44 citations · 116 across the 14 of their papers we have counts for
16 papers
Valid Heteroskedasticity Robust Testing
Benedikt M. Pötscher, David Preinerstorfer
Tests based on heteroskedasticity robust standard errors are an important technique in econometric practice. Choosing the right critical value, however, is not simple at all: conve…
How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?
Benedikt M. Pötscher, David Preinerstorfer
We develop theoretical finite-sample results concerning the size of wild bootstrap-based heteroskedasticity robust tests in linear regression models. In particular, these results p…
Discussion on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin
Hannes Leeb, Benedikt M. Pötscher, Danijel Kivaranovic
This is a comment on the article mentioned in the title.
Further Results on Size and Power of Heteroskedasticity and Autocorrelation Robust Tests, with an Application to Trend Testing
Benedikt M. Pötscher, David Preinerstorfer
We complement the theory developed in Preinerstorfer and Pötscher (2016) with further finite sample results on size and power of heteroskedasticity and autocorrelation robust tests…
Controlling the Size of Autocorrelation Robust Tests
Benedikt M. Pötscher, David Preinerstorfer
Autocorrelation robust tests are notorious for suffering from size distortions and power problems. We investigate under which conditions the size of autocorrelation robust tests ca…
Valid confidence intervals for post-model-selection predictors
François Bachoc, Hannes Leeb, Benedikt M. Pötscher
We consider inference post-model-selection in linear regression. In this setting, Berk et al.(2013) recently introduced a class of confidence sets, the so-called PoSI intervals, th…
On the Power of Invariant Tests for Hypotheses on a Covariance Matrix
David Preinerstorfer, Benedikt M. Pötscher
The behavior of the power function of autocorrelation tests such as the Durbin-Watson test in time series regressions or the Cliff-Ord test in spatial regression models has been in…
On Various Confidence Intervals Post-Model-Selection
Hannes Leeb, Benedikt M. Pötscher, Karl Ewald
We compare several confidence intervals after model selection in the setting recently studied by Berk et al. [Ann. Statist. 41 (2013) 802-837], where the goal is to cover not the t…
On Size and Power of Heteroskedasticity and Autocorrelation Robust Tests
David Preinerstorfer, Benedikt M. Pötscher
Testing restrictions on regression coefficients in linear models often requires correcting the conventional F-test for potential heteroskedasticity or autocorrelation amongst the d…
Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values
Hannes Leeb, Benedikt M. Pötscher
We point out that the ideas underlying some test procedures recently proposed for testing post-model-selection (and for some other test problems) in the econometrics literature hav…
Distributional Results for Thresholding Estimators in High-Dimensional Gaussian Regression Models
Benedikt M. Pötscher, Ulrike Schneider
We study the distribution of hard-, soft-, and adaptive soft-thresholding estimators within a linear regression model where the number of parameters k can depend on sample size n a…
Non-Parametric Maximum Likelihood Density Estimation and Simulation-Based Minimum Distance Estimators
Florian Gach, Benedikt M. Pötscher
Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary non-parametric maximum likelihood density estim…
Efficient Simulation-Based Minimum Distance Estimation and Indirect Inference
Richard Nickl, Benedikt M. Pötscher
Given a random sample from a parametric model, we show how indirect inference estimators based on appropriate nonparametric density estimators (i.e., simulation-based minimum dista…
Confidence Sets Based on Penalized Maximum Likelihood Estimators in Gaussian Regression
Benedikt M. Pötscher, Ulrike Schneider
Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-…
On the Distribution of Penalized Maximum Likelihood Estimators: The LASSO, SCAD, and Thresholding
Benedikt M. Potscher, Hannes Leeb
We study the distributions of the LASSO, SCAD, and thresholding estimators, in finite samples and in the large-sample limit. The asymptotic distributions are derived for both the c…
Learning Trigonometric Polynomials from Random Samples and Exponential Inequalities for Eigenvalues of Random Matrices
Karlheinz Groechenig, Benedikt M. Poetscher, Holger Rauhut
Motivated by problems arising in random sampling of trigonometric polynomials, we derive exponential inequalities for the operator norm of the difference between the sample second…