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【第33期】Arthur Lewbel:Estimating Social Network Models with Missing Links

2023-11-12

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报告题目Estimating Social Network Models with Missing Links



主讲嘉宾Arthur Lewbel is a Professor of Economics at Boston College in Chestnut Hill, Massachusetts, USA. He is the inaugural holder of the Barbara A. and Patrick E. Roche Chair in Economics at BC. He is a co-editor of Econometric Theory, a former co-editor of The Journal of Business and Economic Statistics and of Economics Letters, and has also served on the editorial boards of The Journal of Econometrics and The Journal of Applied Econometrics. He is an elected fellow of the Econometric Society, a fellow of the Journal of Econometrics, and has a Multa Scripsit award from Econometric Theory.

Prof. Lewbel's research is mainly in the areas of microeconometrics and in consumer demand analysis. He has published 25 papers in the top 5 journals in economics, including 11 papers on Econometrica, 5 papers on the American Economic Review, 4 papers on the Journal of Political Economy, 4 papers on the Review of Economic Studies and 1 paper on the Quarterly Journal of Economics. In a study of over 55,000 economists (https://ideas.repec.org/top/top.ecm.html), he was ranked number 18 in the world based on the quality and quantity of publications.




报告摘要We propose an adjusted 2SLS estimator for social network models when some existing network links are missing from the sample. We show missing links add additional sources of regressor endogeneity, thus invalidating conventional estimators used in the literature. We resolve these issues by rescaling peer outcomes with estimates of missing rates and constructing instruments that exploit properties of noisy network measures. We apply our method to study peer effects in household decisions to participate in a microfinance program in India. We find that conventional instruments that ignore our missing link correction yield sizeable upward biases in peer effect estimates.



报告时间2023年11月14日(周二),16:30-18:00

线下地点厦门大学经济楼N302

线上地点腾讯会议 ID:393 3774 3329