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学者姓名：游万海
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Quantifying the influence of uncertainty on gold prices is significant for improving related financial decision making. This study proposes a novel CNN-LSTM neural network that can extract potential features from sample data to effectively predict gold prices. Specifically, we demonstrate various uncertainty measures containing market volatility information, such as the economic policy uncertainty index (EPU), epidemic stock market volatility index (IDEMV), and volatility index (VIX), which can contribute to the prediction of gold prices rather than relying solely on the history of tickers, which is conventionally used for prediction. In addition, the proposed model is evaluated against SVR and two different LSTM models. The empirical findings reveal that incorporating additional features, such as uncertainty measures, contributes to improving the predictive accuracy of the model. The CNN-LSTM model, with the inclusion of EPU, IDEMV, and both, achieves a high prediction accuracy. Additionally, the overall prediction accuracy of the CNN-LSTM model outperforms the other proposed methods. The findings provide profound insight into portfolio diversification and risk management practices for governments and businesses.
Keyword ：
CNN-LSTM CNN-LSTM COVID-19 COVID-19 Gold prices Gold prices Predictions Predictions Uncertainty measures Uncertainty measures
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| GB/T 7714 | You, Wanhai , Chen, Jianyong , Xie, Haoqi et al. Which uncertainty measure better predicts gold prices? New evidence from a CNN-LSTM approach [J]. | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE , 2025 , 76 . |
| MLA | You, Wanhai et al. "Which uncertainty measure better predicts gold prices? New evidence from a CNN-LSTM approach" . | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE 76 (2025) . |
| APA | You, Wanhai , Chen, Jianyong , Xie, Haoqi , Ren, Yinghua . Which uncertainty measure better predicts gold prices? New evidence from a CNN-LSTM approach . | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE , 2025 , 76 . |
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Using panel data on China’s carbon-covered listed enterprises, this study employs the Poisson pseudo-maximum likelihood with high-dimensional fixed effects (PPMLHDFE) approach to investigate the impact of the carbon emissions trading (CET) policy on firms’ green technology innovation. A novel categorization is proposed to classify the enterprises in carbon-covered industries into CET-included and non-CET-included groups. Results show that China’s CET policy plays a vital role in green technology innovation, with a stronger positive effect on CET-included enterprises. Furthermore, heterogeneity exists among eight carbon-covered industries. The green technology innovation of enterprises in the power and petrochemical industries, particularly the CET-included ones, is stimulated by the CET policy. However, it inhibits green technology innovation in the aviation and chemical industries and plays no role in other industries. Additionally, the effect exhibits heterogeneity across pilot areas. It is positive in Hubei and Tianjin, negative in Chongqing and Shanghai, and ineffective in Beijing, Guangdong, and Shenzhen. Finally, the effect of the CET policy on green technology innovation is stronger for state-owned enterprises than for non-state-owned ones. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Keyword ：
carbon-covered industries carbon-covered industries Carbon emissions trading Carbon emissions trading Cet-included and non-cet-included enterprises Cet-included and non-cet-included enterprises green technology innovation green technology innovation PPMLHDFE method PPMLHDFE method
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| GB/T 7714 | Guo, Y. , Lin, Y. , Yi, M. et al. How does China’s carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries [J]. | Applied Economics , 2024 . |
| MLA | Guo, Y. et al. "How does China’s carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries" . | Applied Economics (2024) . |
| APA | Guo, Y. , Lin, Y. , Yi, M. , You, W. . How does China’s carbon emissions trading policy affect green technology innovation? Evidence from enterprises in carbon-covered industries . | Applied Economics , 2024 . |
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This study examines the spatial effects of economic complexity on carbon emissions in 82 countries from 2001 to 2019 based on a spatial panel model. Our research explores the spatial mediating roles of energy structure and energy efficiency, considering both local and spillover effects. We find economic complexity has a positive local effect, but a negative spillover effect on carbon emissions. The positive local effect is more potent in countries with high economic complexity compared to low ones. Economic complexity promotes local carbon emissions by curbing local energy structure but limits the carbon emissions of neighbours by boosting their energy efficiency. This study enriches the relevant achievements of the mechanism by which economic complexity impacts carbon emissions. Governments should take advantage of the environmental benefits of economic complexity, focus on technological improvements and green production, optimize the energy mix and promote energy efficiency.
