Non-representative sampled networks: Estimation of network structural properties by weighting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23330%2F24%3A43971884" target="_blank" >RIV/49777513:23330/24:43971884 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0304407624000356" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0304407624000356</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jeconom.2024.105689" target="_blank" >10.1016/j.jeconom.2024.105689</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-representative sampled networks: Estimation of network structural properties by weighting

Popis výsledku v původním jazyce

This paper analyzes statistical issues arising from non-representative samples of a network. Sampled network data could systematically bias the network properties and generate non-classical measurement error problems. Apart from the sampling rate and the elicitation procedure, the biases on network structural measures depend non-trivially on which subpopulations of nodes are missing with higher probability. We propose a methodology, adapting weighted estimators to networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The proposed weighted estimators are consistent and asymptotically normally distributed and have good performance in finite samples. Notably, our approach does not require users to assume any network formation model and is straightforward to implement.

Název v anglickém jazyce

Non-representative sampled networks: Estimation of network structural properties by weighting

Popis výsledku anglicky

This paper analyzes statistical issues arising from non-representative samples of a network. Sampled network data could systematically bias the network properties and generate non-classical measurement error problems. Apart from the sampling rate and the elicitation procedure, the biases on network structural measures depend non-trivially on which subpopulations of nodes are missing with higher probability. We propose a methodology, adapting weighted estimators to networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The proposed weighted estimators are consistent and asymptotically normally distributed and have good performance in finite samples. Notably, our approach does not require users to assume any network formation model and is straightforward to implement.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50401 - Sociology

Projekt

<a href="/cs/project/GA21-22796S" target="_blank" >GA21-22796S: Difuze v sítích a sociální koheze: vliv koeficientu shlukování</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Econometrics

ISSN

0304-4076

e-ISSN

1872-6895

Svazek periodika

240

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

001181492300001

EID výsledku v databázi Scopus

2-s2.0-85185460966