The DOI matches its Crossref record; 34 of the 37 references it cites were found in a registry or an index.
Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data
Abstract
Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or “spatial” dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples.
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“Deviating from the previous empirical studies such as Dünhaupt (2017), I use Driscoll and Kraay's (1998) method to examine the linear relationship between financialization and the labor share of national”
From Financialization and the Labor Share of Income (2019) · cites this paper as Driscoll and Kraay's (1998)
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“as in Driscoll and Kraay 1998”
From An Introductory Guide to Event Study Models (2023) · cites this paper as Driscoll and Kraay 1998
Citations
Cites
A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity
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Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix
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An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias
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Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
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- 10.2307/2281644 · DOI
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A Practitioner’s Guide to Cluster-Robust Inference
Colin Cameron, A., & Miller, D. L. (2015) · Journal of Human Resources · doi:10.3368/jhr.50.2.317 · Cited by 5,037
Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence
Hoechle, D. (2007) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0700700301 · Cited by 3,462
Government Spending Multipliers in Good Times and in Bad: Evidence from US Historical Data
Ramey, V., & Zubairy, S. (2017) · Journal of Political Economy · doi:10.1086/696277 · Cited by 1,422
Testing for Cross-Sectional Dependence in Panel-Data Models
Hoyos, R. E. D., & Sarafidis, V. (2006) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x0600600403 · Cited by 1,405
Under the hood Issues in the specification and interpretation of spatial regression models
Anselin, L. (2002) · Agricultural Economics · doi:10.1016/s0169-5150(02)00077-4 · Cited by 1,296
Metadata
- Authors
- Driscoll, J. C., & Kraay, A. C.
- Affiliations
- Brown University (US)
- World Bank (US)
- Venue
- Review of Economics and Statistics
- Publisher
- MIT Press
- Published
- 1998-11
- Volume
- 80
- Issue
- 4
- Pages
- 549-560
- ISSN
- 0034-6535 (ISSN-L)
- 1530-9142
- OpenAlex
- W2022220920
- Cited by
- 6,865
- References
- 37
- Citations by year
- 2026: 1321 · 2025: 891 · 2024: 708 · 2023: 693 · 2022: 562 · 2021: 452 · 2020: 346 · 2019: 307 · 2018: 229 · 2017: 238 · 2016: 187 · 2015: 184 · 2014: 195 · 2013: 139 · 2012: 112
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Review of Economics and Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Keywords
- spatial dependence, panel data, nonparametric kernel estimators, standard errors, time dependence
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