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See `FixedEffectModels` for an example.\n\n\nEach type defined in this package defines the following methods: \n```julia\n# return a vector indicating non-missing observations for standard errors\ncompletecases(table, ::CovarianceEstimator) = trues(size(df, 1))\n# materialize a CovarianceEstimator by using the data needed to compute the standard errors\nmaterialize(table, v::CovarianceEstimator) = v\n# return variance-covariance matrix\nvcov(x::RegressionModel, ::CovarianceEstimator) = error(\"vcov not defined for this type\")\n# returns the degree of freedom for the t-statistics and F-statistic\ndof_tstat(x::RegressionModel, ::CovarianceEstimator, hasintercept::Bool) = dof_residual(x) - hasintercept\n```\n\nFor now, it includes `Vcov.simple()`, `Vcov.robust()`, and `Vcov.cluster(...)`.\n\n## References\nKleibergen, F, and Paap, R. (2006) *Generalized reduced rank tests using the singular value decomposition.* Journal of econometrics\n\nKleibergen, F. and Schaffer, M.  (2007) *RANKTEST: Stata module to test the rank of a matrix using the Kleibergen-Paap rk statistic*. Statistical Software Components, Boston College Department of Economics.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffixedeffects%2Fvcov.jl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffixedeffects%2Fvcov.jl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffixedeffects%2Fvcov.jl/lists"}