The fundamental methodologies of machine learning and econometrics are reviewed. We also discuss the challenges of integrating the data-driven and model-based causal approaches and conjecture how it may yield new insights to empirical economic studies.
Interval-valued data is widely present in the economic and financial fields. Compared to point data, it provides richer information but also faces challenges such as model uncertainty and potential structural changes. To address this issue, this paper proposes, for the first time, a time-varying model averaging (IMA) method based on interval-valued data. This method dynamically selects time-varying weights by minimizing a locally quadratic loss function based on the
Based a nested model of IRIOT and WIOT, and combining Kronecker product to construct a spatiotemporal weight matrix, this paper empirically explores the mechanism of dual value chain embedding on “first rich driving later rich” using dynamic spatial panels. The regression results for the segmented manufacturing industry in various provinces of China show that: 1) The embedding of global value chains has widened economic disparities, while the embedding of domestic value chains can leverage industrial linkages to realize vertical governance, effectively weakening the further expansion of economic disparities. 2) The embedding of dual value chains is based on the active “spatial catch-up effect” and passive “spatial spillover effect” to promote the “first rich driving the second rich” of China’s manufacturing industry, with the former becoming stronger and the latter becoming weaker. At the same time, the inhibitory effect of domestic value chain embedding on economic disparities is gradually increasing under the catch-up effect, which can significantly offset the amplification effect of global value chain embedding on economic disparities. 3) The spatial effect decomposition results for the embedding of dual value chains indicate that the indirect effects generated by “third-party associations” are greater than the direct effects generated by “direct associations”. Under the spatial overflow mechanism, the indirect effect exceeds 65%, and even under the more spontaneous spatial catch-up mechanism, the indirect effect is as high as 55%. In view of this, deepening the construction of the domestic value chain system, strengthening inter provincial industrial linkages, and making full use of the dynamic mechanism of spillover and catch-up have important policy implications for promoting the Chinese path to modernization process of “first rich driving later rich, and ultimately achieving common prosperity”.
Climate change has emerged as a new source of instability in the global financial system, making the scientific identification and assessment of its transmission channels to the financial sector a critical issue in the field of climate finance. Currently, climate-related financial risk modeling and practical applications still face numerous obstacles. In this context, this paper reviews several key developments in climate-related financial risk studies, including the characteristics of climate risks in financial markets, the methodologies and practices for assessing climate financial risks, and future research directions. To be specific, this study first elaborates on three crucial features of climate financial risks. Second, it systematically reviews three streams of approaches for climate financial risk assessment developed in recent years, analyzes their applicability and limitations, and examines relevant practices adopted by central banks and financial regulators across different countries. Finally, the paper identifies promising directions for future research to support both theoretical advancement and practical implementation in the field of climate financial risk assessment.
Stock price jumps serve as a key indicator of macroeconomic shifts and are often viewed as a barometer of national economic conditions. The release of macroeconomic policies can significantly impact stock markets through various channels such as asset liquidity, market expectations, and investor confidence. As a result, understanding how policy announcements influence stock market jumps has become a central topic in financial volatility research, attracting the attention of policymakers, financial regulators, investors, and academics alike. This paper develops a Policy model that incorporates both positive incentives and negative suppressive effects to capture the influence of policy announcements on the intensity of market jumps. Empirical results show that policy announcements have a more persistent impact on positive jumps, and the Policy model outperforms traditional self-exciting point process models (e.g., Hawkes models) that do not consider policy factors, which demonstrates the effectiveness of incorporating policy information in analyzing market jumps. Furthermore, the study finds that under lower jump thresholds, the Policy model exhibits enhanced fitting accuracy and stability by more effectively integrating policy-related information to capture market dynamics. Using high-frequency data, this paper evaluates the effects of policy releases from the perspectives of parameter estimation and jump prediction, offering practical insights and policy recommendations based on the findings.
This paper provides a classification framework to integrate multi-source information, including available platform firm data, survey data, and industry reports, to estimate the size of platform employment. We first define and conceptualize platform employment, which is then classified into cloud-based and location-based platform employment. We then estimate the total size and distribution of platform employment in 2023, and correct for duplicates arising from workers engaged on multiple platforms. We also estimate full-time and part-time workers separately. The results show that the total number of platform workers in China has reached 247 million in 2023, including approximately 118 million part-time and 129 million full-time workers, the latter accounts for 14.9% of China’s working-age population. Notably, cloud-based platforms employ significantly more workers than location-based platforms, with cloud-based roles predominantly part-time whereas location-based positions are primarily full-time. The paper provides a consistent framework for incorporating updated information from various sources to provide timely estimates of China’s platform workforce.
Finance is the lifeblood of the national economy and plays a pivotal role in China’s modernization. This paper conducts an in-depth analysis of the 2015 SSE 50ETF options market and reveals the profound impact of institutional investors’ options trading behavior on stock market pricing efficiency during periods of significant financial risk. The findings show that, first, institutional investors’ options trading significantly amplified the deviation of stock prices from fundamentals before the market crash. Although this effect weakened after the crash, it persisted. Second, due to heterogeneous beliefs and short-selling constraints, put and call options exhibited markedly asymmetric effects on the stock market, delaying the absorption of negative information into prices. Third, the analysis of dynamic feedback and momentum strategies demonstrates the existence of positive feedback effects in the put options market. However, informed investors, through “riding the bubble” behavior, absorbed the excess returns from momentum trading in advance. This paper uncovers the complex mechanisms by which institutional investors influence stock price formation through the options market during financial turmoil, offering a new analytical perspective for understanding the interaction between China’s derivatives and stock markets.
