对外直接投资与经济增长关系研究——以湖南经济为例

本文的目的是通过湖南省对外直接投资与经济增长的关系的定量分析,得出一些实证关系,从而对湖南对外直接投资的作用有更精确的掌握,对于湖南贸易及投资的发展提供理论及政策导向.研究表明,湖南对外直接投资是贸易变化的原因,而贸易不是对外直接投资变化的原因.     

疫情改变了我国FDI流入的影响因素吗?

根据联合国贸发会的统计数据,疫情对FDI的影响程度已经远超2008年全球金融危机。在此背景下,本文通过理论分析和实证检验探讨疫情对中国FDI流入的影响、疫情是否改变了我国FDI流入的影响因素和疫情常态化下我国FDI流入恢复的支撑因素等。断点回归模型结果显示,新冠疫情对各样本省市FDI流入的影响属于中短期效应,疫情并没有导致影响样本省市FDI流入的因素发生本质变化,且各样本省市FDI流入受疫情影响不同、恢复快慢也不同。进一步的统计分析和实证检验显示,FDI流入在样本省市均具有较强的惯性,中部地区的湖北和江西FDI流入受经济变量的影响比较显著,而东部地区的广东、江苏和上海FDI流入的影响因素应更多地考虑营商环境、产业结构特征和人力资本等,此外基础设施对样本省市FDI流入的影响并不显著。     

余额宝收益率与上海银行间同业拆借利率 关系的实证研究

作为互联网理财产品的代表,余额宝收益率与货币市场基准利率密切相关。本文 选取余额宝收益率与市场利率的代表——上海银行间同业拆借利率（Shibor）的数据,采用向 量自回归模型（VAR）对两者的关系进行实证研究,研究结果表明：余额宝收益率与Shibor互 为因果,余额宝以上万亿的基金规模已经能够影响Shibor,当期的余额宝收益率和Shibor主要受 自身前期影响。Shibor的市场基准性仍需进一步加强;余额宝应加强自身经营,充分利用大数 据技术建立风险防范机制,提升风险管理效率;相关部门应重视对余额宝的监管,既要保证监 管的有效性,又要适度监管,为金融创新留下空间,维护金融市场的健康稳定发展。     

金融发展对中国经济增长的实证分析

本文通过对股票市场、银行储蓄、外商直接投资建立线性模型以分析这三者对中国经济发展的影响,根据OLS回归分析得出外商直接投资、银行储蓄、股票市场对中国经济都存在着明显的相关性,但其作用依次减弱,最后给出了相应的对策。     

Combining forecasts for universally optimal performance

There are two potential directions of forecast combination: combining for adaptation and combining for improvement. The former direction targets the performance of the best forecaster, while the latter attempts to combine forecasts to improve on the best forecaster. It is often useful to infer which goal is more appropriate so that a suitable combination method may be used. This paper proposes an AI-AFTER approach that can not only determine the appropriate goal of forecast combination but also intelligently combine the forecasts to automatically achieve the proper goal. As a result of this approach, the combined forecasts from AI-AFTER perform well universally in both adaptation and improvement scenarios. The proposed forecasting approach is implemented in our R package AIafter, which is available at https://github.com/weiqian1/AIafter.     

Fiscal rules to tame the political budget cycle: Evidence from Italian municipalities

The paper provides evidence that fiscal rules can limit the political budget cycle. It uses data on Italian municipalities during the 2000s and shows that: 1) municipalities are subject to political budget cycles in capital spending; 2) the Italian sub-national fiscal rule (Domestic Stability Pact, DSP) introduced in 1999 has been enforced by the central government; 3) municipalities subject to the fiscal rule show more limited political budget cycles than municipalities not subject to the rule. In order to identify the effect, we rely on the fact that the domestic fiscal rule does not apply to municipalities below 5000 inhabitants. We find that the political budget cycle increases real capital spending by about 10–20 percent on average in the years prior to municipal elections and that municipalities subject to the DSP show a pre-electoral increase in capital spending which is only a quarter of the one of municipalities not subject to the rule.     

A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition

This work presents key insights on the model development strategies used in our cross-learning-based retail demand forecast framework. The proposed framework outperforms state-of-the-art univariate models in the time series forecasting literature. It has achieved 17th position in the accuracy track of the M5 forecasting competition, which is among the top 1% of solutions.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.