activity
20132022
most citedNonparametric regression for locally stationary time series

142 citations · 145 across the 3 of their papers we have counts for

collaborators

6 papers

econ.EM2022

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…

stat.ME2020

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…

math.ST2019

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…

math.ST2019

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…

stat.ML20183 cited

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…

math.ST2013142 cited

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…