This paper systematically studies the impact and mechanism of digital government development on household consumption based on cross-border panel data from 118 economies worldwide from 2012 to 2022. Research has found that the improvement of the level of digital government construction significantly promotes the growth of household consumption. This conclusion still holds true after a series of robustness tests such as instrumental variable method, sub sample adjustment, and increasing control variables. Mechanism analysis shows that digital government mainly unleashes residents’ consumption potential through three paths: optimizing the business environment, promoting non-agricultural employment, and advancing inclusive finance development; Heterogeneity analysis reveals that the promotion effect of digital government construction on residents’ consumption is more significant in economies with high social system stability and strong macro governance capabilities, as well as in developed economies. However, in low-income economies, its effect is constrained by insufficient social stability and weak macro governance capabilities. It is necessary to enhance social resilience and improve the governance system in order to fully unleash the effectiveness of digital governance. The research conclusion of this article provides theoretical support and empirical evidence for promoting the construction of digital government in China and promoting household consumption.
New quality productive forces is an important engine for high-quality economic development and a crucial force in promoting the great rejuvenation of the Chinese nation in the new era. This article constructs a corresponding indicator evaluation system based on the connotation of the theory of new quality productive forces, and uses the entropy method to calculate the level of new quality productive forces in 236 cities in China from 2010to 2020, By constructing a theoretical model and conducting empirical research, the impact of new quality productive forces on economic growth is explored. The results indicate that new quality productive forces significantly promotes economic growth, and the three dimensions of new quality productive forces have heterogeneous effects on promoting economic growth. Mechanism analysis shows that environmental regulation intensity plays a regulatory role in promoting economic growth through new quality productive forces. In further analysis, by constructing a spatial lag model to test the spatial spillover effect of new quality productive forces, it was found that new quality productive forces can not only promote the macro economy of local cities, but also promote the economic growth of surrounding cities.
High-tech zones serve as crucial platforms for implementing China’s innovation-driven strategy and act as key engines for achieving high-quality economic development. The implementation of the “Upgrading for Promotion” policy in high-tech zones has profound implications for advancing technological innovation and fostering emerging industries. Based on panel data from Chinese cities from 2003 to 2022, this study conducts an in-depth analysis of the relationship between high-tech zone upgrades and the entry of strategic emerging enterprises. The findings indicate that the policy significantly attracts the entry of strategic emerging enterprises, and this conclusion remains robust after a series of robustness tests. Mechanism analysis reveals that the policy promotes enterprise entry through channels such as digital talent reserves, inclusive digital finance, government technology support, and fiscal and tax incentives. Heterogeneity analysis shows that the policy’s effect is more pronounced in regions with higher levels of artificial intelligence development, stronger official incentives, and greater utilization of foreign capital. Furthermore, the study finds that the policy enhances the total factor productivity (TFP) of strategic emerging enterprises within high-tech zones. These findings provide empirical evidence for promoting the advanced development of high-tech zones and accelerating the cultivation and expansion of emerging industries.
We employ the LM-Test and ABD-Test as tools for jump detection to examine the effect of Federal Open Market Committee (FOMC) meetings on cryptocurrency price jumps. The main findings are as follows:First, FOMC meetings increase the likelihood of price jumps in cryptocurrency markets. Second, unscheduled FOMC meetings are more likely to lead to price jumps. Third, smaller-cap cryptocurrencies are more susceptible to price jumps due to FOMC meetings compared to larger-cap cryptocurrencies. Fourth, a classification based on cryptocurrency use cases reveals that currency, smart contract platform, and computing tokens are more prone to FOMC-induced price jumps. Stablecoins, Culture and Entertainment, and decentralized finance (DeFi) tokens also show some response to FOMC meetings, though the effect lacks statistical significance. Finally, by incorporating economic uncertainty and geopolitical risk as environmental variables, this study finds that high economic uncertainty amplifies the impact of FOMC meetings on cryptocurrency price jumps, while high geopolitical risk tends to dampen the effect of FOMC information on cryptocurrency price volatility.
The depth integration of manufacturing and logistics industries is an inevitable trend for promoting industrial upgrading and achieving high-quality economic development. Smart technologies provide an important resource foundation and technical support for this. However, the current level of smartization in the logistics industry remains relatively low, making it difficult to effectively support the flexible production and service extension of the manufacturing industry, which seriously hinders the depth and breadth of the integration of the two industries. Therefore, this paper, from the perspective of the two-way interaction between manufacturing and logistics industries, introduces process factors and conducts empirical analysis based on the improved TOEP-TAM model to explore the influencing factors and mechanisms of smart technology application in logistics enterprises. The research findings show that at the technical level, the superiority of smart technologies significantly promotes the willingness of logistics enterprises to adopt smart technologies, while the security of smart technologies inhibits it; at the organizational level, the scale and strength of logistics enterprises and the mutual trust between the two industries significantly promote the willingness of logistics enterprises to adopt smart technologies; at the environmental level, competitive pressure and government policies significantly promote the willingness of logistics enterprises to adopt smart technologies; at the process level, the customization level of smart logistics significantly promotes the willingness of logistics enterprises to adopt smart technologies. This study provides important decision support for promoting the smart transformation of logistics enterprises and facilitating the in-depth integration of manufacturing and logistics industries.
