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  5. Econometric Issues in Comparative Economics
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Econometric Issues in Comparative Economics

Paper Session

Saturday, Jan. 3, 2026 10:15 AM - 12:15 PM (EST)

Philadelphia Marriott Downtown, Independence Ballroom Salon III
Hosted By: Association for Comparative Economic Studies & Society for Institutional and Organizational Economics
  • Chairs:
    Sascha O. Becker, University of Warwick
  • Hans-Joachim Voth, University of Zurich

Using Multiple Outcomes to Adjust Standard Errors for Spatial Correlation

Stefano DellaVigna
,
University of California-Berkeley
Guido Imbens
,
Stanford University
Woojin Kim
,
Stanford University
David Ritzwoller
,
Stanford University

Abstract

Empirical research in economics often examines the behavior of agents located in a geographic space. In such cases, statistical inference is complicated by the interdependence of economic outcomes across locations. A common approach to account for this dependence is to cluster standard errors based on a predefined geographic partition. A second strategy is to model dependence in terms of the distance between units. Dependence, however, does not necessarily stop at borders and is typically not determined by distance alone. This paper introduces a method that leverages observations of multiple outcomes to adjust standard errors for cross-sectional dependence. Specifically, a researcher, while interested in a particular outcome variable, often observes dozens of other variables for the same units. We show that these outcomes can be used to estimate dependence under the assumption that the cross-sectional correlation structure is shared across outcomes. We develop a procedure, which we call Thresholding Multiple Outcomes (TMO), that uses this estimate to adjust standard errors in a given regression setting. We show that adjustments of this form can lead to sizable reductions in the bias of standard errors in calibrated U.S. county-level regressions. Re-analyzing nine recent papers, we find that the proposed correction can make a substantial difference in practice.

Testing Coefficient Variability in Spatial Regression

Ulrich K. Müller
,
Princeton University
Mark W. Watson
,
Princeton University

Abstract

This paper develops a test for coefficient stability in spatial regressions. The test is designed to have good power for a wide range of persistent patterns of coefficient variation, be applicable in a wide range of spatial designs, and to accommodate both spatial correlation and spatial heteroskedasticity in regressors and regression errors.

The test approximates the best local invariant test for coefficient stability in a Gaussian regression model with Lévy-Brown motion coefficient variation under the alternative, and is thus a spatial generalization of the Nyblom (1989) test of coefficient stability in time series regressions. An application to 1514 zip-code level bivariate regressions of U.S. socioeconomic variables reveals widespread coefficient instability.

Inference with Arbitrary Clustering

Fabrizio Colella
,
Università della Svizzera Italiana
Rafael Lalive
,
University of Lausanne
Seyhun Orcan Sakalli
,
King's College London
Mathias Thoenig
,
University of Lausanne

Abstract

Empirical work using spatial data routinely relies on Conley‑type heteroskedasticity‑ and autocorrelation‑robust (HAR) standard errors to account for spatial dependence. Yet these estimators are highly sensitive to the researcher’s choice of distance cutoff, generating the well‑known U‑shaped pattern in null‑rejection rates and leaving applied work vulnerable to arbitrary tuning decisions. We propose a Filtered Conley variance estimator that substantially reduces this sensitivity. Our approach identifies local spatial outliers using a combination of Local Moran’s I statistics and permutation-based reference distributions, and selectively switches off their spatial links when computing the variance–covariance matrix. This filtering procedure preserves meaningful spatial dependence while preventing outliers from distorting inference. Through Monte Carlo simulations calibrated to U.S. county‑level data, we show that the Filtered Conley estimator flattens the U‑shape in rejection rates and performs competitively with current alternative approaches. The method is simple to implement, data‑driven, and compatible with existing spatial and network‑robust inference frameworks.

How Much Should We Trust Research Using Cross-sectional Spatial Data?

Sascha O. Becker
,
University of Warwick
P. David Boll
,
University of Warwick
Hans-Joachim Voth
,
University of Zurich

Abstract

We re-estimate a large number of published papers in comparative economics to probe the robustness of published findings in light of recent advances in addressing spatial correlations. We present lessons for practitioners.
JEL Classifications
  • P5 - Comparative Economic Systems
  • C1 - Econometric and Statistical Methods and Methodology: General