In the era of digital economy, economic activities based on Internet, mobile Internet and artificial intelligence are generating massive data, which have promoted economic growth in return. Big data have various sources and forms, including structured and unstructured data. Unstructured data can be text, images, audio and video, and new structured data can be functional data, interval data, symbolic data, etc. Most Big data has a huge volume, and some is Tall Big data, whose dimensionality of potential explanatory variables is larger than its sample size. The rise of Big data together with machine learning, a main computer-based automatic analytic tool for Big data, has profound implications on statistical science. Based on the characteristics of Big data and the nature of machine learning, the paper discusses the challenges and opportunities brought by Big data and machine learning to statistical modeling and inference, including sample inference, sufficiency principle, data reduction, variable selection, model specification, out-of-sample prediction and causality. We also explore the theory and methodology foundation of machine learning and its integration with statistics.

Currently, the application of alternative data provides a new perspective for scholars and practitioners in the field of financial investment. This paper builds an analysis platform based on the FarmPredict (factor-augmented regularized model for prediction) framework and deep neural network model, realizing the task of learning trading signals from alternative data such as financial short videos and financial news thereby constructing trading strategies for the China share market. Firstly, match the captured financial news with their corresponding stock code and decompose it into text data and image data. Secondly, the text data is input into the FarmPredict learning framework. We construct and screen the text bag of words by which the phrases are decomposed into common factors and specific factors, and then calculate the score of the news text by the factor regression; We then input the image data into the image recognition deep neural network Google Inception v3 model framework built by the transfer learning technique, thereby outputting the probability that the image represents positive/negative emotions and the image sentiment index and image score. For the captured financial short video, it contains two steps. The first step is to strip the audio data and convert it to audio text data, and use the trained FarmPredict framework to calculate the text score of the short videos; the second step is to extract the key frames of the video, and use the trained image model to calculate the video image score; the text score is summed up with the image score to get the short video data score. Finally, the financial short video score, the text score and the image score of the news report are summed to obtain the stock investment signal, which is used as the basis for constructing the China share stock portfolio and formulating an appropriate investment strategy. Finally, the financial short video score, the text score and the image score of the news report are summed to obtain the stock investment signal, which is used as the basis for constructing the China share stock portfolio and formulating an appropriate investment strategy. The research results show that financial videos and financial news data contain information related to stock prices, which can effectively predict market changes and bring excess returns to investors. The empirical study confirms the importance of alternative data in the Chinese market. By comprehensively analyzing alternative data, this paper provides investors with a comprehensive and effective trading signal extraction method, which can help optimize investment strategies and achieve higher real returns.

This paper aims to introduce the philosophy, theories, fundamental content systems, models, methods and tools of modern econometrics based on its development history. We first review the classical assumptions of the linear regression model and discuss the historical development of modern econometrics by various relaxations of the classical assumptions to further illustrate the modern theoretical system and fundamental contents. We also discuss the challenges and opportunities for econometrics in the Big Data era and point out some important directions for the future development of econometrics.

We selectively review some literature on prediction in the presence of big data. Issues of data based approach versus causal approach, micro versus macro modeling, homogeneity versus heterogeneity, model uncertainty versus sampling errors, constant parameter versus time-varying parameter modeling, model evaluation and cross-validation as well as aggregation, etc. are considered.

This paper provides an overall view on the SAR model, which generalizes the autoregessive time series model to a spatial setting. It is the most popular model in spatial econometrics with broad applications in empirical economics as it captures interactions and spilled over effects across economic agents. We first provide some economic justification of such a model in an complete information static game setting, of which observed outcomes are Nash equilibria. Comparative statics analysis in economics provides economic implications on direct and indirect effects and multiplier effect on outcomes. The traditional ML estimation and its extension in terms of QML estimation are discussed. Recent developments on concavity of its log likelihood function are established, and alternative estimation methods, GMM and GEL, are presented. The construction of best GMM estimation with linear-quadratic moments is feasible. The GEL approach on estimation and testing for the SAR model can be robust against unknown heteroskedasticity.

The real growth rate of gross domestic product (GDP) is an important indicator to measure the state of the economy. However, as it is released quarterly with a time lag, it fails to meet the timely economic analysis demand. In this paper, the mixed frequency dynamic factor model (MF-DFM) is used to nowcast quarterly GDP year-on-year growth rate based on timely large-scale monthly economic data, which improves the timeliness of economic analysis. In order to enhance the efficiency of utilizing a large number of available candidate economic variables and avoid the subjectivity of indicator selection in the factor model, this paper proposes a indicator selection method for MF-DFM with large-scale data, which uses the mean square prediction error of the binary dynamic single factor model as the basis for indicator selection. This method is applicable to selecting effective indicators amongst data with quarterly and monthly frequencies, missing values and jagged edges. The empirical analysis results indicate that compared with the traditional time series prediction models and the commonly used mixed frequency models, the MF-DFM based on screening variables by the binary model has a higher accuracy in predicting quarterly GDP growth rate, both for the stability period before COVID-19 and the recovery period after COVID-19. Moreover, the prediction for monthly GDP growth rate provided by this method has a high synchronization with the macroeconomic consistency index, which is conducive to improving the timeliness of economic analysis. This paper provides a new approach for real-time economic monitoring, prediction, and early warning based on indicator selection with the large-scale data.

