Computing Robust Standard Errors for Within-Groups Estimators

By M. Arellano. 2009

1,387 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3fe3ed2e19fa68f6bd174ef48ab36d2cd21e22ca

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In Statistics and Probability

Citations in the corpus

Inference with dependent data using cluster covariance estimators (2011, neutral): “The CCE is commonly employed along with an assumption of independence across groups; see, e.g., Liang and Zeger (1986), Arellano (1987), Wooldridge (2003), Bertrand, Duflo, and Mullainathan (2004), and Hansen (2007).”
Homogeneity pursuit in panel data models: Theory and application (2018, neutral): “Table 2 reports the estimation results for each group-specific parameter and those for the pooled estimates, all of which are bias-corrected by using Lu and Su's (2017) bias correction formula and Arellano's (1987) country cluster-robust standard errors.”
Effect of Service Transition Strategies on Firm Value (2008, positive): “We employ the White period robust coefficient variance estimator for statistical inferences (Arellano 1987), which is designed to accommodate arbitrary serial correlation in the disturbances.”
Generalized least squares inference in panel and multilevel models with serial correlation and fixed effects (2007, neutral): “This distortion seems likely to be due to the small sample bias of the robust standard errors discussed in Bell and McCaffrey (2002) and illustrated in Table 3.”
Spillovers from Multinationals to Heterogeneous Domestic Firms: Evidence from Hungary (2009, positive): “Since we have short timeseries and a large cross-section, it is appropriate to use cluster-sample methods (Wooldridge, 2003; Arellano, 1987) to estimate the fixed-effects model.”
Asymptotic properties of a robust variance matrix estimator for panel data when T is large (2007, neutral): “One drawback of the estimator of Arellano (1987), hereafter referred to as the ''clustered'' covariance matrix (CCM) estimator, is that its properties are only known in conventional panel asymptotics as the cross-section dimension, n, increases with the time dimension, T, fixed.”
The Diversity of High-Frequency Traders (2013, neutral): “In addition, the standard errors are computed with a White‐type technique, where the coefficient covariance estimator is robust to cross‐section (contemporaneous) correlation as well as arbitrary, unknown forms of different error variances in each cross‐section (see Arellano, 1987, and White, 1980).”
Information Asymmetry and the Value of Cash (2007, neutral): “• Panel D: Rather than using fixed effects, we estimate the model using ordinary least squares with cluster robust standard errors (Arellano, 1987; Rogers, 1993).”
Asymptotic theory and wild bootstrap inference with clustered errors (2019, neutral): “WhenNg = 1 for all g, so thatG = N, the estimator V̂ reduces to the familiar heteroskedasticityconsistent covariance matrix estimator (HCCME) of Eicker (1963) and White (1980); see also Arellano (1987). Several variations of the CRVE have been proposed to reduce its finite-sample bias, in the same w”