Robust Inference with Clustered Data
By A. Cameron, Douglas L. Miller. 2010
274 citations recorded by the source corpus.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/985f0b3d38591437d3916011d1bae9f3bbefc431
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- James G. MacKinnon
- Bernard M. Hoekman
- Ada Ferrer-i-Carbonell
- Colin Cameron
- Martin Karlsson
- Douglas L. Miller
- Xavier Ramos
- Nicolas R. Ziebarth
Citations in the corpus
- Thirty Years of Heteroskedasticity-Robust Inference (2013, background): “See Cameron and Miller (2011) for a recent survey.”
- Global Supply Chains and Trade Policy Responses to the 2008 Crisis (2015, background): “The recent literature suggests error cluster correction at the highest level of the aggregation (Cameron and Miller 2011).”
- Electoral Cycles in Savings Bank Lending (2017, unknown): “Alternative specification Rob7 uses the wild cluster bootstrap-t procedure as suggested by Cameron et al. (2008) without specifying a null hypothesis for regressors.”
- Asymptotic theory for clustered samples (2019, unknown): “Surveys can be found in Wooldridge (2003), Cameron and Miller (2011, 2015), MacKinnon (2012, 2016), and textbook treatments in Angrist and Pischke (2009) and Wooldridge (2010).”
- Population health effects and health-related costs of extreme temperatures: Comprehensive evidence from Germany (2018, background): “Column (1) in Panel A reports results with standard errors clustered at the state instead of the county level; column (2) applies two-way clustering by county and date (Cameron and Miller, 2011), and column (3) corrects for spatial dependence (Driscoll and Kraay, 1998).”
- Inequality and Happiness (2012, background): “However, most of the empirical analyses have too few clusters (regions) and clustering may thus lead to biased estimates (Wooldrige, 2006; Angrist and Pischke, 2009; Cameron and Miller, 2010).”
- A Practitioner’s Guide to Cluster-Robust Inference (2015, background): “of Cameron et al. (2008). In summary, the various wild cluster bootstraps lead to test size that is closer to 0.”