Arbeitspapier

Robust inference with clustered data

to connected objects

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-specific fixed effects, few clusters, multi-way clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.

Language: Englisch

Bibliographic citation: Series: Working Paper ; No. 10-7

Classification: Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models

Subject: cluster robust
random effects
fixed effects
differences in differences
cluster bootstrap
few clusters
multi-way clusters

Event: Geistige Schöpfung

(who): Cameron, A. Colin
Miller, Douglas L.

Event: Veröffentlichung

(who): University of California, Department of Economics

(where): Davis, CA

(when): 2010

Handle: http://hdl.handle.net/10419/58373

Last update: 10.03.2025, 11:44 AM CET

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Object type

Arbeitspapier

Associated

Cameron, A. Colin
Miller, Douglas L.
University of California, Department of Economics

Time of origin

2010

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