中国科学院数学与系统科学研究院期刊网

27 January 2025, Volume 5 Issue 1
    

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  • LIN Jianhao, SUN Lexuan
    China Journal of Econometrics. 2025, 5(1): 1-34. https://doi.org/10.12012/CJoE2024-0208
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    Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.
  • KONG Yanlei, QIN Yichen, LI Yang
    China Journal of Econometrics. 2025, 5(1): 35-51. https://doi.org/10.12012/CJoE2024-0425
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    The accuracy of stock return prediction has a critical impact on investment decisions. The advent of deep learning models has markedly improved the accuracy of return forecasts. However, stock market sequences are often observed with anomalies that can distort key statistical measures, obscure the true trends of the data, and diminish the predictive capabilities of deep learning models. In extreme cases, these anomalies can result in erroneous investment decisions. Based on the presence of anomalies and the learning dynamics of gradient descent algorithms, this paper introduces a novel loss function, the threshold distance weighted loss (TDW), which is designed to mitigate the susceptibility of the model to outliers by assigning variable weights to data samples. The TDW loss function has been tested through simulation studies and empirical analysis. These evaluations have confirmed the improved predictive accuracy and robustness of the method, highlighting its potential to deliver consistent positive returns to investment portfolios and to bolster informed financial investment decisions.
  • YU Xing, FAN Ying, JIN Hao
    China Journal of Econometrics. 2025, 5(1): 52-80. https://doi.org/10.12012/CJoE2024-0220
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    In the process of low-carbon transition, enterprises require substantial financial support for related investments. Therefore, the effectiveness of carbon pricing policies depends on a well-functioning financial market. However, in reality, financial markets face various frictions that hinder the flow of capital, leading to inefficient allocation of resources. These frictions may affect corporate investment behavior, thereby weakening the implementation effects of carbon pricing policies. This paper, focusing on the issue of financing constraints, constructs an environmental-dynamic stochastic general equilibrium (E-DSGE) model incorporating a financing collateral constraint mechanism to analyze the impact of financing constraints on the effectiveness of carbon pricing policies and explores corresponding policy responses. The results show that: 1) From the perspective of environmental benefits, financing constraints weaken the “emission reduction effect” of carbon pricing policies, suppress corporate low-carbon investments, and reduce corporate emission intensity; 2) From the perspective of economic costs, financing constraints amplify the cost impact of carbon pricing on enterprises, restrict output growth, and increase the overall economic cost of the low-carbon transition; 3) Introducing carbon asset-backed loans as a complementary measure to carbon pricing policies can effectively mitigate the negative impact of financing constraints on carbon pricing policies; 4) Numerical simulation shows that financing constraints increase the proportion of carbon pricing-related costs in enterprises' total production costs from an average of 15.31% to 19.47% annually, while reducing the annual average scale of low-carbon investments by approximately 37%. Furthermore, providing more carbon asset-backed loans to high-emission enterprises can significantly enhance policy benefits. The conclusions of this paper are of great significance for improving mechanisms for green and low-carbon development and establishing a systematic climate policy framework.
  • ZHAO Xiangqin, ZHAO Chao, CHEN Guojin
    China Journal of Econometrics. 2025, 5(1): 81-108. https://doi.org/10.12012/CJoE2025-0001
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    In order to explore how green technology innovation and the development of the digital economy can jointly promote green economic growth, this paper constructs a general equilibrium model that includes green technology innovation and digital transition. Combining with the real-world data at the city level in China, from the two aspects of economic growth and carbon emissions, it analyzes the impact and the mechanism of action of the digital economy collaborating with green technology innovation on green economic growth. It found that: 1) Green technology innovation has a “U-shaped” impact on economic growth and carbon emissions. That is, after exceeding a specific threshold of technological innovation level, with the continuous increase in the level of green technology innovation, the economic growth rate will continuously increase, and carbon emissions will continue to decrease. Moreover, the development of digital economy will strengthen the impact of green technology innovation, resulting in a steeper “U-shaped” relationship. 2) The development of economic digitalization has both mediating and moderating effects. Green technology innovation has a positive “U-shaped” impact on the development of the digital economy. That is, an increase in green technology innovation can promote the development of digital economy. In turn, the development of digital economy further moderates the impact of green technology innovation on economic growth and carbon emission reduction, strengthening the positive effect of green technology innovation on green economic growth. 3) The digital economy's enhancement of the impact of green technology innovation on green total factor productivity is the primary mechanism by which the digital economy, in collaboration with green technology innovation, drives green economic growth. 4) Policies to promote the development of economic digitalization need to be accompanied by higher carbon taxes. Although there are short-term economic costs, there are advantages in terms of long-term economic growth and environmental quality. This research combines the study of the green transition of economic development with that of digital transition, providing crucial theoretical support for the coordinated advancement of the green and digital transition of the economy to ensure stable economic growth.
