Inference for Dependent Data with Learned Clusters

Bibliographic Data

ID19314723
AuthorsJianfei Cao (0000-0001-9452-2990, Northeastern University, corresponding author), Christian Hansen (0000-0002-5734-7529, University of Chicago Booth School of Business, corresponding author), Damian Kozbur (University of Zurich, corresponding author), Lucciano Villacorta (Banco Central de Chile, corresponding author)
Year2025
Volume107
Issue6
Pages1684-1701
Publication date2025-11-25
Open AccessNo
TypeARTICLE
VenueThe Review of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0034-6535 • E-ISSN: 1530-9142
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/rest_a_01460
OpenAlexW4399765306
LanguageEN
References cited30

This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the article allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The article gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data

Inference · Machine learning · Advanced Clustering Algorithms Research · Artificial Intelligence · Computer Science · Machine Learning and Algorithms

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