住房保障与财富效应逆转——基于平滑转换回归方法的实证分析

本文采取非线性的平滑转换回归方法,对我国保障房供给影响财富效应的程度进行了研究,结果表明:保障房的供给会使得房地产价格的财富效应发生V型逆转,即当保障房供给处于低水平阶段时,房地产价格的财富效应为负向,房价上涨会抑制消费增加;当保障房供给处于高水平阶段时,房地产价格具有正向的财富效应,房价上涨促进消费增加。这说明存在一个保障房供给的最低门槛,只有跨越这个门槛,才能有利于发挥房地产价格的财富效应,进而促进消费。从对全国各省份的数据分析发现,西部地区的大部分省份已经跨越了保障房供给的最低门槛,而东部地区较多的省份,连续几年都低于这个门槛值。因此,应积极建立健全保障性安居工程的融资机制,合理协调好保障房与商品房用地之间的关系,进而促进消费。     

林改后农户对集体公益林投入意愿影响因素分析——以天保工程区武隆县、会理县和玉龙县为例

文章运用文献分析、问卷调查与计量经济分析相结合的方法对林改后影响农户对集体公益林投入意愿的因素进行分析。结果显示,家庭总收入、家庭林业收入、户主文化程度等因素对农户投入意愿的选择影响显著。提出完善公益林补偿机制、搞活公益林经营机制、加强公益林政策宣传、增加农户林业收入、促进公益林建设等建议。     

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.     

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.     

Green human resource management: Development of a valid measurement scale

The objective of the study was to develop a valid measurement scale for green human resource management (HRM). Even though the common practices of green HRM have been presented in much of the literature, the previous studies focused only on a small number of functions in integrating environmental management with HRM. Additionally, the measurement of green HRM practices still calls for empirical validation. The two‐stage methodology of structural equation modeling in AMOS was employed for data analysis. Exploratory factor analysis revealed seven dimensions of the construct measured by 28 items. Confirmatory factor analysis confirmed the factor structure. The measuring instruments revealed convergent and discriminant validity. Several model fit indices indicated the model fitness. The study provided supplementary evidence on the underlying structure of the construct that can be valuable to researchers and practitioners in this area.     

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.