Green finance plays two important roles. One is a "derivative" of the carbon market. The other is a "booster". It can activate the liquidity of carbon assets and further strengthen the price discovery function of the carbon market. Thus, there is a complementary relationship between the carbon market and the green financial market. This paper first builds a complementary theory of carbon trading and green finance. In the theory, we conclude that green finance is conducive to the development of the carbon market, and carbon market regulation can stimulate enterprises' green financial investment, thereby providing service targets for the green financial market. Meanwhile, a theoretical hypothesis, that can be tested empirically, is provided. Based on the empirical data of carbon trading pilots in seven provinces and cities, the fixed effect panel model and difference-in-difference model are established, respectively, and associated intermediary variables for mechanism analysis are presented. Through the robustness tests, the regression results show that green financial instruments have a significant promotion effect on the development of the carbon-trading market. As the level of green finance is increased by 1%, the development of the carbon market is significantly promoted by 0.657%, but this promotion effect is lagging. The carbon market regulation has a significant crowding effect on enterprises' green innovation, while large-scale enterprises have been encouraged more in green investment. In addition, green investment and green credit instruments play a part in the intermediary effect; Finally, it confirms that carbon regulation has a direct positive impact on the improvement of the green financial market, which verifies the two-way incentive effects between the carbon market and green finance. Based on the empirical results obtained, we put forward some policy recommendations for optimizing the carbon-trading market and improving the green financial system, which is conducive to the achievement of carbon-peaking and carbon neutralization in China.

With the development of global trade, the scale of embodied carbon transfer among countries in the world is generally expanding, and thus an in-depth understanding of China's embodied carbon emissions in global trade is beneficial for fighting for more carbon emission rights and achieving equitable development. By constructing CO₂ emission inventories and multi-regional input-output tables, this study quantitatively estimates the embodied carbon emissions for 140 countries and regions worldwide in 2004, 2007, 2011, and 2014, and analyzes the results at the industry level. This study finds that the carbon transfers among countries further strengthened from 2004 to 2014, and the scale of embodied carbon emissions likewise continued to increase. In 2014, the amount of carbon transfers through global trade is estimated to be 5.3 billion tons, accounting for about a quarter of total global CO₂ emissions. China became the center of world exports of embodied carbon emissions, undertaking more than one-fifth of carbon transfers embodied in global trade, while the United States became the center of imports. China's net carbon emissions embodied in exports increased from 956 Mt in 2004 to 1201 Mt in 2014, making it an increasingly typical net exporter of embodied carbon. China's export structure is mainly concentrated in energy-intensive and carbon-intensive manufacturing industries, while its import structure involves a variety of sectors, including mining, general manufacturing, and transportation. To reduce embodied carbon emissions, China should actively advocate a scientific accounting mechanism based on the shared responsibility of carbon emissions between the production side and the consumption side, so as to ensure that China receives equitable and reasonable emission credits and emission rights. Meanwhile, it is critical to rectify highly polluting industries, optimize the import and export structure, accelerate the efforts of the low-carbon transition, and improve energy efficiency.

In economic randomized controlled trials, subjects are often assigned by complete randomization. However, under complete randomization, the distribution of baseline covariates between treatment and control groups is usually incomparable, which decreases interpretability and accuracy of the experiment, or even distorts the results. In this paper, we introduce the covariate-adjusted randomization design for the economic randomized controlled trial. The covariate-adjusted randomization design adaptively adjusts the covariates balance during the allocation process so as to achieve the better covariate balance. Based on a randomized controlled trial investigating whether personalized information can affect pension savings, we compare the impact of three different randomizations on the covariates balance and estimation of the average treatment effect. Empirical analysis results show that, compared to complete randomization, covariate-adjusted randomization design can significantly reduce the covariate imbalance and thus improve the subsequent estimation precision and testing power.

There is an increasing trend towards combining machine learning methods with traditional econometric methodologies. Starting from comparing features and internal relations of two mainstream causal inference frameworks, this paper proposes that causal inference can be significantly improved with the introducing of machine learning methods in two ways, one is sample matching and one is counterfactual prediction. Firstly, machine learning techniques can enhance matching qualities by pairing samples directly or improving the accuracies of propensity score predictions. This can make the matched samples more similar to samples collected from randomized controlled trials. Secondly, machine learning techniques can improve the accuracies of counterfactual predictions by modeling complex relations, using cross-validation, and adopting regularization. This paper then introduces the theoretical foundations of combining machine learning techniques and causal inferences by reviewing four specific methods: Matching, regression discontinuity, difference-in-difference, and synthetic control method. At the meantime, several application cases are provided in each method section for researchers in applied econometrics as references.