Keyword ：
carbon emission carbon emission Economic complexity Economic complexity mediation model mediation model spillover effects spillover effects
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| GB/T 7714 | Ren, Yinghua , Mo, Yingxin , You, Wanhai . Economic complexity, CO2 emissions, and the mediating roles of energy structure and energy efficiency: a spatial panel analysis [J]. | APPLIED ECONOMICS , 2024 , 57 (1) : 1-15 . |
| MLA | Ren, Yinghua et al. "Economic complexity, CO2 emissions, and the mediating roles of energy structure and energy efficiency: a spatial panel analysis" . | APPLIED ECONOMICS 57 . 1 (2024) : 1-15 . |
| APA | Ren, Yinghua , Mo, Yingxin , You, Wanhai . Economic complexity, CO2 emissions, and the mediating roles of energy structure and energy efficiency: a spatial panel analysis . | APPLIED ECONOMICS , 2024 , 57 (1) , 1-15 . |
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Because of the acceleration in marketization and globalization, stock markets in the BRICS (Brazil, Russia, India, China, and South Africa) countries are affected by various global factors, for example, oil prices, gold prices, global stock market volatility, global economic policy un-certainty, financial stress, and investor sentiment. This paper offers new insights into the short -and long-run linkages between global factors and BRICS stock markets by applying the quantile autoregressive distributed lags (QARDL) approach. This novel methodology enables us to test short-and long-run linkages accounting for distributional asymmetry. That is, the nonlinear dynamic relationship between the global factors and BRICS stock prices depends on market conditions. Our empirical results show that the effects of gold prices and global stock market volatility on BRICS stock prices are more significant in the long run than in the short run. A decrease in global stock market volatility is associated with higher stock prices, while gold prices demonstrate upward co-movement in dynamic correlations with stock markets. Irrational factors, such as economic policy uncertainty, financial stress, and investor sentiment, play a critical role in the short term, and negative interdependence is dominant. Finally, the rolling-window esti-mation technique is used to examine time-varying patterns between major global factors and BRICS stock markets. & COPY; 2022 Elsevier B.V. All rights reserved.
Keyword ：
BRICS stock markets BRICS stock markets Global factors Global factors QARDL QARDL Short-and long-run effects Short-and long-run effects Time-varying patterns Time-varying patterns
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| GB/T 7714 | Wang, Ningli , You, Wanhai . New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach [J]. | ECONOMIC SYSTEMS , 2023 , 47 (2) . |
| MLA | Wang, Ningli et al. "New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach" . | ECONOMIC SYSTEMS 47 . 2 (2023) . |
| APA | Wang, Ningli , You, Wanhai . New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach . | ECONOMIC SYSTEMS , 2023 , 47 (2) . |
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It is widely recognized that the tourism industry is susceptible to crisis or natural disaster. Although some literature has studied the consequences of the crisis and disaster, there remains a lack of study on the effect of COVID-19. Against this background, this paper investigates the tourist flow forecasting by adopting an advanced nonparametric mixed-frequency vector autoregressions model using Bayesian additive regression trees. This is particularly suitable for forecasting the presence of extreme observations, for example, the COVID-19 pandemic. We investigate tourism demand forecasting using a large number of predictors, including industrial production index, CPI, exchange rate, economic policy uncertainty, Google trends index, and COVID-19 infection rate. The data used for this study relate to tourist flows in Chinese Hong Kong, Japan, and South Korea. Empirical study demonstrates that this novel model significantly outperforms the traditional mixed-frequency vector autoregressions model to quarterly tourist flow forecasting. Therefore, this model can significantly enhance tourism forecast accuracy in the face of extreme events. This study contributes to the literature on tourism forecasting and provides policymakers with policy implications.
