t-Statistic Based Correlation and Heterogeneity Robust Inference

By Rustam Ibragimov, Ulrich K. Müller. 2010

326 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8c2c1be3b6530d3d94bd4b3283d9a4f0de6f0e13

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In Statistics and Probability

Citations in the corpus

Inference with dependent data using cluster covariance estimators (2011, methodology): “It is important to note the relationship between our fixed-G approach and the Fama-Macbeth type estimator of Ibragimov and Müller (2006) (IM) mentioned above.”
The Wild Bootstrap with a “Small” Number of “Large” Clusters (2019, methodology): “findings are consistent with our theoretical results in Section 3 and simulations in Ibragimov and Müller (2016), who find that the wild bootstrap may have rejection probability under the null hypothesis greater than the nominal level whenever the dimension of the regressors”
Cluster-robust inference: A guide to empirical practice (2022, background): “Another, quite different, approach to inference when G is fixed is developed in Ibragimov and Müller (2010). The parameter of interest is a scalar, say β, which can be thought of as one element of β.”
Kernel-Based Semiparametric Estimators: Small Bandwidth Asymptotics and Bootstrap Consistency (2018, methodology): “First, the inference procedure of Ibragimov and Müller (2010) can be adapted to the current setup to produce a confidence interval whose asymptotic validity follows from (1) even if Σ0 does not admit a consistent estimator. Second, in the more general case where √ n(θ̂n − θ0) has a (non-degenerate) ”
Randomization Tests Under an Approximate Symmetry Assumption (2017, methodology): “With the exception of the t-test approach developed in Ibragimov and Müller (2010), the competing approaches we discuss in Section 5.1 below do not share this feature.”
Synthetic Control Method: Inference, Sensitivity Analysis and Confidence Sets (2018, methodology): “…of the t-test have been previously observed in contexts that are different from ours: Lehmann (1959) looks to a simple test of mean differences, Ibragimov & Muller (2010) analyzes a two-sample test of mean differences where samples’ variances are different from each other, and Young (2015)…”
Asymptotic theory for clustered samples (2019, unknown): “This verifies (61) and hence (20) holds.”
Placebo inference on treatment effects when the number of clusters is small (2018, methodology): “In this empirical application, I follow Ibragimov and Müller (2016) and reanalyze a lab experiment of Dal Bó and Fréchette (2011), who measure cooperation between players in an infinitely repeated prisoner’s dilemma with a continuation probability of either δ = .”
Heterogeneous change point inference (2015, background): “This may be explained by the fact that the local likelihood tests of H-SMUCE are quite robust against heterogeneous noise (see for instance Bakirov and Szekely (2006) and Ibragimov and Müller (2010)) and against non-normal errors (see Lehmann and Romano (2005) and the references therein).”
Heteroskedasticity, autocorrelation, and spatial correlation robust inference in linear panel models with fixed-effects (2012, methodology): “using straightforward arguments from Kiefer and Vogelsang (2005) to cancel the nuisance parameters RQ 1 0 0.”
HAC Corrections for Strongly Autocorrelated Time Series (2014, methodology): “Especially if second moment instabilities are a major concern, another attractive approach is to follow the suggestion of Ibragimov and Müller (2010).”
FIXED-b ASYMPTOTICS FOR SPATIALLY DEPENDENT ROBUST NONPARAMETRIC COVARIANCE MATRIX ESTIMATORS (2014, background): “…j2)∈Bn K ( i1 h1 , i2 h1 , j1 h2 , j2 h2 ) ŝi1, j1 ŝ ′ i2, j2 , (2.2) where K (x1, x2, x3, x4) ≤ 1 is a kernel weighting function that satisfies K (x, x, y, y) = 1 and h1 and h2 are tuning parameters where larger (smaller) values of h1 and h2 indicate less (more) smoothing for a given kernel.”