Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust
            <i>t</i>
            -Statistic

Tabord-Meehan, Max

doi:10.1080/07350015.2017.1409630

Cited by 23 publications

(15 citation statements)

References 37 publications

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“…Paired observations also pose challenges related to the pattern of error correlations. For example, errors for dyad ij are correlated with errors for any other dyad featuring either i or j (Cameron and Miller 2014;Tabord-Meehan 2019). This makes two-way clustering inadequate.…”

Section: Empirical Strategymentioning

confidence: 99%

School friendship networks, homophily and multiculturalism: evidence from European countries

2021

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This paper investigates the determinants of school friendship networks among adolescents, proposing a model of network formation and estimating it using a sample of about 10,000 secondary school students in four countries: England, Germany, the Netherlands and Sweden. We test the idea that networks arise according to homophily along many characteristics (gender, school achievement and ethnic and cultural backgrounds), and assess the relative importance of each factor. In addition to gender, we find that country of origin, generational status and religion predict friendship for foreign-born students. For individuals born in the survey country, ties depend on a broader set of factors, including socioeconomic status and school achievement. In sum, homophilic preferences go considerably beyond ethnicity. Multiculturalism, which gives prominence to ethnic backgrounds, risks emphasising the differences in that dimension at the expense of affinity in others. Keywords Friendship • Homophily • Immigration • Networks • Social cohesion JEL Classification D85 • J15 • Z13 Earlier versions of this paper were presented at the 2nd Florence-Konstanz workshop on Behavioural and Experimental Social Sciences (University of Florence) and the 2nd International CILS4EU User Conference (Utrecht University). We thank participants at these meetings, Paolo Brunori, Editor Klaus F. Zimmermann and two anonymous referees for their comments and suggestions. Aldo Rustichini thanks the National Science Foundation for the grant SES 1728056, "Rules based policies, and Intelligence in Strateic Behavior"

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Section: Empirical Strategymentioning

confidence: 99%

School friendship networks, homophily and multiculturalism: evidence from European countries

2021

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“…Asymptotic standard errors with multi‐way clustering have been proposed by Cameron, Gelbach, and Miller (2011), and can be used for “plug‐in” asymptotic inference in the Gaussian limiting case—see also Cameron and Miller (2014), Aronow, Samii, and Assenova (2015), and Tabord‐Meehan (2019) for the case of dyadic data. A more recent paper by MacKinnon, Nielsen, and Webb (2021) gives a condition on cluster sizes that is sufficient for asymptotic normality and consistency of these standard errors, and proposes a bootstrap method for that setting.…”

Section: Introductionmentioning

confidence: 99%

Bootstrap With Cluster‐Dependence in Two or More Dimensions

Menzel

2021

ECTA

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We propose a bootstrap procedure for data that may exhibit cluster‐dependence in two or more dimensions. The asymptotic distribution of the sample mean or other statistics may be non‐Gaussian if observations are dependent but uncorrelated within clusters. We show that there exists no procedure for estimating the limiting distribution of the sample mean under two‐way clustering that achieves uniform consistency. However, we propose bootstrap procedures that achieve adaptivity with respect to different uniformity criteria. Important cases and extensions discussed in the paper include regression inference, U‐ and V‐statistics, subgraph counts for network data, and non‐exhaustive samples of matched data.

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“…Perhaps surprisingly however, theory has lagged behind this practice. To our knowledge, the only paper showing the asymptotic validity of inference based on Fafchamps and Gubert's suggestion for dyadic data is Tabord-Meehan (2019). Moreover, his result is restricted to OLS estimators only.…”

Section: Introductionmentioning

confidence: 98%

Empirical Process Results for Exchangeable Arrays

Davezies¹,

Guyonvarch²

2019

Preprint

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Exchangeable arrays are natural ways to model common forms of dependence between units of a sample. Jointly exchangeable arrays are well suited to dyadic data, where observed random variables are indexed by two units from the same population. Examples include trade flows between countries or relationships in a network. Separately exchangeable arrays are well suited to multiway clustering, where units sharing the same cluster (e.g. geographical areas or sectors of activity when considering individual wages) may be dependent in an unrestricted way. We prove uniform laws of large numbers and central limit theorems for such exchangeable arrays. We obtain these results under the same moment restrictions and conditions on the class of functions as with i.i.d. data. As a result, convergence and asymptotic normality of nonlinear estimators can be obtained under the same regularity conditions as with i.i.d. data. We also show the convergence of bootstrap processes adapted to such arrays.

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Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t -Statistic

Cited by 23 publications

References 37 publications

School friendship networks, homophily and multiculturalism: evidence from European countries

School friendship networks, homophily and multiculturalism: evidence from European countries

Bootstrap With Cluster‐Dependence in Two or More Dimensions

Empirical Process Results for Exchangeable Arrays

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