Keyword ：
COVID-19 COVID-19 extreme observations extreme observations forecasting accuracy forecasting accuracy nonparametric mixed-frequency VAR model nonparametric mixed-frequency VAR model tourism forecasting tourism forecasting
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| GB/T 7714 | You, Wanhai , Huang, Yuming , Lee, Chien-Chiang . Forecasting tourist flows in the COVID-19 era using nonparametric mixed-frequency VARs [J]. | JOURNAL OF FORECASTING , 2023 , 43 (2) : 473-489 . |
| MLA | You, Wanhai et al. "Forecasting tourist flows in the COVID-19 era using nonparametric mixed-frequency VARs" . | JOURNAL OF FORECASTING 43 . 2 (2023) : 473-489 . |
| APA | You, Wanhai , Huang, Yuming , Lee, Chien-Chiang . Forecasting tourist flows in the COVID-19 era using nonparametric mixed-frequency VARs . | JOURNAL OF FORECASTING , 2023 , 43 (2) , 473-489 . |
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Using the annual report text information of Chinese listed banks from 2007 to 2020, this paper constructs multiple kinds of spatial weight matrices from the perspective of business similarity to identify the systemic risk spillover channels. Furthermore, the Bayesian posterior probability methodology proposed by Debarsy and LeSage (2018) is employed to assess the relative impor-tance of each spillover channel. Besides, we discuss the risk spillovers of specific factors. The empirical results show that the loan type, loan region, investment industry, and income structure are all effective risk spillover channels, and the loan type channel is of the utmost importance. And also the spillover effects of bank-specific factors are identified and continuous. Our results are validated by robust analysis.
Keyword ：
Annual reports Annual reports Business similarity Business similarity Spatial spillover channels Spatial spillover channels Systemic risk Systemic risk
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| GB/T 7714 | Nan, Shijing , Wang, Minna , You, Wanhai et al. Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text [J]. | FINANCE RESEARCH LETTERS , 2023 , 55 . |
| MLA | Nan, Shijing et al. "Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text" . | FINANCE RESEARCH LETTERS 55 (2023) . |
| APA | Nan, Shijing , Wang, Minna , You, Wanhai , Guo, Yawei . Making text count: Identifying systemic risk spillover channels in the Chinese banking sector using annual reports text . | FINANCE RESEARCH LETTERS , 2023 , 55 . |
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The goal of this study is to explore the causal relationship among economic growth, economic complexity and CO2 emissions by using panel data of 95 countries for the period 1996-2015. A novel panel Granger approach proposed by Juodis et al. (2021) is adopted. Under this approach, we can explore the Granger causality in homogeneous or heterogeneous panels. To uncover the heterogeneous causal effects at different income levels, this study further divide the sample into three groups according to their annual income levels. Empirical results show that there are bi-directional causalities among economic growth, economic complexity and CO2 emissions for all groups. However, the magnitudes of the effects are heterogeneous for different groups. As to low-income countries, economic complexity is positive and significant for CO2 emissions, while CO2 emissions are negative for economic complexity. Furthermore, there is a positive interaction between economic complexity and C(O)2 emissions for middle-income countries. Regarding high-income countries, however, increasing economic complexity might effectively reduce CO2 emissions, and CO2 emissions can significantly increase economic complexity. Additionally, economic complexity will prominently decrease GDP in low-income countries. These findings are robust to different economic complexity indexes. Our results suggest that both developing and developed countries should set a reasonable CO2 emissions target, and on this basis, maintain a good balance between economic complexity and CO2 emissions. (C) 2021 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
Keyword ：
CO2 emissions CO2 emissions Economic complexity Economic complexity Granger non-causality test Granger non-causality test Panel data Panel data
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| GB/T 7714 | You, Wanhai , Zhang, Yue , Lee, Chien-Chiang . The dynamic impact of economic growth and economic complexity on CO2 emissions: An advanced panel data estimation [J]. | ECONOMIC ANALYSIS AND POLICY , 2022 , 73 : 112-128 . |
| MLA | You, Wanhai et al. "The dynamic impact of economic growth and economic complexity on CO2 emissions: An advanced panel data estimation" . | ECONOMIC ANALYSIS AND POLICY 73 (2022) : 112-128 . |
| APA | You, Wanhai , Zhang, Yue , Lee, Chien-Chiang . The dynamic impact of economic growth and economic complexity on CO2 emissions: An advanced panel data estimation . | ECONOMIC ANALYSIS AND POLICY , 2022 , 73 , 112-128 . |
