142 citations · 198 across the 8 of their papers we have counts for
13 papers
High-Dimensional Panel Data Models with Interactive Fixed Effects: Beyond the Linear Case
Maximilian Ruecker, Michael Vogt, Oliver Linton
Modern economic panel data sets are often high-dimensional: they contain information on a wide variety of control variables whose number may even exceed the sample size. Neverthele…
Piecewise Constant Hazard Estimation with the Fused Lasso
Manuel Rosenbaum, Jan Beyersmann, Michael Vogt
In applied time-to-event analysis, a flexible and useful approach is to model the hazard rate as a piecewise constant function of time. However, the change points and values of the…
Multiscale Comparison of Nonparametric Trend Curves
Marina Khismatullina, Michael Vogt
We develop new econometric methods for the comparison of nonparametric time trends. In many applications, practitioners are interested in whether the observed time series all have…
Estimation and Inference in High-Dimensional Panel Data Models with Interactive Fixed Effects
Maximilian Ruecker, Michael Vogt, Oliver Linton +1
We develop new econometric methods for estimation and inference in high-dimensional panel data models with interactive fixed effects. Our approach can be regarded as a non-trivial…
Nonparametric comparison of epidemic time trends: the case of COVID-19
Marina Khismatullina, Michael Vogt
The COVID-19 pandemic is one of the most pressing issues at present. A question which is particularly important for governments and policy makers is the following: Does the virus s…
Estimating the Lasso's Effective Noise
Johannes Lederer, Michael Vogt
Much of the theory for the lasso in the linear model hinges on the quantity , which we call the lasso's effective…
Multiscale clustering of nonparametric regression curves
Michael Vogt, Oliver Linton
In a wide range of modern applications, we observe a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in…
Multiscale inference and long-run variance estimation in nonparametric regression with time series errors
Marina Khismatullina, Michael Vogt
In this paper, we develop new multiscale methods to test qualitative hypotheses about the regression function m in a nonparametric regression model with fixed design points and tim…
On the Differences between L2-Boosting and the Lasso
Michael Vogt
We prove that L2-Boosting lacks a theoretical property which is central to the behaviour of l1-penalized methods such as basis pursuit and the Lasso: Whereas l1-penalized methods a…
Clustering with Statistical Error Control
Michael Vogt, Matthias Schmid
This paper presents a clustering approach that allows for rigorous statistical error control similar to a statistical test. We develop estimators for both the unknown number of clu…
Detecting Gradual Changes in Locally Stationary Processes
Michael Vogt, Holger Dette
In a wide range of applications, the stochastic properties of the observed time series change over time. The changes often occur gradually rather than abruptly: the prop- erties ar…
Detecting gradual changes in locally stationary processes
Michael Vogt, Holger Dette
In a wide range of applications, the stochastic properties of the observed time series change over time. The changes often occur gradually rather than abruptly: the properties are…
Nonparametric regression for locally stationary time series
Michael Vogt
In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extens…