社会学习与消费升级——来自中国电影市场的经验证据

文化服务产业消费“扩容”与“提质”对于中国当前消费升级至关重要。以2012—2016年中国1854部电影为研究对象,本文将每日电影院层面票房数据汇总至全国层面,构建“电影—日”全国票房数据。为避免弱工具变量问题,采用新近发展的套索(LASSO)回归选择最优的天气与空气污染变量作为电影首映周非预期票房的工具变量,识别了社会学习对电影跨期消费增长的影响。研究发现:受外生冲击影响的电影首映周非预期票房变化对于后续周票房存在显著的正向跨期溢出效应,且该溢出效应随着时间推移而逐渐减小。进一步地,制作质量越高、事前质量信息不确定性越高以及市场偏好越集中,电影消费的跨期溢出效应就越明显。这一结果表明,社会学习是引致电影消费跨期溢出效应的主要机制,观察学习与社会网络外部性等机制并未得到实证证据的支持。总体而言,本文揭示了社会学习是影响电影消费“扩容”与“提质”的关键因素,从个体间微观互动机制这一全新视角为激发中国新一轮消费升级提供了可行的实践路径。     

Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data

We propose an out-of-sample prediction approach that combines unrestricted mixed-data sampling with machine learning (mixed-frequency machine learning, MFML). We use the MFML approach to generate a sequence of nowcasts and backcasts of weekly unemployment insurance initial claims based on a rich trove of daily Google Trends search volume data for terms related to unemployment. The predictions are based on linear models estimated via the LASSO and elastic net, nonlinear models based on artificial neural networks, and ensembles of linear and nonlinear models. Nowcasts and backcasts of weekly initial claims based on models that incorporate the information in the daily Google Trends search volume data substantially outperform those based on models that ignore the information. Predictive accuracy increases as the nowcasts and backcasts include more recent daily Google Trends data. The relevance of daily Google Trends data for predicting weekly initial claims is strongly linked to the COVID-19 crisis.     

税收努力对金融发展的影响研究

搜集2002—2021年31个省区市的非平衡面板数据，运用LASSO回归测度税收努力，构建非线性计量模型考察税收努力与金融发展的关系。结果发现：税收努力与金融发展存在显著的倒U形关系，而财政缺口对二者关系起负向调节作用，经内生性处理和一系列稳健性检验后仍成立。异质性检验发现，消费税、西部地区、低出口退税率地区的税收努力与金融发展存在明显的倒U形关系，增值税税收努力决定了总税收努力对金融发展的负面影响。因此，在未来推进金融发展的过程中，既要保持适度税收努力，稳定财政缺口，又要规范地方政府行为，合理界定政府与市场的边界，为地方金融发展创造良好环境，助力实体经济高质量发展。     

Are cryptocurrencies homogeneous?

This article investigates if cryptocurrencies returns' are similarly affected by a selection of demand- and supply-side determinants. Homogeneity among cryptocurrencies is tested via a least absolute shrinkage and selection operator (LASSO) model where determinants of Bitcoin returns are applied to a sample of 12 cryptocurrencies. The analysis goes beyond existing research by simultaneously covering different periods and design choices of cryptocurrencies. The results show that cryptocurrencies are heterogeneous, apart from some similarities in the impact of technical determinants and cybercrime. The cryptocurrency market displays evidence of substitution effects, and design choices related explain the impact of the determinants of return.