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In this article, we investigate whether world uncertainty index (WUI) Granger-causes visitor arrivals (VA) in 19 emerging economies using the mixed-frequency vector autoregressive model (MF-VAR). Moreover, a comparative study on MF-VAR and low-frequency vector autoregressive model (LF-VAR) had been analyzed to further tap the need for MF-VAR model. Empirical results reveal that the Granger causality is not homogeneous for emerging economies. To be more specific, WUI Granger-causes VA in some emerging economies, while others do not. Also, the causality running from WUI to VA is heterogeneous at various horizons. The results show the short-run causality running from WUI to VA in China, Israel, Morocco, the Philippines, Poland, and Turkey whereas long-run causality in Colombia. From these findings, different countries should formulate corresponding tourism strategies to deal with uncertainty. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
Keyword ：
Emerging economies Emerging economies MF-VAR model MF-VAR model Visitor arrivals Visitor arrivals World uncertainty index World uncertainty index
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| GB/T 7714 | Ding, H. , Ren, Y. , You, W. . Does uncertainty granger-causes visitor arrivals? evidence from the MF-VAR model [J]. | Quality and Quantity , 2022 , 56 (6) : 4193-4215 . |
| MLA | Ding, H. et al. "Does uncertainty granger-causes visitor arrivals? evidence from the MF-VAR model" . | Quality and Quantity 56 . 6 (2022) : 4193-4215 . |
| APA | Ding, H. , Ren, Y. , You, W. . Does uncertainty granger-causes visitor arrivals? evidence from the MF-VAR model . | Quality and Quantity , 2022 , 56 (6) , 4193-4215 . |
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This paper employs the spatial panel model to explore the interaction between globalization and economic complexity on CO2 emissions. To capture different potential mechanisms of diffusions, this paper builds hybrid W matrices based on trade distance and geographical distances. Further, to reduce small sample bias and capture "true interactions" across countries, the multiple imputation algorithm is used to address missingness pattern. This approach can efficiently enhance data quality and inferences validity. Furthermore, to distinguish the spatial spillover accurately, cross-sectional averages are used to solve the issue caused by the omission of common factors or shocks that affect differently the different spatial units. Results show that there is a positive spatial spillover of CO2 emissions from neighboring countries to the local country. Furthermore, the results show that globalization on its own has no significant effect on CO2 emissions in the local country but decreases CO2 emissions in neighboring countries. More importantly, when taking economic complexity into account, high level of economic complexity may decrease the negative indirect impact of globalization on carbon emissions. The more complex countries are to the economic structure, the less is the negative impact of globalization on carbon emissions in spatially related countries. These findings highlight that the role of economic complexity and spatial spillovers effects are imperative to be considered. Based on this study, several policy recommendations are provided.
Keyword ：
CO2 emissions CO2 emissions Common factors Common factors Economic complexity Economic complexity Globalization Globalization Multiple imputation Multiple imputation Spatial panel model Spatial panel model
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| GB/T 7714 | Nan, Shijing , Huo, Yuchen , You, Wanhai et al. Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity? [J]. | ENERGY ECONOMICS , 2022 , 112 . |
| MLA | Nan, Shijing et al. "Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity?" . | ENERGY ECONOMICS 112 (2022) . |
| APA | Nan, Shijing , Huo, Yuchen , You, Wanhai , Guo, Yawei . Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity? . | ENERGY ECONOMICS , 2022 , 112 . |
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This paper studies the multiscale features of extreme risk spillover among global stock markets over various time-frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-errorvariance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.
Keyword ：
Extreme risk spillover Extreme risk spillover Global equity markets Global equity markets Multiscale networks Multiscale networks Time -frequency dynamic Time -frequency dynamic Wavelet analysis Wavelet analysis
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| GB/T 7714 | Ren, Yinghua , Zhao, Wanru , You, Wanhai et al. Multiscale features of extreme risk spillover networks among global stock markets [J]. | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE , 2022 , 62 . |
| MLA | Ren, Yinghua et al. "Multiscale features of extreme risk spillover networks among global stock markets" . | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE 62 (2022) . |
| APA | Ren, Yinghua , Zhao, Wanru , You, Wanhai , Zhu, Huiming . Multiscale features of extreme risk spillover networks among global stock markets . | NORTH AMERICAN JOURNAL OF ECONOMICS AND FINANCE , 2022 , 62 . |
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