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来源类型Working Paper
规范类型报告
DOI10.3386/w25270
来源IDWorking Paper 25270
Non-Randomly Sampled Networks: Biases and Corrections
Chih-Sheng Hsieh; Stanley I. M. Ko; Jaromír Kovářík; Trevon Logan
发表日期2018-11-19
出版年2018
语种英语
摘要This paper analyzes statistical issues arising from networks based on non-representative samples of the population. We first characterize the biases in both network statistics and estimates of network effects under non-random sampling analytically and numerically. Sampled network data systematically bias the properties of population networks and suffer from non-classical measurement-error problems when applied as regressors. Apart from the sampling rate and the elicitation procedure, these biases depend in a nontrivial way on which subpopulations are missing with higher probability. We propose a methodology, adapting post-stratification weighting approaches to networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The advantages of the proposed methodology are that it can be applied to network data collected via both designed and non-designed sampling procedures, does not require one to assume any network formation model, and is straightforward to implement. We apply our approach to two widely used network data sets and show that accounting for the non-representativeness of the sample dramatically changes the results of regression analysis.
主题Econometrics ; Estimation Methods ; Microeconomics ; Economics of Information ; Industrial Organization ; Market Structure and Firm Performance ; Other ; Culture
URLhttps://www.nber.org/papers/w25270
来源智库National Bureau of Economic Research (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/582944
推荐引用方式
GB/T 7714
Chih-Sheng Hsieh,Stanley I. M. Ko,Jaromír Kovářík,et al. Non-Randomly Sampled Networks: Biases and Corrections. 2018.
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