Spatial Correlation Robust Inference

Ulrich K. Müller; Mark W. Watson

doi:10.3982/ecta19465

journal article Jan 01, 2022

Spatial Correlation Robust Inference

Ulrich K. Müller Mark W. Watson

Econometrica Vol. 90 No. 6 pp. 2901-2935 · JSTOR

View at Publisher Save 10.3982/ecta19465

Abstract

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 value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.

Topics

No keywords indexed for this article. Browse by subject →

References

46

[1]

Andrews Econometrica (1991) 10.2307/2938229

[2]

Bakirov Zapiski Nauchnyh Seminarov POMI (2005)

[3]

Berti Stochastics: An International Journal of Probability and Stochastic Processes (2006) 10.1080/17442500600745359

[4]

Bester Journal of Econometrics (2011) 10.1016/j.jeconom.2011.01.007

[5]

Bester Econometric Theory (2016) 10.1017/s0266466614000814

[6]

Cao, Jianfei, Christian Hansen, Damian Kozbur, and Lucciano Villacorta (2020): “Inference for Dependent Data With Learned Clusters,” Working paper.

[7]

Chan The Annals of Statistics (1987) 10.1214/aos/1176350492

[8]

Conley Journal of Econometrics (1999) 10.1016/s0304-4076(98)00084-0

[9]

Conley Journal of Econometrics (2007) 10.1016/j.jeconom.2006.09.003

[10]

Dou, Liyu (2020): “Optimal HAR Inference,” Working Paper, CUHK-Shenzen.

[11]

Elliott Econometrica (2015) 10.3982/ecta10535

[12]

Harkrishan (2017)

[13]

Henderson Quarterly Journal of Economics (2018) 10.1093/qje/qjx030

[14]

Ibragimov Journal of Business and Economic Statistics (2010) 10.1198/jbes.2009.08046

[15]

Ibragimov Review of Economics and Statistics (2016) 10.1162/rest_a_00545

[16]

Kato Communications in Mathematical Physics (1987) 10.1007/bf01238911

[17]

Kelejian Journal of Econometrics (2007) 10.1016/j.jeconom.2006.09.005

[18]

Kelly, Morgan (2019): “The Standard Errors of Persistence,” University College Dublin WP19/13. 10.2139/ssrn.3398303

[19]

Kiefer Econometric Theory (2005) 10.1017/s0266466605050565

[20]

Kiefer Econometrica (2000) 10.1111/1468-0262.00128

[21]

Kim Journal of Econometrics (2011) 10.1016/j.jeconom.2010.10.002

[22]

Lahiri Sankhya (2003)

[23]

Lahiri Bernoulli (2016) 10.3150/14-bej661

[24]

Lazarus Journal of Business and Economic Statistics (2018) 10.1080/07350015.2018.1506926

[25]

Marshall (2011) 10.1007/978-0-387-68276-1

[26]

Müller Journal of Econometrics (2007) 10.1016/j.jeconom.2007.01.019

[27]

Müller Journal of Business and Economic Statistics (2014) 10.1080/07350015.2014.931238

[28]

Müller Review of Economics and Statistics

[29]

Müller Journal of Econometrics (2019) 10.1016/j.jeconom.2018.11.016

[30]

Müller Econometrica (2008) 10.3982/ecta6814

[31]

Müller Review of Economic Studies (2016) 10.1093/restud/rdw003

[32]

Müller (2017)

[33]

Müller Journal of Business and Economic Statistics

[34]

Spatial Correlation Robust Inference

Ulrich K. Müller, Mark W. Watson

Econometrica 10.3982/ecta19465

[35]

A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

Whitney K. Newey, Kenneth D. West

Econometrica 1987 10.2307/1913610

[36]

Phillips Biometrika (1987) 10.1093/biomet/74.3.535

[37]

Phillips Econometric Theory (2005)

[38]

Pötscher Econometrica (2002) 10.1111/1468-0262.00318

[39]

Rho Econometric Theory (2019) 10.1017/s0266466618000117

[40]

Robinson Econometric Theory (2005) 10.1017/s0266466605050115

[41]

Rosasco Journal of Machine Learning Research (2010)

[42]

Saitoh (2016) 10.1007/978-981-10-0530-5

[43]

Sun The Econometrics Journal (2013) 10.1111/j.1368-423x.2012.00390.x

[44]

Sun Review of Economics and Statistics (2015) 10.1162/rest_a_00441

[45]

Taqqu Advances in Applied Probability (1975) 10.2307/1426060

[46]

Wang Journal of Theoretical Probability (2014) 10.1007/s10959-013-0483-2

Cited By

33

Paths that led to gold: Historical roads, trade, and persistence

Alexandre Portugal, Bruno Barsanetti · 2026

Regional Science and Urban Economic...

Spatial Correlation Robust Inference

Ulrich K. Müller, Mark W. Watson · 2022

Econometrica

Metrics

33

Citations

46

References

Details

Published: Jan 01, 2022
Vol/Issue: 90(6)
Pages: 2901-2935

Authors

U

Ulrich K. Müller

Department of Economics, Princeton University

M

Mark W. Watson

Department of Economics, Princeton University

Funding

National Science Foundation

Cite This Article

Ulrich K. Müller, Mark W. Watson (2022). Spatial Correlation Robust Inference. Econometrica, 90(6), 2901-2935. https://doi.org/10.3982/ecta19465

Spatial Correlation Robust Inference

You May Also Like