This survey paper highlights some recent developments in estimating treatment effects for panel data. First, this paper begins with a brief introduction of the basic model setup in modern econometric analysis of program or economic policy evaluation for panel data. Second, the primary attention goes to the focus on estimating both the average and quantile treatment effects for panel data. Finally, it concludes the paper by addressing theoretically, methodologically and empirically some possible future research directions for young scholars in econometrics and statistics, particularly, some interesting and challenging research topics related to a combination of machine learning and casual inference for panel data.

Gold spot, ETF and futures markets play important roles in maintaining a country's economic stability, enhancing national credit and hedging financial market fluctuations. In recent years, the price of gold has fluctuated greatly, and the phenomenon of unsynchronized and uncoordinated gold spot, ETF and futures has been observed, which has increased market uncertainty and risks. This paper selects the daily closing data of gold spot contracts, Huaan gold ETF and gold futures main contracts from 2015 to 2021 to discuss the dynamic linkage and volatility spillover effects among gold spot, ETF and futures markets. Firstly, the MS-GARCH model is built to study the relationship between price fluctuation and regional system transformation in the three markets. Then through DCC-GARCH model to explore the dynamic linkage between gold futures, spot and ETF markets. Furthermore, the spillover index model is used to measure the volatility spillover effect among the three markets. The results show that: 1) The integration degree of the three markets is high. DCC-GARCH model shows that the dynamic correlations of the three market returns are maintained at about 0.9 for most of the time. 2) There are significant differences in dynamic linkage among different markets. DCC-GARCH model shows that the correlation coefficient between gold spot and futures market returns fluctuates more violently and frequently. Spillover index model shows that the gold spot market presents negative net spillover in most periods and is the receiver of spillover in most periods. While the return rate of gold ETF and futures market is positive spillover in most periods, that is, it is the disseminator of spillover in most periods. 3) During the sample period, the volatility spillovers of the three markets are dynamic. The smoothing probability diagram of MS-GARCH model shows that after 2019, the duration of the three market returns in the high and low volatility states is relatively short, and the alternation of the two states is more frequent. DCC-GARCH model shows that the correlation coefficients of the three markets' returns have decreased significantly after 2015 and 2018. Spillover index model shows that: The total spillover index of the three markets fluctuated greatly in 2019, once dropping to 55%; In 2019, the external spillover index (TO) and the received spillover (FROM) both declined significantly, and the gold spot market fell the most, falling to 43% and 50% respectively; In 2019, the net spillover index of gold spot and ETF market returns fluctuated greatly, and the net paired spillover index of gold spot to ETF and spot to futures fluctuated greatly.

Since the reform and opening up, especially during 2008-2019, China's M2/GDP has been rising for a long time without serious persisting inflation and China's velocity of money is declining continually, which is called "the monetary puzzle in China". In order to explain this phenomenon, based on China's important economic characteristics and more than 30 types of actual data, this paper expands the model structure in the literature on real estate bubble, establishes and calibrates a general equilibrium model. After accounting for two kinds of money demands with leverage effects and two money supply drivers, real estate investment and local government debt, the calibrated model maintains a low CPI growth rate and explains 42.27% of the 12-year M2/GDP rise, and 49.70% of the decline in the velocity of money. The "counterfactual" analysis shows that if the money supply basically matches the nominal GDP growth rate according to the requirement of "14th Five-Year Plan of China", it can solve the "the monetary puzzle in China" and enhance the high-quality economic development in China. The main contribution of this paper is to provide an analytical framework that can quantify the impact of real estate investment, local government debt, and other factors on the rise in M2/GDP and the decline in the velocity of money.

This paper investigates the effect and mechanisms of industrial digitization on gender employment preference. In theory, industrial digitization can change the work mode and job demand preferences by improving the level of mechanization, promoting service-oriented transformation, and enhancing training investment, thereby changing the employment preferences of various industries for gender. Based on this theoretical framework, we compiled industry-level data to measure the gender employment preference and industrial digitization level for 103 industries in China in 2002, 2007, 2012 and 2017. Then, using the panel regression model, we empirically verify how industrial digitization affects gender employment preference, including the mechanisms and differences. The results show that industrial digitization makes the industry prefer to hire men, and increase the gender gap in employment preferences. The sub-sample regression analysis reveals that the expansion effect of industrial digitalization on industry's gender employment preference is more pronounced in male-preferred and secondary industries. Besides, the mechanism analysis shows that the above heterogeneity is caused by the difference in the mechanism (mechanical input level, service transition degree, or training intensity) of industrial digitization on gender employment preferences in different industries. The findings of this paper have important theoretical and practical significance in understanding the role of digital technology empowering industries on the gender employment structure and preferences in the industry.

