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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”
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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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