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Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data
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AI
DOI：10.1257/aer.20170861.png)
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摘要
En 中文
Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form how many of your links have trait k? Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.
Keyword:
FINANCIAL NETWORKS
SOCIAL NETWORKS
ARRAYS
MODELS
LIMITS
RISK
期刊
IF:
11.6
论文数:
5.0K
被引数:
7.5W
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