近期,ZIBS助理教授周闻宇以通讯作者身份在经济学国际顶尖学术期刊Journal of Business & Economic Statistics上发表合作论文“Uniform Nonparametric Inference for Spatially Dependent Panel Data”。这项研究的合作者为新加坡管理大学李嘉教授、美国加州大学洛杉矶分校廖志鹏教授。
Recently, a research paper by ZIBS Assistant Professor ZHOU Wenyu, titled “Uniform Nonparametric Inference for Spatially Dependent Panel Data,” was formally accepted by the Journal of Business & Economic Statistics, a leading journal in economics. This work was co-authored with Prof. Jia Li from SMU and Prof. Zhipeng Liao from UCLA.

在这篇论文中,研究团队提出了一种针对具有未知形式的“空间—时序”相关性面板数据的非参数回归模型的一致函数检验方法。该方法仅要求面板数据在时间维度上具有延展性,而对截面大小和空间相关性无任何约束。研究团队开发了一种新的高维高斯耦合理论,用于推导该统计检验方法的理论性质。该方法在经济学、金融学以及数据科学中具有广泛的潜在应用。研究团队将这一新的统计检验方法用于研究资产波动率和交易量之间的复杂关系以及宏观预测中可能存在的非理性行为。(点击文末“阅读原文”了解更多)

Journal of Business & Economic Statistics(JBES)是经济学、统计学领域公认的国际顶尖学术期刊,由美国统计协会出版,主要关注商业、经济、金融领域的统计研究问题。该期刊为浙江大学经济学科A类国际学术期刊、ABS4星期刊,过去五年影响影子为4.8。
摘要
This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a longtime span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method in two empirical settings. One concerns the nonparametric relationship between asset price volatility and trading volume as depicted by the mixture of distribution hypothesis. The other pertains to testing the rationality of survey-based forecasts, in which we document nonparametric evidence for information rigidity among professional forecasters, offering new support for sticky-information and noisy-information models in macroeconomics.
作者简介
周闻宇
ZIBS助理教授

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