The innovative R&D activities of enterprises are also necessary for promoting the transformation of economy and social development to a green and sustainable pattern. But it is still unclear how green-credit policy will influence the allocation of credit funds among enterprises with different R&D intensities within high carbon industries. Based on the data of a total of 620 listed companies in high-carbon industries from 2007-2019, this study investigates how green-credit policies exert heterogeneous influence on the credit allocation among high-carbon firms with different R&D intensity using T-tests, mixed cross-sectional regression models, and difference-in-difference models. The study shows that from a quantitative perspective, the "Green Credit Guidelines" issued in 2012 have led to a significant increase in the allocation of credit within high carbon sectors to companies with high R&D intentions and high R&D investments. These enterprises are relatively less affected while most of the high-carbon enterprises are faced at greater financing pressure because of the "Green Credit Guidelines". From the perspective of the cost of credit, we find that compared to the high-carbon enterprises with low R&D investment, high-carbon enterprises with high R&D investment had gradually received more credit concessions for quite a long time with the development of monetary markets. But there is no sufficient evidence that the "Green Credit Guidelines" have exerted a significant influence in this process.

From the perspective of economics, we introduce the economic interpretations and applications of some basic concepts, ideas, methods and tools in probability and statistics. Examples include subjective probability, cumulative distribution function, stochastic dominance, quartile, expectation, Jensen's inequality, mean and variance, law of large numbers, variance of sample mean converging to zero, independence, martingale difference sequence, statistical significance, goodness of fit and model specification, spectral analysis of time series, machine learning and random sets. Economics applications consist of rational expectations, depicting income inequality, portfolio, efficient market hypothesis, capital asset pricing model, financial derivatives pricing, risk management, model risk, measuring economic causal relationships, identifying economic cycles, and macroeconomic interval management. These examples and applications are helpful in understanding the importance and usage of probability and statistics in economic research and analysis.

It is urgent to develop systematic theories and methods to support the scientific research of innovation management and innovation policy. At the same time, the scientometric theories and methods of analyzing the upstream scientific and technological output of the innovation process fail to meet the needs of comprehensively analyzing the whole innovation process and systematically supporting the research of innovation management and innovation policy. This situation inevitably leads to a more comprehensive interdisciplinary "Innovation Metrology (Innovametrics)". Innovametrics is an emerging discipline that takes the entire innovation system as the research object, orients to the occurrence and development of innovation, and comprehensively analyzes the innovation system. Innovametrics constructs the theoretical and methodological system for analyzing the innovation process from the perspective of innovation system, so as to realize the systematic diagnosis and analysis of the innovation process. Based on the simultaneous development of innovation activity analyses and innovation models, this paper classifies Innovametrics into innovation input metrics, innovation output metrics, innovation profit metrics, innovation transformation metrics and innovation system metrics from five aspects: Input (I), output (O), profit (P), transformation (T) and system (S). Typical research problems and analysis techniques of Innovametrics are summarized from the perspectives of structural, development and dynamic problems. Finally, combined with the practice of innovation management in China, the urgent scientific problems of Innovametrics are prospected. The continuous enrichment of innovation statistics and surveys will inevitably promote the vigorous development of Innovametrics, and the development of Innovametrics will also make innovation management and innovation policy more scientific. The "I-O-P-T-S" five-dimensional Innovametrics system constructed in this paper not only provides a classification system for the analysis of innovation activities for the first time, but also provides a systematic whole-process analysis perspective for the design of research problems in the field of innovation.

Current research has found that the introduction of latent Dirichlet allocation (LDA) topic modeling can improve the prediction of corporate financial fraud. To further explore the source of predictive ability in the topic model, this study uses a sample of 18, 220 annual reports from 3, 397 A-share listed companies in China from 2008 to 2019. Building upon previous LDA models, the study incorporates company, manager, and macro fundamental variables as topic selection variables, and includes a fraud label as content variable to analyze the quality of annual report information and extract high-quality and low-quality topic factors. The empirical results of this study show that the semi-supervised STM-based financial fraud prediction model outperforms models based on LDA, word frequency, and financial indicators, reducing misclassification costs by more than 13%. Further research reveals that the predictive ability of topic factors is more closely related to company characteristics such as size, age, leverage, and proportion of PPE, rather than variables reflecting managerial characteristics. The proposed predictive model not only predicts major frauds but also provides a higher coverage of violating companies or safe investment targets with high accuracy. The findings of this study have important practical implications for regulatory agencies monitoring financial frauds and investors constructing safe investment portfolios.

The purpose of this paper is to systematically state how to use the Gibbs sampling method as a tool, based on the inference principle of big data association feature factors to conduct the extraction of related risk characteristics in the context of financial derivatives. Specifically, using stochastic search method based on Gibbs sampling algorithm under the framework of Markov chain Monte Carlo (MCMC), incorporating the "odd ratio" as criteria, taking FOF, commodity futures as examples, this report systematically discusses how to extract related risk factors for financial derivatives based on big data (including traditional and non-structure data) approach as shown by using so-called stochastic research method. Furthermore, we like to point out the way to identify the risk factors based on big data method established in this paper is quite now and should be useful for the financial service in the practice of Fintech.

