Impacts of the overheating economy on China’s manufacturing industry

Chung, Roy C. P.; Ip, W. H.; Chan, Siu Lai

doi:10.1007/s00170-008-1792-y

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…To our knowledge, this study is the rst to apply the ARIMA intervention time series analysis to evaluate the impact of Ohio's concussion law on rates of concussion-related medical encounters over time among Medicaid-insured children aged ≤18 years. The ARIMA intervention time series model has several advantages over traditional time series analyses, including power, exibility, and increased accuracy of predictions [26][27][28][29]. The ndings suggest that the application of the ARIMA intervention time series analysis may be appropriate for explaining the effect of Ohio's concussion law on concussion-related medical encounters.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Impact of a State Concussion Law using an Autoregressive Integrated Moving Average Intervention Analysis

Huang

Sullivan

Yang

2020

Preprint

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Background: Existing studies analyzing the impact of state concussion laws have found an increase in concussion-related medical encounters post-law, in some instances, such increases were observed during the pre-law period due to a potential “spillover” effect. This study assessed the effects of Ohio’s concussion law, while accounting for such a “spillover” effect, on the trends in monthly rates of concussion-related medical encounters in Medicaid insured children using autoregressive integrated moving average (ARIMA) analysis.Methods: We analyzed claim data obtained from the Partners For Kids database, a pediatric accountable care organization in Ohio. Concussion-related medical encounters for Medicaid-insured children (ages 0-18 years) treated between April 1, 2008 to December 31, 2016 were selected and analyzed. We assessed pre- and post-law trends in concussion-related medical encounters using an ARIMA intervention model. We also used traditional regression methods to validate the study results.Results: A total of 16,943 concussion-related medical encounters sustained by 15,545 unique patients were included. Monthly rates of concussion-related medical encounters significantly increased from 4.64 per 10,000 member months during the pre-law period to 6.69 per 10,000 member months in the post-law period (P < 0.0001). Three upward breaks in the monthly rates of concussion-related medical encounters were observed between 2009 and 2016, with two breaks observed during the pre-law period. Specifically, the increased breakpoint observed in July 2011 (P = 0.0186) was significantly associated with an estimated 7.3% increase (95% CI: 1.1-13.7) in the rate of concussion-related medical encounters. This finding was confirmed in the Poisson regression and curve fitting models. Furthermore, a seasonal trend in concussion-related medical encounters was observed with the highest rates in September and October of each year. Conclusions: Two of the three upward breaks identified in the monthly rate of concussion-related medical encounters occurred before the enactment of Ohio’s concussion law, suggesting a potential “spillover” effect. Further research is needed to confirm such an effect in children with other types of medical insurance.

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Section: Discussionmentioning

confidence: 99%

Analyzing the Impact of a State Concussion Law using an Autoregressive Integrated Moving Average Intervention Analysis

Huang

Sullivan

Yang

2020

Preprint

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“…Liu and Liang (2015) tried to explore the practical intersection of operational management and strategy from the view of resources through evaluating and developing the sustainability of operational competitive advantage. Chung et al (2009) investigated how the recent overheating economy and other economic factors influence the manufacturing industry in China. Liao et al (2015) conclude that the industry either sustains or creates the competitive advantage; it all requires concomitant interaction between innovation and effective market engagement.…”

Section: Research At Homementioning

confidence: 99%

Analysis on the core competitiveness of Shenyang equipment manufacturing industry

Zhang

et al. 2019

IJIMS

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This paper extracts six factors which affect the equipment manufacturing in Shenyang according to the data of local 54 equipment manufacturers with the grounded theory coding method, then measures out the importance of various factors through the multiple linear regression analysis, and analyses the reasons by using statistical analysis software (SPSS). The results reveal the moderate growth in the output from Shenyang equipment manufacturing industry, whose core competitiveness is boosted by the market demand, innovation capability, foreign investment and international markets and inhibited by the company size and personnel management. Based on the conclusions, many methods are proposed to enhance the core competitiveness of the equipment manufacturing industry in Shenyang.

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“…This will provides information for financial analysts, economic forecasters and decision-makers to plan for and cope with sudden movements in the prices of Chinese stock. Previously, the application of ARIMA modeling with and without intervention was implemented in such applications as system scheduling and simulation (Ip, 1997; Ip et al , 1999), tourism forecasting (Du Preez and Witt, 2003, and Cho 2001) investigation and forecast of economic factors (Chung et al , 2008) the financial crisis in China's Manufacturing (Chung et al , 2009) demand in telemarketing centers (Bianchi, Jarrett and Hanumara, 1998) and the impact analysis on air travel demand (Lai and Lu, 2005).…”

Section: Arima Modeling and Intervention Analysismentioning

confidence: 99%

ARIMA Modeling with Intervention to Forecast and Analyze Chinese Stock Prices

Jarrett

Kyper

2011

International Journal of Engineering Business Management

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In this study, we demonstrate the usefulness of ARIMA-Intervention time series analysis as both an analytical and forecast tool. The data base for this study is from the PACAP-CCER China Database developed by the Pacific-Basin Capital Markets (PACAP) Research Center at the University of Rhode Island (USA) and the SINOFIN Information Service Inc, affiliated with the China Center for Economic Research (CCER) of Peking University (China). These data are recent and not fully explored in any published study. The forecasting analysis indicates the usefulness of the developed model in explaining the rapid decline in the values of the price index of Shanghai A shares during the world economic debacle stating in China in 2008. Explanation of the fit of the model is described using the latest development in statistical validation methods. We note that the use of a simpler technique although parsimonious will not explain the variation properly in predicting daily Chinese stock prices. Furthermore, we infer that the daily stock price index contains an autoregressive component; hence, one can predict stock returns.

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Impacts of the overheating economy on China’s manufacturing industry

Cited by 8 publications

References 15 publications

Analyzing the Impact of a State Concussion Law using an Autoregressive Integrated Moving Average Intervention Analysis

Analyzing the Impact of a State Concussion Law using an Autoregressive Integrated Moving Average Intervention Analysis

Analysis on the core competitiveness of Shenyang equipment manufacturing industry

ARIMA Modeling with Intervention to Forecast and Analyze Chinese Stock Prices

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