数字经济发展水平的统计测度及时空演变趋势研究北大核心

本文构建数字经济发展新动能指数综合指标体系,然后基于2017~2020年我国30个省(区、市)的面板数据,运用熵权TOPSIS法对各个省(区、市)的数字经济发展水平进行测度,利用探索性空间数据分析和核密度估计法对数字经济发展新动能指数的时空演变趋势进行分析。研究发现:数字经济的发展水平呈现出东部地区>中部地区>西部地区的阶梯式分布,相邻省(区、市)的数字经济发展水平趋同且集聚情况存在地区差异。对于与东部地区数字经济发展水平的差距,中部地区正逐步缩小而西部地区正逐步扩大,同时东、西部乃至全国内部省(区、市)间的数字经济发展水平的差距都在逐年扩大。最后,基于研究结论,提出推动数字经济发展水平的对策建议。     

Modeling time-varying higher-order conditional moments: A survey

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time-varying second-order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher-order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time-varying higher-order conditional moments in the last two decades. This paper outlines recent developments in modeling time-varying higher-order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time-varying higher-order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher-order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.     

Option prices for risk-neutral density estimation using nonparametric methods through big data and large-scale problems

Option pricing theory determines the structure of call and put option pricing functions. In nonparametric risk-neutral density estimation based on kernel functions, local constraints cannot induce a second derivative function that must integrate one. Convexity and monotonicity of pricing functions also cannot be enforced. A large-scale (optimization) approach is proposed for the risk-neutral density estimation, imposing an enlarged set of no-arbitrage constraints. We considered simulations using Heston's model and hypergeometric functions. The method is applied to samples of intraday data from VIX and S&P500 indexes.     

Rebuilding or refurbishing: Heterogeneity effects of urban renewal strategy

This study investigates the heterogeneous policy effects of urban renewal strategies in shantytown renewal in Beijing. Using the spatial difference-in-difference approach, this study identifies the renewal approaches for each shantytown to analyze how each strategy affects housing prices and population density within the policy target area and surrounding area. The empirical results suggest that current urban renewal actions in Beijing have not achieved the expected policy goals and imply significant heterogeneity between refurbishing and rebuilding shantytowns in improving building value. Rebuilding projects have positive renewal effects and externalities, driving housing prices to increase by 5.86%–7.25% within the shantytown and a 1-km radius. Furthermore, the dynamic analysis shows that these impacts are prolonged, with housing prices maintaining an upward trend for 2–6 years after rebuilding. However, there is no causality between refurbishing dilapidated areas and increasing building values.     

Population density,urbanisation and agricultural mechanisation in modern Ghana

Farmers' technological choices take place within farming systems that are shaped by population pressure, connectivity to urban markets and agro-ecological conditions. The relationship between these drivers and agricultural technology use is ambiguous. On the one hand, population growth can increase the supply of labour, driving down wage rates and reducing the incentives for mechanisation. On the other hand, rural–urban movements of people can reduce rural labour supply while simultaneously driving up the demand for food and hence the demand for mechanisation. Past theories of agricultural mechanisation have explained the low adoption of agricultural machinery in land-abundant cereal production systems of SSA in terms of these drivers. However, recent empirical observations find extensive adoption of mechanised ploughing technology by small, medium and large-scale farmers in Ghana. Examining the Ghanaian experience can thus shed new light on theories of mechanisation. A large household survey dataset covering eight districts is combined with geo-spatial data on population, urban proximity and agro-ecological factors to consider whether the existing theories are able to explain farm-level adoption decisions in this context. The analysis finds that a farmer's decision to use agricultural machinery is associated with lower population density and proximity to urban centres. In northern Ghana, these drivers of technological change are as important as farm household characteristics in understanding cross-sectional patterns of machinery adoption.     

近距离无线电台邻道干扰的功率谱解析

指挥控制系统的无线通信设备数量多、工作频率范围窄，相互之间存在严重的邻道干扰。针对近距离电台间邻道干扰问题，提出了基于功率谱解析的分析方法。首先理论推导了信号原功率谱密度函数与展宽延拓后功率谱密度的严格数学关系，在此基础上研究邻道干扰产生机制，进而分析不同邻道的干扰强度，得到了邻道功率与频率之间的关系式，推导了进入接收机的信干噪比（SINR）；最后，通过仿真验证了所提出的分析方法的可行性。理论计算与仿真结果相差在0.5 dB以内，为进一步有效规划指控系统频谱和提高利用效率提供了重要依据。     

柔软剂BT在腈纶绒线上的应用

国内外腈纶织物柔软整理正由传统的手感改良向功能化、仿动物毛感方向发展。本文研究了柔软剂BT在腈纶绒线上的工艺条件,并从柔软剂BT的性能测试、柔软剂用量、温度影响、时间影响进行了探讨。实验结果表明,柔软剂BT柔软效果好,绒线柔软丰满、毛感强、而且耐洗、抗静电,并且不影响染色色光。     

Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models

Many static and dynamic models exist to forecast Value-at-Risk and other quantile-related metrics used in financial risk management. Industry practice favours simpler, static models such as historical simulation or its variants. Most academic research focuses on dynamic models in the GARCH family. While numerous studies examine the accuracy of multivariate models for forecasting risk metrics, there is little research on accurately predicting the entire multivariate distribution. However, this is an essential element of asset pricing or portfolio optimization problems having non-analytic solutions. We approach this highly complex problem using various proper multivariate scoring rules to evaluate forecasts of eight-dimensional multivariate distributions: exchange rates, interest rates and commodity futures. This way, we test the performance of static models, namely, empirical distribution functions and a new factor-quantile model with commonly used dynamic models in the asymmetric multivariate GARCH class.