Characteristics selection approach is quite critical to multi-factor model for stock selection. Based on data from the Chinese A-share stock market, this paper uses Group LASSO to select characteristics and to nonparametrically estimate the effect of selected factors on future returns. We find that, although a few selected factors are the same as traditional characteristics selection models, many (e.g., current ratio, de-trended turnover, price-to-earnings ratio) are unique. In addition, we use the selected characteristics to predict 1-month-ahead returns, and construct a portfolio going long 20 stocks with the highest predicted returns. We show that, compared with portfolios generated by traditional models, portfolios based on Group LASSO and non-parametric estimation approaches perform better, with higher abnormal return and greater Sharp ratio. Furthermore, selected factors using Group LASSO and non-parametric estimation approaches are quite different between the Chinese and US stock markets. For example, momentum (or reversal), and volatility which are selected factors in the US stock market are not related to future stock return in the Chinese stock market; price-to-earnings ratio and current ratio, which are selected characteristics in the Chinese stock market, are not significant in the US stock market.

Large models, exemplified by ChatGPT, represent a significant breakthrough in general generative artificial intelligence technology. Their far-reaching implications extend into diverse facets of human production, lifestyle, and cognitive processes, prompting a transformative paradigm shift in the realm of economic research. Originating from the convergence of big data and artificial intelligence, these large models introduce a novel approach to systemic analysis, particularly adept at scrutinizing intricate human economic and social systems. We first discuss the fundamental characteristics and development paradigms of ChatGPT and large models, focusing on how these models effectively tackle the methodological challenges posed by the "curse of dimensionality". We then delve into how ChatGPT and large models will influence the paradigm of economic research. This includes a shift from the assumption of the rational economic man to an AI-driven "human-machine hybrid" economic agent, from the isolated economic individual to the socio-economic individual whose behaviors are measurable, from the separation of macroeconomics and microeconomics to their integration, from the separation of qualitative and quantitative analysis to their unification, and from the long-dominant "small-model" paradigm to a "large-model" paradigm in economic research. We also cover the increasing significance of computer algorithms as a prominent research paradigm and method in economics. Finally, we point out the limitations inherent in artificial intelligence technologies, including large models, when employed as a research method in economics and the broader social sciences.

Currently, the Party Central Committee and the State Council have elevated the building of an integrated national market with high efficiency, fair competition, and full openness to a global and strategic level. Based on the public policy experiment of setting up administrative approval centers in prefecture-level cities in China, this paper examines the effect of administrative approval reform with the main direction of "reform of government functions" on breaking monopolies and promoting the construction of an integrated market from the perspective of fair competition. The study finds that the administrative approval reform through administrative approval centers in China helps reduce the monopoly power of enterprises and promote fair competition. However, due to the limited efforts of "reform of government functions" and failure to effectively reduce the institutional transaction costs faced by enterprises, the establishment of administrative approval centers only reduces the monopoly power of enterprises in the short term, but does not have a sustained effect on promoting enterprise entry and fair competition in the long term. Further analysis shows that the establishment of administrative approval centers only has the short-term effect of suppressing monopoly power for eastern regions and non-state enterprises. Moreover, the suppression of monopoly power is more pronounced for the administrative approval centers set up later than for those established earlier. These findings can provide direct policy implications for the current construction of an integrated big market in China.

In recent years, natural language processing (NLP) technology has been widely used to study the emotional polarity of unstructured text data such as financial news, financial commentary and social media, and the emotional polarity of these unstructured text data are utilized as proxy variables of investor sentiment to predict the volatility of financial market. Based on behavioral finance theory, an exchange rates forecasting with sentiment mining of online foreign exchange news is proposed in this dissertation by means of NLP and deep learning. This approach uses mutual information theory to construct the first sentiment lexicon in the field of foreign exchange. On the basis of sentiment lexicon of foreign exchange, the sentiment polarity of foreign exchange news is calculated by combining the basic lexicon constructed in this dissertation. The study shows that there is a Granger causality and long-term co-integration relationship between the sentiment polarity of foreign exchange news and USD/CNY exchange rate. Additionally, this study incorporates the sentiment polarity data of foreign exchange news and other financial data into deep learning approach. The empirical results show that our proposed approach has a significant effect on short-, medium-, and long-term volatility forecasting of USD/CNY exchange rate.

With the increasing global attention to carbon dioxide emissions, the carbon market price has become more and more important. Accurate and reliable carbon market price forecasts can not only provide a better reference for the government's macro-control to achieve the "Chinese carbon peak and carbon neutralization" goal, but also help enterprises can more effectively manage the risks brought by carbon emissions. This article uses empirical mode decomposition (EMD), convolutional neural networks (CNN) and long short-term memory networks (LSTM) to predict carbon emissions trading prices to propose different EMD-CNNs-LSTM combination strategy, and discussed the effect of the CNN-LSTM combination strategy, divide it into serial strategy and parallel strategy. This article uses the Guangdong, Shanghai, Hubei, and Chongqing carbon market price data to systematically compare the EMD-CNN-LSTM combination strategy with single prediction model and combination prediction model. The prediction effect of EMD-CNN-LSTM verifies the accuracy and robustness of the four combined strategy models of EMD-CNN-LSTM, demonstrated that parallel strategy forecasting is better for the universality of carbon market price series.

