How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?

We investigate how economic policy uncertainty (EPU) and geopolitical risks (GPR) impact Bitcoin volatility with respect to factors related to type and nationality of uncertainty, investigated period, relationship horizon and extreme conditions. Applying ARDL model and quantile regression for monthly data from August 2010 to September 2021, we reveal that June 2014 corresponds to a key date that marks a reversal in the investigated relationship. Furthermore, we show that the relationship between uncertainty and bitcoin volatility changes according to different factors. US uncertainty has short run effects on Bitcoin volatility, while China’s uncertainty has rather long run effects. Moreover, Bitcoin volatility responds in the same manner to US EPU and GPR, while, it responds differently to China's EPU and GPR. In extreme quantiles, we find that Bitcoin hedges against US EPU and GPR. Further, Bitcoin hedges against either individual or joint effects of US uncertainty, but not both.     

结构异质性对中国能源高质量发展的影响研究

基于结构异质性对能源高质量发展影响的理论分析,使用稀疏主成分分析方法测度中国能源高质量发展水平,构建中国和八大经济区域面板分位数回归模型,实证研究了结构异质性对中国能源高质量发展的影响。结果表明：2003—2020年中国能源高质量发展水平有所提升,但仍存在明显的区域发展不平衡问题,西部地区和北部沿海地区的能源高质量发展水平更高。产业结构与能源高质量发展水平呈负相关关系,能源消费结构、资本配置结构与能源高质量发展水平呈正相关关系。     

我国上市公司年度报告信息披露结构的实证分析

以上市公司强制性信息披露水平、自愿性信息披露以及总体信息披露水平的显著性检验为基础,建立上市公司自愿信息披露指数与其影响因素之间的多元线性回归模型,来试图找出我国上市公司年度报告信息披露的结构特征,从而为改进我国上市公司信息披露水平提供最直接的证据。     

上市公司信息披露违规影响因素的实证研究

针对目前中国上市公司信息披露违规事件的频繁发生,选取2003年2月至2004年8月被公开谴责的上市公司作为样本,通过分层随机抽样引入配对样本———期间未被谴责的上市公司,从行业特征、股权结构、规模、盈利能力、负债状况等方面分析被谴责上市公司的特征,实证研究上市公司发生信息披露违规的影响因素。研究结果表明,行业特征、公司的规模以及盈利能力对其发生信息披露违规有显著的影响作用。     

回归分析在土地估价中的应用

回归分析是一种统计学方法，在地价分析与测算中得到广泛应用。本文从统计学的角度阐明了回归分析的原理，并介绍了如何建立回归方程解决土地估价中出现的有关问题。     

货币政策影响居民储蓄行为的有效性分析

本选取了我国1994年至2000年的统计数据，通过统计学的方法来分析货币政策在分流我国居民的巨额储蓄时是否有效，并进一步分析了对货币政策效果产生干扰的制度因素。     

探究“新零销售”模式下影响销售量的相关因素

在新零销售模式的推动下,客户越来越倾向于消费个性化的商品。为了精准预测客户的消费需求,论文以单款单色产品为研究对象,通过建立多元回归分析的数学模型,来探究影响商品销售量的相关因素。经过分析发现,除了一些定性因素外,定量因素对销售量也产生了一定的影响,其中实际花费总金额、实际销售单价、库存数等定量因素对销售量的影响较大,且各个变量之间具有相关性,所以电商平台应特别关注这三个变量的影响。     

川渝毗邻地区合作示范区协同发展研究 ———基于哈肯模型的实证分析

摘 要：本文将川渝毗邻地区合作共建区域发展功能平台统称为川渝毗邻地区合作示范区（以下简称“示范区”）,并以此为研究对象,通过泰尔L指数,测度示范区经济发展差异,随后运用哈肯模型、耦合协调和灰色关联度等方法进行协调度分析。研究发现,各示范区经济发展差异较小,经济发展趋同,加剧示范区之间的竞争,削弱协同合作,抑制比较优势的发挥和提升;成渝“双核”对示范区的辐射带动作用不明显,示范区总体与成渝“双核”的差距仍然较大;在示范区协同发展中,经济金融活动发挥着主导作用,经济环境与金融支持协调发展趋势向好,金融支持对示范区协同发展有显著的正向影响。基于此,建议大力发展新型金融业态,为优势产业发展营造良好的金融支持环境;开辟特色金融“试验区”,引入特色金融活水,有针对性地加强各示范区优势产业和特色产业的金融支持,进一步提升比较优势,强化示范区与成渝“双核”的经济联系,为成渝地区双城经济圈高质量发展提供重要支撑。     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

Public-private sector wage differentials in Germany: Evidence from quantile regression

This paper measures and decomposes the differences in earnings distributions between public sector and private sector employees in Germany for the years 1984–2001. Oaxaca decomposition results suggest that conditional wages are higher in the public sector for women but lower for men. Using the quantile regression decomposition technique proposed by Machado and Mata (2004), we find that the conditional distribution of wages is more compressed in the public sector. At the low end of wages, differences in characteristics explain less than the raw wage gap when it is the opposite at high wages. Separate analyses by work experience and educational groups reveal that the most experienced employees and those with basic schooling do best in the public sector. All these results are stable over the 80s and 90s.I thank Michael Lechner and the Centre for European Economic Research (ZEW), Mannheim, for letting me work with the full sample of the German Socio-Economic Panel (GSOEP). I am grateful to an anonymous referee and particularly to the editor, Bernd Fitzenberger, for providing me detail comments and suggestions that have significantly improved the paper. I have also benefited from discussions with Michael Lechner and Ruth Miquel.First version received: April 2002/Final version received: June 2004