Arbeitspapier
Inference with large clustered datasets
Inference using large datasets is not nearly as straightforward as conventional econometric theory suggests when the disturbances are clustered, even with very small intra-cluster correlations. The information contained in such a dataset grows much more slowly with the sample size than it would if the observations were independent. Moreover, inferences become increasingly unreliable as the dataset gets larger. These assertions are based on an extensive series of estimations undertaken using a large dataset taken from the U.S. Current Population Survey.
- Language
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Englisch
- Bibliographic citation
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Series: Queen's Economics Department Working Paper ; No. 1365
- Classification
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Wirtschaft
Hypothesis Testing: General
Statistical Simulation Methods: General
Methodological Issues: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
- Subject
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cluster-robust inference
earnings equation
wild cluster bootstrap
CPS data
sample size
placebo laws
- Event
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Geistige Schöpfung
- (who)
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MacKinnon, James G.
- Event
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Veröffentlichung
- (who)
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Queen's University, Department of Economics
- (where)
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Kingston (Ontario)
- (when)
-
2016
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- MacKinnon, James G.
- Queen's University, Department of Economics
Time of origin
- 2016
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