Automated build.

.setup/latex/bib/bib.bib

@@ -1645,6 +1645,61 @@ @Article{Efron-1979b
   publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
 }
 
+@Article{Hinkley-1977,
+  author = {David V. Hinkley},
+  date = {1977-08},
+  journaltitle = {Technometrics},
+  title = {Jackknifing in unbalanced situations},
+  doi = {10.1080/00401706.1977.10489550},
+  number = {3},
+  pages = {285--292},
+  volume = {19},
+  abstract = {Both the standard jackknife and a weighted jackknife are investigated in the general linear model situation. Properties of bias reduction and standard error estimation are derived and the weighted jackknife shown to be superior for unbalanced data. There is a preliminary discussion of robust regression fitting using jackknife pseudo-values.},
+  publisher = {Informa {UK} Limited},
+  keywords = {jackknife, linear model, regression, residual, robustness,},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Horn-Horn-Duncan-1975,
+  author = {Susan D. Horn and Roger A. Horn and David B. Duncan},
+  date = {1975-06},
+  journaltitle = {Journal of the American Statistical Association},
+  title = {Estimating heteroscedastic variances in linear models},
+  doi = {10.1080/01621459.1975.10479877},
+  number = {350},
+  pages = {380--385},
+  volume = {70},
+  publisher = {Informa {UK} Limited},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Andrews-1991,
+  author = {Donald W. K. Andrews},
+  date = {1991-05},
+  journaltitle = {Econometrica},
+  title = {Heteroskedasticity and autocorrelation consistent covariance matrix estimation},
+  doi = {10.2307/2938229},
+  number = {3},
+  pages = {817},
+  volume = {59},
+  abstract = {This paper is concerned with the estimation of covariance matrices in the presence of heteroskedasticity and autocorrelation of unknown forms. Currently available estimators that are designed for this context depend upon the choice of a lag truncation parameter and a weighting scheme. Results in the literature provide a condition on the growth rate of the lag truncation parameter as $T \to \infty$ that is sufficient for consistency. No results are available, however, regarding the choice of lag truncation parameter for a fixed sample size, regarding data-dependent automatic lag truncation parameters, or regarding the choice of weighting scheme. In consequence, available estimators are not entirely operational and the relative merits of the estimators are unknown. This paper addresses these problems. The asymptotic truncated mean squared errors of estimators in a given class are determined and compared. Asymptotically optimal kernel/weighting scheme and bandwidth/lag truncation parameters are obtained using an asymptotic truncated mean squared error criterion. Using these results, data-dependent automatic bandwidth/lag truncation parameters are introduced. The finite sample properties of the estimators are analyzed via Monte Carlo simulation.},
+  publisher = {{JSTOR}},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Andrews-Monahan-1992,
+  author = {Donald W. K. Andrews and J. Christopher Monahan},
+  date = {1992-07},
+  journaltitle = {Econometrica},
+  title = {An improved heteroskedasticity and autocorrelation consistent covariance matrix estimator},
+  doi = {10.2307/2951574},
+  number = {4},
+  pages = {953},
+  volume = {60},
+  publisher = {{JSTOR}},
+  annotation = {regression, regression-hc},
+}
+
 @Article{Barnard-Collins-Farewell-etal-1981,
   author = {George A. Barnard and J. R. Collins and V. T. Farewell and C. A. Field and J. D. Kalbfleisch and Stanley W. Nash and Emanuel Parzen and Ross L. Prentice and Nancy Reid and D. A. Sprott and Paul Switzer and W. G. Warren and K. L. Weldon},
   date = {1981},
@@ -1657,6 +1712,19 @@ @Article{Barnard-Collins-Farewell-etal-1981
   publisher = {Wiley},
 }
 
+@Article{Chesher-Jewitt-1987,
+  author = {Andrew Chesher and Ian Jewitt},
+  date = {1987-09},
+  journaltitle = {Econometrica},
+  title = {The bias of a heteroskedasticity consistent covariance matrix estimator},
+  doi = {10.2307/1911269},
+  number = {5},
+  pages = {1217},
+  volume = {55},
+  publisher = {{JSTOR}},
+  annotation = {regression, regression-hc},
+}
+
 @Article{Efron-1981a,
   author = {Bradley Efron},
   date = {1981},
@@ -1683,6 +1751,32 @@ @Article{Efron-1981b
   publisher = {Wiley},
 }
 
