2021年人大计量与数量经济学系三位老师6篇论文被A类英文顶刊接受

时间：2023-01-20

　　来源：“中国人民大学经济学院” 微信公众号

　　计量与数量经济学系是中国人民大学经济学院适应大数据，人工智能、机器学习等科技创新技术的快速发展，定位于新兴学科发展以及交叉学科研究，完善学科布局而组建的新系。该系于2020年正式组建。2021年，计量与数量经济学学系在学术研究上取得了快速进步，有多位老师在中英文顶级期刊发表文章。

　　葛雷助理教授的文章被《Management science》接受待发表。《Credit Stimulus, Executive Ownership, and Firm Leverage》（Chakraborti, Dahiya , Ge, and Gete，forthcoming）用中国实证数据表明了，在2008年信贷扩张政策之后，公司高管对公司所有权的比重是信贷需求与高杠杆的重要驱动因素之一。

　　王莹副教授的三篇文章在《Journal of Econometrics》接受待发表。《Spurious functional-coefficient regression models and robust inference with marginal integration》（Tu, YD. and Wang, Y.）研究了变系数回归模型中的伪回归问题，提出了基于均衡回归的稳健统计推断方法来解决该问题。《Functional coefficient panel modeling with communal smoothing covariates》（Phillips, PCB. and Wang, Y.）提出了有共同平滑协变量的面板数据变系数模型，解决了该模型的估计和统计推断等理论问题。《When bias contributes to variance: True limit theory in functional coefficient cointegrating regression》（Phillips, PCB. and Wang, Y.）对函数系数协整模型进行了再研究，发现传统的偏差项可能会贡献渐进分布，并给出了模型完整的大样本理论结果。

　　李勇教授的两篇文章被《Journal of Econometrics》接受待发表，一篇文章被《经济学季刊》接受。《A Posterior-type Wald test for hypothesis testing》（Li,XB., Li,Y. Zeng,T.and Yu,J.）基于一种新的损失函数，提出了一种非常简单实用的假设检验的新方法；《Improved Marginal Likelihood Estimation via Power Posteriors and Importance Sampling》(Li,Y.Wang,N.L,,Yu,J.)提出了贝叶斯因子计算的快速而且精准的有效方法。

　　作者简介

　　葛雷

　　葛雷博士，中国人民大学经济学院助理教授，主要从事房地产，金融，计量经济学与机器学习等领域的研究。葛雷于2018年获得乔治城大学经济学博士。博士毕业后曾在联邦国家房地产债务协会（Fannie Mae）担任研究员，从事近三年的大数据与机器学习模型研究与开发。期间，其所开发并被广泛应用的大数据机器学习模型包括房屋售价预测模型，房产增值建议模型等。葛雷博士于2021年9月加入中国人民大学经济学院。

　　《Credit Stimulus, Executive Ownership, and Firm Leverage》

　　接受期刊

　　《Management science》

　　内容摘要

　　The Great Recession of 2008 triggered an extraordinarily large and rapid response by monetary authorities world-wide. A key feature of these policies was to provide banks with additional funds at a reduced cost. Agarwal et al. (2018) discuss this stimulus policy and note that “one goal was to encourage banks to expand credit to households and firms that would, in turn, increase their borrowing, spending, and investment”. Most of the literature examining the effectiveness of credit policies has focused on the “supply” side frictions that alter banks’ willingness to lend. Our paper takes a different approach. We study the “demand” side of credit policies, which is a relatively unexplored research area. In this paper, we focus on corporate borrowers. We provide evidence that the structure of executive compensation is an important determinant of the transmission of credit policies. In this regard, our results complement the growing literature that links compensation policies and risk-taking.

　　作者简介

　　王莹

　　王莹，中国人民大学经济学院副教授，2017年博士毕业于北京大学，2017-2020于新西兰奥克兰大学从事博士后研究，2020年加入中国人民大学，研究方向为理论计量经济学，主要兴趣包括时间序列分析，非参数方法，变系数模型等，多篇研究论文发表于Journal of Econometrics, Oxford Bulletin of Economics and Statistics等期刊杂志，主持国家自然科学基金青年项目一项。

　　《Spurious functional-coefficient regression models and robust inference with marginal integration》

　　接受期刊

　　《Journal of Econometrics》

　　内容摘要

　　Functional-coefficient cointegrating models have become popular to model nonlinear nonstationarity in econometrics (Cai et al., 2009; Xiao, 2009). However, there is rare study on testing the existence of functional-coefficient cointegration. Consequently, functional-coefficient regressions involving nonstationary regressors may be spurious. This paper investigates the effect that spurious functional-coefficient regression has on the model diagnostics. We find that common characteristics of spurious regressions are manifest, including divergent local significance tests, random local goodness-of-fit, and local Durbin–Watson ratio converging to zero, complementing those discovered in spurious linear and nonparametric regressions (Phillips, 1986, 2009). In addition, spuriousness causes the divergences of the global significance tests proposed by Xiao (2009) and Sun et al. (2016), which are likely to produce misleading conclusions for practitioners when cointegration tests fail to reject a spurious regression. To resolve the problems, we propose a simple-to-implement inference procedure based on a semiparametric balanced regression, by augmenting regressors of the original spurious regression with lagged dependent variable and independent variables, with the aid of the marginal integration. This procedure achieves spurious regression detection via standard nonparametric inferential asymptotics, and is found robust to the true relationship between the integrated processes. The theoretical results are also corroborated by simulations.

