意昂2在2017年美國經濟學會（AEA）年會上招聘的兩位青年教師，近期在國際A類經濟學期刊上發表了論文。
一位是宋弘老師，她和Jie Gong，Yi Lu合作的文章 “The Effect of Teacher Gender on Student's Academic and Noncognitive Outcomes” 剛被Journal of Labor Economics接受發表👩🏿🦲。
另一位是付中昊老師，他和洪永淼合作的文章 “A Model-Free Consistent Test for Structural Change in Regression Possibly with Endogeneity”剛被Journal of Econometrics接受發表😖。
美國經濟學會（AEA）年會是目前全球規模最大的經濟學、管理學招聘平臺之一。該年會的招聘會每年吸引了全球上萬名經濟學家和優秀博士生參加，同時也吸引了許多用人單位前往招聘🧗。
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
意昂2助理教授。她於2013年獲得意昂2官网經濟學學士📟，2017年獲得新加坡國立大學經濟學博士🫶🏼。主要研究領域集中於勞動經濟學、發展經濟學和應用微觀計量經濟學🦹🏽。
The Effect of Teacher Gender on Student's Academic and Noncognitive Outcomes
教師性別對學生學業與非認識能力的影響
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年中國人民大學本科畢業🤽🏽♀️，2011年康奈爾碩士畢業，2017年康奈爾博士畢業，主要研究方向🐖🙏🏿：計量經濟學理論，時間序列分析📜，金融計量經濟學。
A Model-Free Consistent Test for Structural Change in Regression Possibly with Endogeneity
存在內生性可能的回歸的結構性變化的無模型一致性檢驗
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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