Developing effective systematic risk measures plays an important role in preventing systematic risk. Based on detailed information of 16 Chinese commercial banks and daily returns of China Securities Index (CSI) 300 from Jan-2011 to Dec-2019, we estimate the systematic risk measure of SRISK for the Chinese banks, which is the expected shortfall of a bank conditional on a prolonged market decline, by means of Monte Carlo simulations. The SRISK is applied to reveal risk ranking of banks, uncover risk contagion among industries, and predict macroeconomic indicators. Our results highlight that: 1) SRISK delivers robust rankings of systematically important banks; 2) Despite the fact that some commercial banks are in the same category, they have heterogeneous effects on different industries. State-owned banks are contagious to the entire industry; 3) SRISK can be used to predict PPI and RPI in short term. It is also capable to predict CPI in 2~3 months. Our results indicate that SRISK not only can be incorporated into the risk management system to strengthen prudential supervision of the banking industry, but also satisfies the requirements of macroprudential supervision due to its bank-industry sensitivity.

The socio-economic field is one of the main occasions for data science applications. How to grasp the disciplinary pattern and focus of data science in this field is a basic problem in formulating and implementing discipline development strategies. Based on the practical needs of China's economic statistics and the important points in the "HMYW 2019 Statistics Report", this paper discusses the disciplinary pattern of data science applications in the socio-economy. We propose that "data processing methods", "ambiguity uncertainty phenomenon" and "problem-driven pattern" are the three focuses of the application of data science in the socio-economy in the era of big data, analyzing their respective importance and key points. Finally, we discuss the future development direction and strategic adjustment of data science.

For the study of text, the mathematical principle of representation learning is firstly introduced, in which distributed representation makes the representation of text information richer and more effective, especially for the ultra-high-dimensional text big data. Taking the famous BERT Transformers model as an example, its high-dimensional representation vector (768 dimensions) codes grammar and semantic knowledge. This information can be reconstructed using a decoder. The main contributions of this paper are in two aspects. The first aspect is to introduce statistical methods to evaluate the representation ability of this BERT model; the second aspect is to use this model to deal with the difficulty of identifying the ambiguity of Chinese sentence segmentation. In the first aspect, we have two findings. One is that BERT model has sufficient representation capability. Even for the text data of 100, 000 samples, dimensional reduction of vector representation space can be obtained to a large extent, which indicates that BERT model has reserved sufficient representation vector space to contain various complex languages. We find that different layers of representation vectors of BERT model of deep learning represent different levels of information of language knowledge. The first layer mainly represents the information of words, while the deeper the depth is, the closer the representation vector is to the whole language knowledge of text. Second, it is found that semantically similar sentences are also in the similar domain in BERT vector space, which indicates that the whole BERT vector representation space adaptively arranges similar language organization in the similar subspace. In the second aspect, we cleverly make use of the MASK mechanism of the BERT model, which allows some words to be hidden at will in the input sentences of the model. However, the model can adaptively predict the representation vector of the part that is deliberately hidden. By comparing the representation vector after correct and wrong segmentation, we were able to correctly identify which molecular approach was correct with an average accuracy of 66.875%.

CPI and industrial added value are basic and important indicators of national economic accounting, and have important practical significance for the analysis of the macroeconomic situation. This paper makes an out-of-sample prediction of China's CPI and industrial added value based on a factor model that better characterizes China's economic characteristics, and compares and analyzes with traditional prediction models. The empirical results support that the factor model has better prediction capabilities than traditional methods.

Central bank communication is an important narrative text that receives a lot of attention from the market, and how to effectively extract key information from the high-dimensional text is a scientific problem to be studied in depth. In this paper, we apply the Sentiment Extraction via Screening and Topic Modeling method proposed by Ke et al. (2019) to measure central bank communication, which has the advantages of simplicity, transparency and replicability. Considering the characteristics of Chinese texts and the multi-instrument framework of China's monetary policy, we select the change values of several actual monetary policy interventions as supervised variables and then construct a central bank communication index, and forecast future actual monetary policy interventions based on generalized monetary policy rules. The results show that textual information on central bank communications helps to provide additional forecasting power. Compared with the indexes constructed by the existing literature based on text analysis methods such as keywords, supervised dictionaries and LDA methods, the index constructed in our paper has better forecasting power, especially with superior performance in long-term forecasting. We verify the effectiveness of central bank communication in guiding expectations from a predictive perspective, and provides feasible solutions for extracting textual information based on different target indicators.

