The model is verified on the example of the 5th information LW for the US economy (1982-2018) and it has had highly accurate approximation for both technological progress and economic growth. A number of new results were obtained using the developed information models for forecasting technological progress. The forecasting trajectory of economic growth of developed countries (on the example of the USA) on the upward stage of the 6th LW (2018-2042), engendered by the digital technologies of the 4th Industrial Revolution is given. It is also demonstrated that the symbiosis of human and intelligent machine (IM) is the driving force in the digital economy, where man plays the leading role organizing effective and efficient mutual work. Authors suggest a mathematical model for calculating labour productivity in the digital economy, where the symbiosis of “human + IM” is widely used. The calculations carried out with the help of the model show: 1) the symbiosis of “human + IM” from the very beginning lets to realize the possibilities of increasing work performance in the economy with the help of digital technologies;2) the largest labour productivity is achieved in the symbiosis of “human + IM”, where man labour prevails, and the lowest labour productivity is seen where the largest part of the work is performed by IM;3) developed countries may achieve labour productivity of 3% per year by the mid-2020s, which has all the chances to stay up to the 2040s.

The impact of patent citation information flow regarding economic innovation on common stock returns:Volume vs.patent citations

This study examines whether the number of forward patent citations(along with alternative patent data)dwhen used as a proxy for the mixing variabledcould infer the aggregate amount of economic-innovation information arriving at the New York Stock Exchange(NYSE)in the United States.The results show that the number of forward patent citations,when used as a mixing variable,fails to eliminate total volatility persistence in the conditional variance equation of the exponential generalized autoregressive conditional heteroscedastic(EGARCH)model.However,the trading volume successfully eliminates total volatility persistence,thus confirming the validity of the framework used.When the volatility is modeled with an expectation of mean return,the persistence of conditional variance is deterministically increased,and the sum of the volatility coefficients exceeds unity.The inclusion of trading volume with a time trend in the variance equation rectifies the deterministic increase in the conditional volatility.These findings suggest that the form of heteroscedasticity(i.e.,as per the autoregressive conditional heteroscedastic model,ARCH model)in NYSE portfolio returns is based on the type of shocks to volatility(e.g.,deterministic vs.stochastic),which manifests as news arrivals(i.e.,new information arrivals proxied by trading volume)at the stock market.The volume therefore reflects the time dependence in the innovations to the ARCH error generation process.The response of volatility to volume persists over time when the volatility estimates are derived from the EGARCH model with an expectation for the mean of return.Backward patent citations,patent applications,and patents issued have been found to interact somewhat with trading volume,suggesting that each of these variables could play the role of an absorptive capacity variable as the new information flow associated with economic innovation(i.e.,flow of firms’stock of new knowledge)could be picked up by the trading volume.