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Non-Randomly Sampled Networks: Biases and Corrections -- by Chih-Sheng Hsieh, Stanley I. M. Ko, Jaromir Kovarik, Trevon LoganThis paper analyzes statistical issues arising from non-representative network samples of the population, the most common network data used. We first characterize the biases in both network statistics and estimates of network effects under non-random sampling theoretically and numerically. Sampled network data systematically bias the properties of observed networks and suffer from non-classical measurement-error problems if applied as regressors. Apart from the sampling rate and the elicitation procedure, these biases depend in a non-trivial way on which subpopulations are missing with higher probability. We then 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 use Monte Carlo simulation and two widely used empirical network data sets to show that accounting for the non-representativeness of the sample dramatically changes the results of regression analysis.
http://papers.nber.org/papers/w25270#fromrss
http://papers.nber.org/papers/w25270#fromrssRationalizing Rational Expectations? Tests and Deviations -- by Xavier D'Haultfoeuille, Christophe Gaillac, Arnaud MaurelIn this paper, we build a new test of rational expectations based on the marginal distributions of realizations and subjective beliefs. This test is widely applicable, including in the common situation where realizations and beliefs are observed in two different datasets that cannot be matched. We show that whether one can rationalize rational expectations is equivalent to the distribution of realizations being a mean-preserving spread of the distribution of beliefs. The null hypothesis can then be rewritten as a system of many moment inequality and equality constraints, for which tests have been recently developed in the literature. Next, we go beyond testing by defining and estimating the minimal deviations from rational expectations that can be rationalized by the data. In the context of structural models, we build on this concept to propose an easy-to-implement way to conduct a sensitivity analysis on the assumed form of expectations. Finally, we apply our framework to test for and quantify deviations from rational expectations about future earnings, and examine the consequences of such departures in the context of a life-cycle model of consumption.
http://papers.nber.org/papers/w25274#fromrss
http://papers.nber.org/papers/w25274#fromrss