+@Article{MacKinnon-White-1985,
+  author = {James G. MacKinnon and Halbert White},
+  date = {1985-09},
+  journaltitle = {Journal of Econometrics},
+  title = {Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties},
+  doi = {10.1016/0304-4076(85)90158-7},
+  number = {3},
+  pages = {305--325},
+  volume = {29},
+  abstract = {We examine several modified versions of the heteroskedasticity-consistent covariance matrix estimator of Hinkley (1977) and White (1980). On the basis of sampling experiments which compare the performance of quasi t-statistics, we find that one estimator, based on the jackknife, performs better in small samples than the rest. We also examine the finite-sample properties of using modified critical values based on Edgeworth approximations, as proposed by Rothenberg (1984). In addition, we compare the power of several tests for heteroskedasticity, and find that it may be wise to employ the jackknife heteroskedasticity-consistent covariance matrix even in the absence of detected heteroskedasticity.},
+  publisher = {Elsevier {BV}},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Newey-West-1987,
+  author = {Whitney K. Newey and Kenneth D. West},
+  date = {1987-05},
+  journaltitle = {Econometrica},
+  title = {A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix},
+  doi = {10.2307/1913610},
+  number = {3},
+  pages = {703},
+  volume = {55},
+  publisher = {{JSTOR}},
+}
+
 @Article{Rasmussen-1987,
   author = {Jeffrey L. Rasmussen},
   date = {1987},
@@ -1721,6 +1815,19 @@ @Article{Oud-vandenBercken-Essers-1990
   publisher = {{SAGE} Publications},
 }
 
+@Book{Davidson-MacKinnon-1993,
+  author = {Russell Davidson and James G. MacKinnon},
+  publisher = {Oxford University Press},
+  title = {Estimation and inference in econometrics},
+  date = {1993},
+  location = {New York, NY},
+  isbn = {9780195060119},
+  library = {HB139 .D368 1993},
+  keywords = {Econometrics},
+  addendum = {https://lccn.loc.gov/92012048},
+  annotation = {regression, regression-hc},
+}
+
 @Article{Andrews-2000,
   author = {Donald W. K. Andrews},
   date = {2000-03},
@@ -1927,6 +2034,113 @@ @Article{Holmes-2003b
   publisher = {Institute of Mathematical Statistics},
 }
 