  • LIU Yu, LIANG Dong, ZHANG Shuo
    China Journal of Econometrics. 2025, 5(1): 109-128. https://doi.org/10.12012/CJoE2024-0270
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    The open sharing of data resources is key to unlocking the value of data elements. Assessing the impact of government data openness on corporate sustainable development is of significant importance for promoting high-quality economic and social development. This paper uses the openness of government data as a quasi-natural experiment, taking Chinese listed companies from 2009 to 2022 as the research sample, and explores the impact of government data openness on corporate economic performance and environmental performance through the difference-in-differences model. The study demonstrates that government data openness has brought dual benefits to corporate economic and environmental performance, that is, government data openness has promoted corporate sustainable development. The reason is that government data openness can promote corporate technological innovation and improve the efficiency of corporate operation and management. Further research finds that the role of government data openness in promoting economic performance is more significant in state-owned enterprises, regions with a better business environment, and areas with better digital infrastructure conditions, while the enhancement of environmental performance is more fully demonstrated in state-owned enterprises, non-heavy polluting enterprises, and regions with higher environmental regulation intensity. This study reveals the role of government data openness in improving corporate economic and environmental performance, providing important empirical insights for promoting sustainable economic and social development and enhancing the scientific formulation of data openness policies in the context of “Dual Carbon” goals.
  • LI Xinru, JIANG Xuemei, YANG Cuihong
    China Journal of Econometrics. 2025, 5(1): 129-147. https://doi.org/10.12012/CJoE2024-0356
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    Since the global financial crisis in 2008, the United States has actively promoted the reshoring of manufacturing, creating development opportunities for domestic manufacturing while profoundly impacting the global industrial structure and layout, particularly posing significant challenges to the development of China's manufacturing industry. Based on the multi-regional input-output data in constant prices from 2009 to 2022 released by the Asian Development Bank, this paper measures the dynamic evolution of the manufacturing international competitiveness of China and the United States under the background of the U.S. manufacturing reshoring policies. It also employs a structural decomposition model to explore the driving factors and their contributions to the changes in the manufacturing international competitiveness with different technological levels at various stages. The study finds that: 1) The U.S. manufacturing reshoring policy has achieved certain effects in dealing with the issue of industrial hollowing, increasing employment, reducing supply chain risks, and improving economic conditions; 2) From 2009 to the present, on a global scale, the international competitiveness of U.S. manufacturing has not seen a significant improvement, while China's manufacturing international competitiveness has continued to strengthen, with the trend of industrial transformation and upgrading continues; 3) In recent years, the competition between China and the U.S. in the fields of medium and high-tech manufacturing has intensified; 4) The decomposition of international competitiveness indicates that the resilience of supply chains, the evolution of traditional trade patterns, and the reconstruction of global value chains are beneficial for enhancing China's manufacturing international competitiveness; whereas domestic drivers that could consolidate and strengthen the comparative advantage of high-tech manufacturing in the U.S. have not been formed. The empirical results of this paper provide implications for how China can further enhance the international competitiveness of its manufacturing industry.
  • REN Xiaohang, FU Chenjia, ZHOU Ling, YANG Xiaoguang, LU Zudi
    China Journal of Econometrics. 2025, 5(1): 148-170. https://doi.org/10.12012/CJoE2024-0276
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    The structure of the financial system is constantly changing under the impact of the macro environment, and risk spillover is the key to analyze systemic risk. In order to break through the dimension limitation and model specification of traditional parametric models, this paper proposes a semiparametric method, Dynamic Bayesian-Local Gaussian Correlation Network (DBN-LGCNET) to measure the time-varying nonlinear correlation between the general and tail risks. The model is applied to the data of 65 listed financial institutions in China's A-share market, and the results show that: 1) There are obvious tail risk spillovers in the financial system. 2) Risk spillover in the financial industry display heterogeneity, with the source of general risk propagation mainly in the banking sector and the source of tail risk propagation mainly in the securities sector. 3) Risks propagate dynamically among financial institutions, state-owned banks demonstrate a consistent capacity to absorb risk spillovers, whereas small and medium-sized banks show a lesser ability to cope with extreme events. 4) After an extreme event, the impact of the banking industry in the general correlation network is enhanced and the impact of the securities industry is weakened. Links between financial institutions in the tail correlation network are strengthened, especially insurance institutions.
  • ZHU Mengchen, HAN Xiaoyi, LI Muyi
    China Journal of Econometrics. 2025, 5(1): 171-196. https://doi.org/10.12012/CJoE2024-0365
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    Different from traditional linear models which assume that firms make decisions independently according to their own characteristics, this paper uses a spatial autoregressive model to study the heterogeneous peer effects of firm innovations, under the impact of industry policy. We find that firms supported by the policy will show positive innovation interactions, that is, they will tend to imitate the innovation behavior of their peers in the same region, while the non-supported firms do not have this pattern. The difference between them are significant and robust. The imitation behaviors of supported firms aim to maintain a relative competitive position. As for the non-supported firms, those with large financial constraints prefer the innovation imitation to reduce the information cost of decision-making while firms with small financial constraints are likely to shrinkage the innovation investment if their peers have great innovation output due to competition in product or factor markets. The conclusions of this paper provide a new perspective of innovation interaction for studying the results of policy impact. In addition, by emphasizing the heterogeneity of firm behavior, it shows that different mechanisms will have different explanatory power in different cases, thus providing empirical evidence of heterogeneity effect for the analysis of firm interactions.
