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100 1 a| Cameron, A. Colin.?| UNAUTHORIZED;

245 10 a| Robust inference with multi-way clusteringh| [electronic resource] /c| A. Colin Cameron, Jonah B. Gelbach, Douglas L. Miller.;

260 a| Cambridge, MA :b| National Bureau of Economic Research,;

490 1 a| NBER working paper series ;v| working paper . 327;

500 a| Title from PDF file as viewed on 10/12/2006.;

504 a| Includes bibliographical references.;

506 a| Available only to authorized users.;

520 3 a| "In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present"--National Bureau of Economic Research web site.;

530 a| Also available in print.;

538 a| Mode of access: World Wide Web;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Cameron, A.?| UNAUTHORIZED;

700 1 a| Gelbach, Jonah B.?| UNAUTHORIZED;

700 1 a| Miller, Douglas L.?| UNAUTHORIZED;

710 2 a| National Bureau of Economic Research.=| ^A186;

830 0 a| Working paper series (National Bureau of Economic Research : Online) ;v| working paper no. . 327.?| UNAUTHORIZED;

856 40 z| Full text available from NBER Working Papersu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fdx.doi.org%2F10.3386%2Ft0327;

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Robust inference with multi-way clustering / A. Colin Cameron, Jonah B. Gelbach, Douglas L. Miller.

Author/creator	Cameron, A. Colin
Format	Electronic
Publication Info	Cambridge, MA : National Bureau of Economic Research,
Supplemental Content	Full text available from NBER Working Papers

Other author/creator	Cameron, A.
Other author/creator	Gelbach, Jonah B.
Other author/creator	Miller, Douglas L.
Other author/creator	National Bureau of Economic Research.
Series	NBER working paper series ; working paper . 327 Working paper series (National Bureau of Economic Research : Online) ; working paper no. . 327. UNAUTHORIZED
Summary	"In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present"--National Bureau of Economic Research web site.
General note	Title from PDF file as viewed on 10/12/2006.
Bibliography note	Includes bibliographical references.
Access restriction	Available only to authorized users.
Other forms	Also available in print.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2006619714

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Robust inference with multi-way clustering / A. Colin Cameron, Jonah B. Gelbach, Douglas L. Miller.

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