+@Article{CribariNeto-2004,
+  author = {Francisco Cribari-Neto},
+  date = {2004-03},
+  journaltitle = {Computational Statistics {\&} Data Analysis},
+  title = {Asymptotic inference under heteroskedasticity of unknown form},
+  doi = {10.1016/s0167-9473(02)00366-3},
+  number = {2},
+  pages = {215--233},
+  volume = {45},
+  abstract = {We focus on the finite-sample behavior of heteroskedasticity-consistent covariance matrix estimators and associated quasi-$t$ tests. The estimator most commonly used is that proposed by Halbert White. Its finite-sample behavior under both homoskedasticity and heteroskedasticity is analyzed using Monte Carlo methods. We also consider two other consistent estimators, namely: the HC3 estimator, which is an approximation to the jackknife estimator, and the weighted bootstrap estimator. Additionally, we evaluate the finite-sample behavior of two bootstrap quasi-$t$ tests: the test based on a single bootstrapping scheme and the test based on a double, nested bootstrapping scheme. The latter is very computer-intensive, but proves to work well in small samples. Finally, we propose a new estimator, which we call HC4; it is tailored to take into account the effect of leverage points in the design matrix on associated quasi-$t$ tests.},
+  publisher = {Elsevier {BV}},
+  annotation = {regression, regression-hc},
+}
+
+@Article{CribariNeto-daSilva-2010,
+  author = {Francisco Cribari-Neto and Wilton Bernardino {da Silva}},
+  date = {2010-11},
+  journaltitle = {{AStA} Advances in Statistical Analysis},
+  title = {A new heteroskedasticity-consistent covariance matrix estimator for the linear regression model},
+  doi = {10.1007/s10182-010-0141-2},
+  number = {2},
+  pages = {129--146},
+  volume = {95},
+  abstract = {The assumption that all random errors in the linear regression model share the same variance (homoskedasticity) is often violated in practice. The ordinary least squares estimator of the vector of regression parameters remains unbiased, consistent and asymptotically normal under unequal error variances. Many practitioners then choose to base their inferences on such an estimator. The usual practice is to couple it with an asymptotically valid estimation of its covariance matrix, and then carry out hypothesis tests that are valid under heteroskedasticity of unknown form. We use numerical integration methods to compute the exact null distributions of some quasi-t test statistics, and propose a new covariance matrix estimator. The numerical results favor testing inference based on the estimator we propose.},
+  publisher = {Springer Science and Business Media {LLC}},
+  annotation = {regression, regression-hc},
+}
+
+@Article{CribariNeto-Souza-Vasconcellos-2008,
+  author = {Francisco Cribari-Neto and Tatiene C. Souza and Klaus L. P. Vasconcellos},
+  date = {2008-09},
+  journaltitle = {Communications in Statistics - Theory and Methods},
+  title = {Errata: Inference under heteroskedasticity and leveraged data, {Communications in Statistics, Theory and Methods}, 36, 1877--1888, 2007},
+  doi = {10.1080/03610920802109210},
+  number = {20},
+  pages = {3329--3330},
+  volume = {37},
+  publisher = {Informa {UK} Limited},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Hayes-Cai-2007,
+  author = {Andrew F. Hayes and Li Cai},
+  date = {2007-11},
+  journaltitle = {Behavior Research Methods},
+  title = {Using heteroskedasticity-consistent standard error estimators in {OLS} regression: An introduction and software implementation},
+  doi = {10.3758/bf03192961},
+  number = {4},
+  pages = {709--722},
+  volume = {39},
+  publisher = {Springer Science and Business Media {LLC}},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Kauermann-Carroll-2001,
+  author = {G{\"o}ran Kauermann and Raymond J. Carroll},
+  date = {2001-12},
+  journaltitle = {Journal of the American Statistical Association},
+  title = {A note on the efficiency of sandwich covariance matrix estimation},
+  doi = {10.1198/016214501753382309},
+  number = {456},
+  pages = {1387--1396},
+  volume = {96},
+  abstract = {The sandwich estimator, also known as robust covariance matrix estimator, heteroscedasticity-consistent covariance matrix estimate, or empirical covariance matrix estimator, has achieved increasing use in the econometric literature as well as with the growing popularity of generalized estimating equations. Its virtue is that it provides consistent estimates of the covariance matrix for parameter estimates even when the fitted parametric model fails to hold or is not even specified. Surprisingly though, there has been little discussion of properties of the sandwich method other than consistency. We investigate the sandwich estimator in quasi-likelihood models asymptotically, and in the linear case analytically. We show that under certain circumstances when the quasi-likelihood model is correct, the sandwich estimate is often far more variable than the usual parametric variance estimate. The increased variance is a fixed feature of the method and the price that one pays to obtain consistency even when the parametric model fails or when there is heteroscedasticity. We show that the additional variability directly affects the coverage probability of confidence intervals constructed from sandwich variance estimates. In fact, the use of sandwich variance estimates combined with $t$-distribution quantiles gives confidence intervals with coverage probability falling below the nominal value. We propose an adjustment to compensate for this fact.},
+  publisher = {Informa {UK} Limited},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Long-Ervin-2000,
+  author = {J. Scott Long and Laurie H. Ervin},
+  date = {2000-08},
+  journaltitle = {The American Statistician},
+  title = {Using heteroscedasticity consistent standard errors in the linear regression model},
+  doi = {10.1080/00031305.2000.10474549},
+  number = {3},
+  pages = {217--224},
+  volume = {54},
+  publisher = {Informa {UK} Limited},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Zeileis-2004,
+  author = {Achim Zeileis},
+  date = {2004},
+  journaltitle = {Journal of Statistical Software},
+  title = {Econometric computing with {HC} and {HAC} covariance matrix estimators},
+  doi = {10.18637/jss.v011.i10},
+  number = {10},
+  volume = {11},
+  abstract = {Data described by econometric models typically contains autocorrelation and/or heteroskedasticity of unknown form and for inference in such models it is essential to use covariance matrix estimators that can consistently estimate the covariance of the model parameters. Hence, suitable heteroskedasticity consistent (HC) and heteroskedasticity and autocorrelation consistent (HAC) estimators have been receiving attention in the econometric literature over the last 20 years. To apply these estimators in practice, an implementation is needed that preferably translates the conceptual properties of the underlying theoretical frameworks into computational tools. In this paper, such an implementation in the package sandwich in the R system for statistical computing is described and it is shown how the suggested functions provide reusable components that build on readily existing functionality and how they can be integrated easily into new inferential procedures or applications. The toolbox contained in sandwich is extremely flexible and comprehensive, including specific functions for the most important HC and HAC estimators from the econometric literature. Several real-world data sets are used to illustrate how the functionality can be integrated into applications.},
+  publisher = {Foundation for Open Access Statistic},
+  annotation = {regression, regression-hc},
+}
+
+@Article{Zeileis-2006,
+  author = {Achim Zeileis},
+  journal = {Journal of Statistical Software},
+  title = {Object-oriented computation of sandwich estimators},
+  year = {2006},
+  number = {9},
+  volume = {16},
+  doi = {10.18637/jss.v016.i09},
+  abstract = {Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed.},
+  publisher = {Foundation for Open Access Statistic},
+  annotation = {regression, regression-hc},
+}
+
 @Article{Asparouhov-Hamaker-Muthen-2017,
   author = {Tihomir Asparouhov and Ellen L. Hamaker and Bengt Muth{\a'e}n},
   date = {2017-12},

DESCRIPTION

@@ -35,4 +35,3 @@ Suggests:
     MASS,
     mice,
     Amelia
-RoxygenNote: 7.2.3