Limit Theorems for Regressions with Unequal and Dependent Errors

By F. Eicker. 1967

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Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, positive): “Gelman and Imbens [2014] present a detailed discussion on the concerns with global polynomial methods. These local linear methods require a bandwidth choice. We recommend the optimal bandwidth algorithms based on asymptotic arguments involving local expansions discssed in Imbens and Kalyanaraman [20”
When Should You Adjust Standard Errors for Clustering? (2017, unknown): “Often researchers allow for general heteroskedasticity and use the robust Eicker-Huber-White (EHW) variance (White [2014], Eicker [1967], Huber [1967]) VEHW(β̂) = (”
Robust variance estimation in meta‐regression with dependent effect size estimates (2010, result): “The main results of this paper are a generalization of the results of [26, 27], and [28].”
Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence (2007, background): “Probably the most popular of these alternative covariance matrix estimators has been developed by Huber (1967), Eicker (1967), and White (1980).”
Thirty Years of Heteroskedasticity-Robust Inference (2013, background): “For many decades, despite a few precursors in the statistics literature such as Eicker (1963, 1967) and Hinkley (1977), econometricians believed that it is necessary to estimate each expectation separately in order to estimate an average of expectations consistently.”
LARGE SAMPLE PROPERTIES OF GENERALIZED METHOD OF (1982, unknown): “results of Eicker [8] to the class of time series estimators considered here would be of interest.”
Testing Negative Error Variances (2012, methodology): “In the linear regression context where the sandwich-type estimators are best studied, the heteroskedasticity-robust variance estimator of Eicker (1967) and White (1980) will be inconsistent under serial or cluster correlations of observations.”
The Effects of Firm-Specific Factors on the Profitability of Non-Life Insurance Companies in Turkey (2015, methodology): “Because heteroskedasticity problem was encountered in both models, to obtain heteroskedasticityconsistent standard errors, the fixed effects models were estimated using heteroskedasticity-consistent covariance matrix estimator developed by Huber [42], Eicker [43], and White [44].”
Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model (2000, unknown): “The development of the HCCM can be traced to the early work of Eicker (1963, 1967) and Huber (1967).”
Robust Standard Errors in Small Samples: Some Practical Advice (2016, unknown): “The most widely used form of the robust, heteroskedasticity-consistent standard errors is that associated with the work of White (1980) (see also Eicker, 1967; Huber, 1967), extended to the case with clustering by Liang and Zeger (1986).”
High-dimensional simultaneous inference with the bootstrap (2016, background): “The phenomenon is closely related to the robust sandwich estimator for the standard error of the MLE in low-dimensional models (Eicker 1967; Huber 1967; White 1980; Freedman 1981).”
Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation (2007, methodology): “Building upon the earlier work of Eicker (1963, 1967) and Huber (1967), White (1980) argued to place the ith squared error into the ith row of the diagonal of the Φ matrix, using the OLS residuals as estimators of the errors.”