Asymptotic properties of a robust variance matrix estimator for panel data when T is large

By Christian Hansen. 2007

351 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/041134fd988273d3bf73b7b97f33edbd57017e80

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Citations in the corpus

Fast and wild: Bootstrap inference in Stata using boottest (2018, methodology): “These assumptions were weakened in Hansen (2007) to allow the Ωg to vary across clusters and further weakened in Carter, Schnepel, and Steigerwald (2017), which, however, effectively assumed that the error terms are very nearly normally distributed.”
Asymptotic theory and wild bootstrap inference with clustered errors (2019, methodology): “In particular, we use critical values taken from the t(G− 1) distribution, which in this case is t(24), instead of standard normal ones, as advocated by Bester, Conley, and Hansen (2011), and the CRVE is the one in (5) with the factor d = G(N − 1)/ ( (G − 1)(N − k) ) . Without this factor, or if we ”
Inference in Regression Discontinuity Designs with a Discrete Running Variable (2016, background): “To see this, observe that 11In general, clustered standard errors converge to a non-degenerate limiting distribution unless the number of clusters increases with the sample size at a suitable rate (see for example, Hansen, 2007).”
Feasible generalized least squares for panel data with cross-sectional and serial correlations (2019, methodology): “Hansen (2007a) studied an asymptotic theory that are robust to the heteroskedasticity and serial correlation in the case of large-N large-T .”
Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects (2020, background): “In line with the discussion in Hansen (2007), it can be shown that when {v′ it, εit} follows a certain strong mixing process over t and is independent over i, Ψ̂ − Ψ p −→ 0 as N, T → ∞, N/T → C.”
Alternative diff-in-diffs estimators with several pretreatment periods (2019, methodology): “…cross-sectional correlation within groups as well as correlations over time of the units in the di erent groups (in panel data applications) has been shown to substantially a ect the validity of traditional inference (Moulton, 1990; Bertrand et al., 2004; Donald and Lang, 2007; Hansen, 2007a,b).”
Robust Inference With Multiway Clustering (2006, neutral): “Christian Hansen (2007a) shows that even for long panels it is okay to use CRVE with FE estimator (as shown in simulations by Krezde (2002)). Christian Hansen (2007b) shows that for short panels with AR(p) errors bias-corrected FGLS does well. Hausman and Kuersteiner (2005) do qualitatively similar ”
Cluster-robust inference: A guide to empirical practice (2022, background): “The first proof for this case of which we are aware is in White (1984, Chapter 6); see also Hansen (2007).”
Robust Inference with Clustered Data (2010, background): “Hansen (2007a) derived asymptotic results for the standard one-way cluster-robust variance matrix estimator for panel data under various assumptions.”
THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS (2011, methodology): “Theorem 2.3 is the analogue of the Hansen (2007) result when we assume that the serial dependence is mixing and the cross-sectional dependence can either be strong or weak.”