# Inference for Dependent Data with Learned Clusters

@inproceedings{Cao2021InferenceFD, title={Inference for Dependent Data with Learned Clusters}, author={Jianfei Cao and Christian Hansen and Damian Kozbur and Lucciano Villacorta}, year={2021} }

This paper 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…

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## References

SHOWING 1-10 OF 46 REFERENCES

### Inference with dependent data using cluster covariance estimators

- Mathematics, Economics
- 2011

### Panel Data with Unknown Clusters

- Economics, Computer Science
- 2021

This work proposes a procedure to help researchers discover clusters in panel data based on thresholding an estimated long-run variance-covariance matrix and requires the panel to be large in the time dimension, but imposes no lower bound on the number of units.

### Spatial Correlation Robust Inference

- Economics, Mathematics
- 2021

We propose a method for constructing conﬁdence intervals that account for many forms of spatial correlation. The interval has the familiar ‘estimator plus and minus a standard error times a critical…

### t-Statistic Based Correlation and Heterogeneity Robust Inference

- Mathematics
- 2007

We develop a general approach to robust inference about a scalar parameter of interest when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the…

### FIXED-b ASYMPTOTICS FOR SPATIALLY DEPENDENT ROBUST NONPARAMETRIC COVARIANCE MATRIX ESTIMATORS

- MathematicsEconometric Theory
- 2014

This paper develops a method for performing inference using spatially dependent data. We consider test statistics formed using nonparametric covariance matrix estimators that account for…

### Asymptotic theory for clustered samples

- MathematicsJournal of Econometrics
- 2019

We provide a complete asymptotic distribution theory for clustered data with a large number of groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and…

### HAC ESTIMATION BY AUTOMATED REGRESSION

- Computer ScienceEconometric Theory
- 2005

A simple regression approach to HAC and LRV estimation that exploits the fact that the quantities of interest relate to only one point of the spectrum (the origin) and shows that its properties are comparable to those of conventional HAC estimates constructed from quadratic kernels.

### Randomization Tests Under an Approximate Symmetry Assumption

- Mathematics, Computer Science
- 2017

Conditions under which the same construction can be used to construct tests that asymptotically control the probability of a false rejection whenever the distribution of the observed data exhibits approximate symmetry in the sense that the limiting distribution of a function of the data exhibits symmetry under the null hypothesis are provided.