The dynamic adjustment of capital structure is an important factor for the promotion of enterprise value. Taking the state-owned listed enterprises from 2010 to 2021 as a sample, this paper examines the impact of the reform of the authorized operation system of state-owned capital on the dynamic adjustment of the capital structure of state-owned enterprises. This paper finds that the reform can significantly improve the speed of capital structure adjustment of state-owned enterprises. The mechanism test finds that the reform can improve the speed of the capital structure adjustment of state-owned enterprises by reducing the social burden and governing the managers' agency problem. Further research shows that in enterprises with lower shareholding ratios of non-state-owned shareholders, controlled by state-owned capital operating companies, low growth potential, and in mature and declining periods, the reform plays a more significant role in improving the speed of capital structure adjustment. This paper expands the research on the influencing factors of the dynamic adjustment of enterprise capital structure, and enriches the research on the effect of the reform of authorized operation system of state-owned capital. The conclusion of this paper has some practical enlightenment for optimizing the capital structure of state-owned enterprises and perfecting the authorized operation system of state-owned capital.

Forecasting stock prices has been regarded as one of the most challenging tasks, because stock prices are determined by many factors, including macroeconomic indicators, fundamental factors and investors sentiment etc. Traditional time series models forecast stock returns only based on daily, weekly or monthly historical observations. Recently, the fast development of the financial markets and data collection technology make high-frequency data more and more available. As high-frequency data may contain some extra information compared to low-frequency data, it may be able to improve stock prices forecasting. In this paper, we propose a new method based on nonparametric functional data analysis, which is used to extract predictors from high-frequency data. Then we combine the high frequency predictors with traditional time series predictors to be a mixed prediction model for forecasting stock prices. The construction method of predictors is very flexible, without any parametric assumption. For empirical studies, we apply the method to CSI300 index, and find that the new method after incorporating the useful information from high-frequency data performs better than the traditional autoregressive model. Our results provide an empirical evidence that high-frequency data could help improve stock prices forecasting.

Stock index price change reflects the trend of the equity market, is an important financial market index, has been the focus of academic circles. This paper taking the CSI300 price index as the research object and the data from January 2006 to March 2019 is selected, use H-LSTM model to predict the stock index price. The result show that: Using batch prediction, point-by-point pushback, and controlling the rise and fall, as long as increasing the number of network input variables and reducing the time width of the forecast can increase the fitting degree of the LSTM model to the index price; The hybrid information extractor is better than the principal component analysis, the sparse Autoencoder and the t-SNE, it can effectively extract the characteristics of the CSI300.

The changing international situation and global market environment are accompanied by a series of "Black Swan" and "Gray Rhino" events. Estimating the shock effect of significant emergencies and predicting price change points have always been among the hot and challenging issues the academic circle faces. This paper developed the GSI-BN research framework to analyze the shock effect of significant emergencies on the oil market and predict the oil price trend when different events occur. This paper explores the impact of emergencies and forecasts the crude oil price by developing the GSI-BN research framework. First, we construct the network public opinion attention index of emergencies and determine the time windows of different emergencies based on the Google Search Index (GSI). Secondly, the impact mechanism of emergencies is simplified to the topological network diagram based on the Bayesian network (BN). The emergencies are subdivided and the conditional probability behind them is mined to predict the occurrence probability of the emergencies. Finally, we analyze and forecast the price caused by emergencies in different scenarios. The results show that when the monthly average growth rates of supply and demand are high, supply and demand are still in a state of balance, and the price will not rise or fall sharply. Therefore, the probability of low price is the largest. When the supply shock is significant and the demand is growing at a normal level, the probability of a medium-low price is the largest. On the demand side, when the financial crisis occurs, the probability of absolute growth of crude oil consumption in the next month was highest in the medium growth range; On the supply side, the simultaneous occurrence of two or three emergencies is a small probability event. In addition, the global oil demand will rise as OPEC is committed to reducing production. Therefore, the impact of hurricanes on prices is smaller than in previous years. The effect of the financial crisis on the crude oil market is comprehensive, and the demand shock caused by the financial crisis is only one of the factors. Both the war and the OPEC meeting were short-lived supply shocks and primarily reflected market expectations, and they may not cause a supply side shock. Therefore, there will not be a significant price difference when the impact is transmitted to the oil price. This paper provides a new perspective and method for studying the shock effect of emergencies on the crude oil market and the change point of oil price.

Dissatisfaction in feeling state is crucial factors causing decision-making bias and economic loss among middle-aged and elderly. Based on the tracking data of the 2015 China Health and Retirement Longitudinal Study (CHARLS), this paper empirically analyzes the impact of happiness on the fraud risk in middle-aged and elderly. The results show that there is a significant negative correlation between the happiness of middle-aged and elderly people and the risk of being cheated. Besides, we find the result is still robust after controlling endogeneity and other methodological alternatives. Moreover, this paper provide enough evidence to support the hypothesis that happiness could prevent middle-aged and elderly from bearing fraud risk by increasing their self-control capacity. Against the background of frequent frauds among the middle-aged and elderly, this paper helps to understand the role of mental health in their economic decision-making, offering policy makers suggestions to establish a fraud prevention system for middle-aged and elderly.

