A Note on Robust Variance Estimation for Cluster‐Correlated Data

By Rick L. Williams. Biometrics, 2000

2,622 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f82f57c819797241f20848f2473ed1bcb8cae03c

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Citations in the corpus

How do bankruptcy laws affect entrepreneurship development around the world (2011, methodology): “Furthermore, we also use the cluster command in STATA to obtain a robust variance estimate that accounts for (and adjusts for) within-country correlation andwithingroup dependence (Barkema and Shvyrkov, 2007; Williams, 2000).”
Employment Regulations Through the Eyes of Employers: Do They Matter and How Do Firms Respond to Them? (2004, neutral): “The cluster adjustment made to the variance-covariance matrix are standard and described in Rogers (1993) and Williams (2000).”
Religiosity and Earnings Management: International Evidence from the Banking Industry (2014, neutral): “We therefore use OLS/ logistic regressions with clustered robust errors to account for both serial and cross-sectional correlations (Rogers 1993; Williams 2000; Petersen 2009).”
Trade Generation, Reputation and Sell-Side Analysts (2005, methodology): “T-statistics are calculated firstly using White (1980) robust standard errors to control for heteroscedasticity, and secondly using the Williams (2000) robust variance estimate to adjust for within-cluster correlation in residuals induced by the same analyst covering several stocks in a given year.”
Economic Globalization and Transnational Terrorism (2004, background): “These estimated standard errors are robust to both heteroskedasticity and to a general type of serial correlation within any cross-sectional unit (Rogers 1993; Williams 2000).”
Does Sentiment Drive the Retail Demand for IPOs? (2009, neutral): “All columns report heteroskedasticity-robust standard errors that allow same-week observations to be arbitrarily correlated (Williams (2000)).”
Knowledge Flows Within Multinational Corporations: Explaining Subsidiary Isolation and Its Performance Implications (2008, methodology): “…from the same host country may not be independent, we also used a robust clustering procedure as implemented in Stata 8.0 for all our models (Williams 2000, Wooldridge 2002).9 To eliminate any spurious effects from unobserved differences among firms, we included fixed-firm effects by…”
From Opportunity Insight to Opportunity Intention: The Importance of Person–Situation Learning Match (2007, methodology): “…response per participant, in order to account for the nonindependence of residuals, the estimation of the effects on action likelihood included a robust variance estimator adjusted for within-cluster correlation in the standard errors of the coefficients in the model (Rogers, 1993; Williams, 2000).”
Racing to the Bottom? Trade, Environmental Governance, and ISO 14001 (2006, methodology): “10 Because observations within countries are not independent, though we assume they are independent across countries, we use robust standard errors adjusted for clustering within countries (Williams 2000).”