　　《Functional coefficient panel modeling with communal smoothing covariates》

　　接受期刊

　　《Journal of Econometrics》

　　内容摘要

　　Behavior at the individual level in panels or at the station level in spatial models is often influenced by aspects of the system in aggregate. In particular, the nature of the interaction between individual-speci?c explanatory variables and an individual dependent variable may be a?ected by `global’ variables that are relevant in decision making and shared communally by all individuals in the sample. To capture such behavioral features, we employ a functional coe?icient panel model in which certain communal covariates may jointly influence panel interactions by means of their impact on the model coe?icients. Two classes of estimation procedures are proposed, one based on station averaged data the other on the full panel, and their asymptotic properties are derived. Inference regarding the functional coe?icient is also considered. The ?nite sample performance of the proposed estimators and tests are examined by simulation. An empirical spatial model illustration is provided in which the climate sensitivity of temperature to atmospheric CO_2 concentration is studied at both station and global levels.

　　《When bias contributes to variance: True limit theory in functional coefficient cointegrating regression》

　　接受期刊

　　《Journal of Econometrics》

　　内容摘要

　　Limit distribution theory in the econometric literature for functional coefficient cointegrating regression is incorrect in important ways, influencing rates of convergence, distributional properties, and practical work. The correct limit theory reveals that components from both bias and variance terms contribute to variability in the asymptotics. The errors in the literature arise because random variability in the bias term has been neglected in earlier research. In stationary regression this random variability is of smaller order and can be ignored in asymptotic analysis but not without consequences for finite sample performance. Implications of the findings for rate efficient estimation are discussed. Simulations in the Online Supplement provide further evidence supporting the new limit theory in nonstationary functional coefficient regressions.

　　作者简介

　　李勇

　　李勇，金融学和计量经济学教授，博士生导师，现任中国人民大学经济学院副院长、香港中文大学统计学博士、新加坡管理大学金融学博士后、中国人民大学量化投资研究所所长。长期以来从事金融计量经济学、量化投资、资产配置方面的研究，培养量化投资和资产配置方向硕士博士生近150名，多名学生赴沃顿商学院、纽约大学等海外高校深造。在中英文顶级期刊如《Journal of Econometrics》《经济研究》《管理世界》等学术杂志共发表文章近50篇，其中SSCI/SCI收录近50篇，出版学术专著一部，编著一部。

　　《A Posterior-type Wald test for hypothesis testing》

　　接受期刊

　　《Journal of Econometrics》

　　内容摘要

　　A new Wald-type statistic is proposed for hypothesis testing based on Bayesian posterior distributions under the correct model specification. The new statistic can be explained as a posterior version of the Wald statistic and has several nice properties. First, it is well-defined under improper prior distributions. Second, it avoids JeffreysLindley-Bartlett’s paradox. Third, under the null hypothesis and repeated sampling, it follows a χ 2 distribution asymptotically, offering an asymptotically pivotal test. Fourth, it only requires inverting the posterior covariance for parameters of interest. Fifth and perhaps most importantly, when a random sample from the posterior distribution (such as MCMC output) is available, the proposed statistic can be easily obtained as a by-product of posterior simulation. In addition, the numerical standard error of the estimated proposed statistic can be computed based on random samples. A robust version of the test statistic is developed under model misspecification and inherits many nice properties of the new posterior statistic. The finite sample performance of the statistics is examined in Monte Carlo studies. The method is applied to two latent variable models used in microeconometrics and financial econometrics.

　　《Improved Marginal Likelihood Estimation via Power Posteriors and Importance Sampling》

　　接受期刊

　　《Journal of Econometrics》

　　内容摘要

　　Power posteriors have become popular in estimating the marginal likelihood of a Bayesian model. A power posterior is referred to as the posterior distribution that is proportional to the likelihood raised to a power b ∈ [0, 1]. Important power-posteriorbased algorithms include thermodynamic integration (TI) of Friel & Pettitt (2008) and steppingstone sampling (SS) of Xie et al. (2011). In this paper, it is shown that the Bernstein-von Mises (BvM) theorem holds for power posteriors under regularity conditions. Due to the BvM theorem, power posteriors, when adjusted by the square root of the auxiliary constant, have the same limit distribution as the original posterior distribution, facilitating the implementation of the modified TI and SS methods via importance sampling. Unlike the TI and SS methods that require repeated sampling from the power posteriors, the modified methods only need the original posterior output and hence, are computationally more efficient. Moreover, they completely avoid the coding efforts associated with sampling from the power posteriors. Primitive conditions, under which the TI and modified TI algorithms can produce consistent estimators of the marginal likelihood, are provided. The numerical efficiency of the proposed methods is illustrated using two models.

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