This study investigates the recent development of financial research. We first summarize the up-to-date progress of research on asset pricing, corporate finance, and the economic development and finance market. Then we analyze emerging trends and challenges and show the following frontiers of financial research, including new monetary theory to accommodate financial crisis and digital currency, sustainable finance, finance safety, and climate finance. Among these topics, there are significant opportunities for China's finance research, especially in the area of preventing financial systemic risk, expanding and monetary theory, and developing inclusive finance.

Growth-at-risk is an analysis framework for stabilizing growth and preventing risks, and stabilizing growth and preventing risks are the "mini version" of coordinating safety and development. As an attempt to construct a new paradigm of macroeconomic analysis, we use the theory of nonlinear expectations to study growth-at-risk with uncertain mean and variance. The main feature or advantage of nonlinear expectations lies in the "endogenous characterization" of economic growth fluctuations, the bottom line thinking and interval thinking of risk measurement, and the "one-direction independence" of data processing. The main innovations and contributions are: Firstly, theoretically, the analysis and definition of the G-vertex distribution is given from the monotonic and convex-concave changes of the initial conditions of the partial differential equation that the economic growth G-distribution satisfies, and the G-vertex distribution gives G-value-at-risk (G-VaR); secondly, explain the interval data thinking of GaR measured by G-VaR, empirical tests show that the accuracy of risk growth is the highest under uncertainty of mean and variance and the most significant impact on economic growth, and the risky growth corridor is given; thirdly, the maximum volatility is negatively correlated with economic growth, and the smallest volatility is positively correlated with economic growth, the rolling standard deviation rises before the negative growth of the economy, which partially explains the "Volatility Paradox" of economic growth.

With the development of information technology, social media have gradually become an important channel for people to obtain information, share views, and vent feelings, and exerted a profound impact on information dissemination and sentiment contagion in the capital market, giving birth to a large body of literature focusing on social media and capital market. The extant literature has confirmed social media's information content and role in influencing the behaviors of investors, companies as well as other information intermediaries. This paper uses the method of bibliometrics to sort out and analyze the literature in this area. The main results are as follows: 1) This research area is in a stage of vigorous development, and the amount of publications exhibits an increasing trend over the years; 2) The core effects of journals for research in this area have not fully emerged; 3) The research team in this field has basically formed; 4) The research topic in this area can be divided into two parts: Social media and stock trading and social media and corporate governance. Based on these findings, this paper summarizes the possible research directions as well as issues worthy of attention in the hope of providing a valuable reference for scholars who are interested in this area.

The main goal of macro prudential policies is to maintain financial stability. This paper proposes adopting the macro-econometric policy evaluation method under the Rubin causal effect framework to evaluate the impact of China's macro prudential policies on financial stability during the sample period 2007--2020. First, the paper constructs a macro prudential policy index to quantitatively measure the intensity of China's macro prudential policies. Second, the paper uses the systemic financial risk index, termed as SRISK to measure China's systemic financial risk. Finally, the paper evaluates the macro prudential policies' effects on the systemic financial risk, cross-sectoral contagion of systemic financial risk and important intermediate variables in the credit channel. Our empirical findings indicate that loose macro prudential policies can increase the risks of intermediate variables in the credit channel, and the risks lead to a significant rise in SRISK of house sector, but for the SRISK of financial and manufacturing sectors, the cumulative effects in 24 periods are not significant. However, in addition to a significant rise in commercial banks' capital adequacy ratio growth, tight macro prudential policies have no significant effects on the other intermediate variables in the credit channel, and further have no obvious effects on SRISK of financial, house and manufacturing sectors. Based on the conclusions, we suggest that systemic risk indicators should be further researched to provide more comprehensive and systematic targets for macro prudential authorities. Moreover, the transmission channel of macro prudential policies on financial stability should be improved to enhance the efficiency of regulation. Finally, more attentions should be paid to the cross-sectoral contagion of systemic financial risk so as to prevent systemic financial risk from a systemic perspective.

This paper proposes proportion of gainers (POG) to reflect the heterogeneity of social program. This parameter reflects the proportion of different subgroups who benefit from the participation of the social program in the audience, which can be used to reflecting the universality of the policy benefiting the people. Under the general framework of causal analysis of latent variables, this paper studies the nonparametric identification of POG, and proposes a feasible estimation method. Through numerical simulation, it is found that the proposed estimator has good small sample properties. We use this method to estimate the effect of the New Rural Pension Scheme (NRPS) on the labor supply, family income and family consumption of rural residents. And we find that the NRPS has obvious heterogeneity. TheNRPS has a greater role in promoting individuals with high labor supply, high family income and high family consumption, but a weaker role in promoting individuals with low labor supply, low family income and low family consumption. This shows that in order to comprehensively and accurately evaluate a livelihood policy, we should pay attention to the heterogeneity of the policy.