复旦大学经济学院在2017年美国经济学会（AEA）年会上招聘的两位青年教师，近期在国际A类经济学期刊上发表了论文。
一位是宋弘老师，她和Jie Gong，Yi Lu合作的文章 “The Effect of Teacher Gender on Student's Academic and Noncognitive Outcomes” 刚被Journal of Labor Economics接受发表。
另一位是付中昊老师，他和洪永淼合作的文章 “A Model-Free Consistent Test for Structural Change in Regression Possibly with Endogeneity”刚被Journal of Econometrics接受发表。
美国经济学会（AEA）年会是目前全球规模最大的经济学、管理学招聘平台之一。该年会的招聘会每年吸引了全球上万名经济学家和优秀博士生参加，同时也吸引了许多用人单位前往招聘。
Song Hong, Jie Gong, Yi Lu, The Effect of Teacher Gender on Student's Academic and Non-cognitive Outcomes,Journal of Labor Economics, (forthcoming).
宋弘
复旦大学经济学院助理教授。她于2013年获得复旦大学经济学学士，2017年获得新加坡国立大学经济学博士。主要研究领域集中于劳动经济学、发展经济学和应用微观计量经济学。
The Effect of Teacher Gender on Student's Academic and Noncognitive Outcomes
教师性别对学生学业与非认识能力的影响
Song Hong
School of Economics, Fudan University
Jie Gong
National University of Singapore
Yi Lu
Tsinghua University
Abstract:
This paper examines the role of teacher genderin education production. We extend student outcomes from traditionally focused academic achievement to non-cognitive outcomes. Drawing on a representative survey of middle-school students in China, we focus on schools in which student-teacher assignments are random. Our results show that having a female teacher raises girls’ test scores and improves both their mental status and social acclimation relative to boys. There is evidence that female teachers provide feedback differently to girls and boys, and that having a female teacher alters girls’ beliefs about commonly held gender stereotypes and increases their motivation to learn.
Fu Zhonghao, Hong Yongmiao, A Model-Free Consistent Test for Structural Change in Regression Possibly with Endogeneity,Journal of Econometrics, (forthcoming).
付中昊
2009年中国人民大学本科毕业，2011年康奈尔硕士毕业，2017年康奈尔博士毕业，主要研究方向：计量经济学理论，时间序列分析，金融计量经济学。
A Model-Free Consistent Test for Structural Change in Regression Possibly with Endogeneity
存在内生性可能的回归的结构性变化的无模型一致性检验
Fu Zhonghao
School of Economics, Fudan University
Hong Yongmiao
School of Economics, Xiamen University
Department of Economics, Cornell University
Abstract:
Structural instability leads to misleading inference and imprecise prediction of timeseries models that assume stationarity. We propose a model-free consistent test for structural change in regression by testing instability of the Fourier transform of data. This novel approach avoids smoothed nonparametric estimation of the unknown regression function and so is free of the “curse of dimensionality” problem. Unlike the existing literature, we allow for endogenous and discrete regressors. By using a proper choice of weighting functions for the transform parameters in the Fourier transform, we avoid numerical integration so that our test statistic is easy to compute. Our test statistic has a convenient asymptotic N(0,1) distribution under the null hypothesis of no structural change and is consistent against a large class of smooth structural changes as well as abrupt structural breaks with unknown break dates. A Monte Carlo study and an empirical application show that our test performs reasonably well in finite samples.
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