The measurement of small-scale industry for China's GDP accounts

Wiemer, Calla; Xiuhua, Tian

doi:10.1016/s1043-951x(01)00063-3

Cited by 8 publications

(4 citation statements)

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“…Finally, industrial information is directly associated with GDP. For example, industrial output, energy consumption, the transportation efficiency are all crucial parts of the economy [46]. The stability and accuracy of the regional economic forecasting model can be greatly improved through multivariate data fusion.…”

Section: Multivariate Economic Characteristic Datamentioning

confidence: 99%

A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China

Yan

2022

Sustainability

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Gross domestic product (GDP) is an important index reflecting the economic development of a region. Accurate GDP prediction of developing regions can provide technical support for sustainable urban development and economic policy formulation. In this paper, a novel multi-factor three-step feature selection and deep learning framework are proposed for regional GDP prediction. The core modeling process is mainly composed of the following three steps: In Step I, the feature crossing algorithm is used to deeply excavate hidden feature information of original datasets and fully extract key information. In Step II, BorutaRF and Q-learning algorithms analyze the deep correlation between extracted features and targets from two different perspectives and determine the features with the highest quality. In Step III, selected features are used as the input of TCN (Temporal convolutional network) to build a GDP prediction model and obtain final prediction results. Based on the experimental analysis of three datasets, the following conclusions can be drawn: (1) The proposed three-stage feature selection method effectively improves the prediction accuracy of TCN by more than 10%. (2) The proposed GDP prediction framework proposed in the paper has achieved better forecasting performance than 14 benchmark models. In addition, the MAPE values of the models are lower than 5% in all cases.

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Section: Multivariate Economic Characteristic Datamentioning

confidence: 99%

A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China

Yan

2022

Sustainability

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“…It leads and supports other supporting sectors. It is one of the sectors that greatly contribute to the gross domestic product (GDP) in a region [2]. Industrial sector is strategic and important to realize development goals.…”

Section: Introductionmentioning

confidence: 99%

Pentahelix Model for Eco-Industrial Development: A Collaborative Policy Approach

Wicaksono,

Agustina,

Hidayat

2023

IJSDP

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Currently, industrial sector has been facing challenges in terms issues where the industrial activities impact the environment. The industrial sector in Ngawi Regency is one of the sectors that contributes the fourth largest to the region's economy. This study identifys the factors that influence industrial sectors namely the aspects of revenue growth, sales growth, and production growth by using multiple linear regression analysis and applying Pentahelix Model as a collaboration model towards eco-industrial development. The findings of the study revealed the variables which significantly influenced the aspect of revenue growth namely raw material, capital, and infrastructure. The production growth is affected by raw materials, capital, marketing, production, and institutional. Supported by the result of industrial development factors, Pentahelix synergy is expected to be applied in the practice of ecoindustrial development based on the roles of each actor in Pentahelix.

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“…This is especially the case for coal which is the dominant form of commercial energy. Wiemer and Tian [21] refer to increasing private ownership and indicate the requirement for relying on sample survey methods rather than comprehensive reporting, which was the system for a state-dominated economy. Holz & Lin [4] warn about the 1997-1998 break in industrial statistics which was due to the scope of enterprises covered and the enterprise classification system.…”

Section: Introductionmentioning

confidence: 99%

The estimation of China's economic growth rate

Klein

Özmucur

2003

JEM

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Many scholars of the Chinese economy have concluded that official estimates of China's GDP growth rate are too high. Journalists have popularized this view, but we disagree. We use a method that examines several strategic indicators that are suggested by basic social accounting principles and conclude that principal components of these indicators reflect the movement of official estimates of the Chinese economy. This conclusion holds whether one uses annual, quarterly or monthly indicators. It cannot be claimed that we have proved that GDP as officially measured is correct. No one knows the correct estimate; that is the whole point of showing how different such estimates can be, depending on how they are calculated, and this is true the world over. Our estimates survive diagnostic tests, within-sample interpolations and outside-sample extrapolations in monthly, quarterly, and annual time frames. It is reasonable to expect that introduction of quality adjustments will justify higher estimates.JEL Classification: C51, C82, O53

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The measurement of small-scale industry for China's GDP accounts

Cited by 8 publications

References 0 publications

A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China

A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China

Pentahelix Model for Eco-Industrial Development: A Collaborative Policy Approach

The estimation of China's economic growth rate

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