  • TIAN Yajie, JI Jie, WANG Shouyang, WEI Yunjie
    China Journal of Econometrics. 2025, 5(1): 197-217. https://doi.org/10.12012/CJoE2024-0480
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    The China-U.S. trade dispute has been a significant event in the field of international trade in recent years, with profound impacts on bilateral trade relations. This study examines the effects of the 2018 and 2019 tariff increases imposed by the Trump administration on Chinese goods using the synthetic control method from two perspectives. First, we analyze the dynamic impacts of tariff policies on China's total exports to the U.S., identifying changes before and after policy implementation. Second, we investigate the heterogeneous impacts across 21 HS product categories, uncovering variations in sensitivity to tariff policies. Furthermore, based on the historical policy effects, this study predicts the potential impacts of similar tariff policies under Trump's new administration on China-U.S. trade. The results suggest that if similar policies are implemented, China's total exports to the U.S. will experience further declines. In the early stage of the policy, market behaviors such as stockpiling and anticipatory exports may temporarily drive up export values, but the decline in exports will gradually increase thereafter. Products with high price elasticity are likely to face greater negative impacts compared to high-value-added products, but high-tech products will also face significant pressure due to U.S. technological restrictions. This study provides valuable insights into the economic impacts of the China-U.S. trade dispute, evaluates the consequences of tariff policies, and offers guidance for policymaking.
  • MA Yong, SU Xiaojian, ZHANG Zhengjun
    China Journal of Econometrics. 2025, 5(1): 218-240. https://doi.org/10.12012/CJoE2024-0207
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    In the context of China's emphasis on “maintaining the bottom line of no systemic financial risks”, this paper examines the impact of industry bubbles on systemic risks contribution from an industry perspective. Furthermore, we construct inter-industry risk networks to identify the risk sources under different levels of systemic shocks. This provides effective policy references for deepening the reform of the new development pattern in which the domestic grand cycle plays a leading role. The empirical results indicate that, apart from the promoting effect of asset price bubbles in the Agriculture, Water, Environment, and Utilities Management, and Culture, Sports, and Entertainment industries, the impact of most industry bubbles on the systemic risk contributions of firms within those industries is either insignificant or inhibitory. However, when investor sentiment is high, the systemic risk of companies within the industry increases during a bubble period. In the cumulative effects of the local projection model, the promoting effect of bubbles in most industries shows a sustained upward trend, with the cumulative effect of promotion being heterogeneous. In the quantile risk network, industry connections tighten during extreme shocks compared to normal periods. Additionally, the risk defense capabilities of each industry vary depending on the intensity of systemic shock. Besides, the risk sources in industry bubble networks are predominantly in the Agriculture, Manufacturing, Transportation, Catering, and Leasing industries. This implies that risk shocks propagate outward, revolving around production, distribution, circulation, and consumption as the core, indicating that China's economy is transitioning towards a new development pattern centered on the domestic grand cycle.
  • XIE Zhonghua, ZHU Hongquan, LIN Zhiyu
    China Journal of Econometrics. 2025, 5(1): 241-266. https://doi.org/10.12012/CJoE2024-0199
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    This study proposes a new risk perception factor (FRP) and adds it to Liu et al.'s (2019) three-factor model to form a four-factor model (henceforth RPM4 model) for the Chinese stock market. The results show that the FRP's volatility, Sharpe ratio, and maximum drawdown have significant advantages over the factors of popular asset pricing models. Further testing reveals that the RPM4 model contains more information and has a clear advantage in explaining stock portfolio returns, and performs well in the $R^2$ comparison tests for cross-sectional regressions. The RPM4 model is a useful complement to the Fama and French (1993) three-factor model, as well as Liu et al.'s (2019) three- and four-factor models.
  • CHEN Liming, SU Yang, WANG Xiaoyan, GANG Jianwei, ZHANG Zhi
    China Journal of Econometrics. 2025, 5(1): 267-292. https://doi.org/10.12012/CJoE2024-0237
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    Improving residents' happiness is the fundamental purpose of development and an important measure of development effectiveness. Leveraging big data and large language model technologies represented by ChatGPT, this study explores the possibility of accurately measuring residents' subjective happiness on a national scale. We propose a method for constructing a residents' subjective happiness index based on Weibo data and develop a large language model for sentiment analysis, SentiGLM, built on ChatGLM3, to enhance the accuracy and effectiveness of sentiment classification in Weibo texts. The SentiGLM model significantly improves the performance of sentiment analysis tasks on Weibo texts through low-rank adaptation fine-tuning on a multi-task instruction dataset. Based on approximately 60 million Weibo text data points, this study calculates the subjective happiness index at regional and national levels in China for the first time across four temporal granularities: yearly, monthly, weekly, and daily. The study finds that SentiGLM significantly outperforms traditional machine learning models (such as BERT, LSTM, and SnowNLP) in sentiment analysis of Weibo texts. Moreover, compared to traditional survey methods, the measurement approach based on large language models demonstrates superior cost-effectiveness, timeliness, and robustness, while also providing finer granularity in both temporal and spatial dimensions.