34 citations · 45 across the 18 of their papers we have counts for
22 papers
Drivers of Success: A Bayesian State-Space Model to Disentangling Latent Driver and Constructor Abilities in Formula One
Tim Lindner, Rui Jorge Almeida, Nalan Baştürk +1
Formula One outcomes reflect the joint contributions of drivers and constructors, but these contributions are unobserved and vary over time. We propose a Bayesian state-space model…
Sparse Tree-Based Aggregation for Time Series Regressions
Marie Corillon, Stephan Smeekes, Ines Wilms
High-dimensional time series regressions are often regularized to produce sparse coefficients. We show that temporal aggregation provides a powerful alternative to reduce dimension…
Autotune: fast, accurate, and automatic tuning parameter selection for Lasso
Tathagata Sadhukhan, Ines Wilms, Stephan Smeekes +1
Least absolute shrinkage and selection operator (Lasso), the popular variable selection engine for high-dimensional regression, is commonly tuned using cross-validation (CV). This…
Estimation of Latent Group Structures in Time-Varying Panel Data Models
Paul Haimerl, Stephan Smeekes, Ines Wilms
We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly…
Transmission Channel Analysis in Dynamic Models
Enrico Wegner, Lenard Lieb, Stephan Smeekes +1
We propose a framework for analysing transmission channels in a large class of dynamic models. We formulate our approach both using graph theory and potential outcomes, which we sh…
High-Dimensional Granger Causality for Climatic Attribution
Marina Friedrich, Luca Margaritella, Stephan Smeekes
In this paper we test for Granger causality in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcing…
Inference in Non-stationary High-Dimensional VARs
Alain Hecq, Luca Margaritella, Stephan Smeekes
In this paper we construct an inferential procedure for Granger causality in high-dimensional non-stationary vector autoregressive (VAR) models. Our method does not require knowled…
Sparse High-Dimensional Vector Autoregressive Bootstrap
Robert Adamek, Stephan Smeekes, Ines Wilms
We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consi…
Local Projection Inference in High Dimensions
Robert Adamek, Stephan Smeekes, Ines Wilms
In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local pro…
Min(d)ing the President: A text analytic approach to measuring tax news
Lenard Lieb, Adam Jassem, Rui Jorge Almeida +2
Economic agents react to signals about future tax policy changes. Consequently, estimating their macroeconomic effects requires identification of such signals. We propose a novel t…
bootUR: An R Package for Bootstrap Unit Root Tests
Stephan Smeekes, Ines Wilms
Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in th…
Lasso Inference for High-Dimensional Time Series
Robert Adamek, Stephan Smeekes, Ines Wilms
In this paper we develop valid inference for high-dimensional time series. We extend the desparsified lasso to a time series setting under Near-Epoch Dependence (NED) assumptions a…
High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration
Stephan Smeekes, Etienne Wijler
We investigate how the possible presence of unit roots and cointegration affects forecasting with Big Data. As most macroeoconomic time series are very persistent and may contain u…
A statistical analysis of time trends in atmospheric ethane
Marina Friedrich, Eric Beutner, Hanno Reuvers +6
Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an…
A General Framework for Prediction in Time Series Models
Eric Beutner, Alexander Heinemann, Stephan Smeekes
In this paper we propose a general framework to analyze prediction in time series models and show how a wide class of popular time series models satisfies this framework. We postul…
Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure
Alain Hecq, Luca Margaritella, Stephan Smeekes
We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the v…
A dynamic factor model approach to incorporate Big Data in state space models for official statistics
Caterina Schiavoni, Franz Palm, Stephan Smeekes +1
In this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high-dimensional data…
An Automated Approach Towards Sparse Single-Equation Cointegration Modelling
Stephan Smeekes, Etienne Wijler
In this paper we propose the Single-equation Penalized Error Correction Selector (SPECS) as an automated estimation procedure for dynamic single-equation models with a large number…
A Residual Bootstrap for Conditional Value-at-Risk
Eric Beutner, Alexander Heinemann, Stephan Smeekes
A fixed-design residual bootstrap method is proposed for the two-step estimator of Francq and Zakoïan (2015) associated with the conditional Value-at-Risk. The bootstrap's consiste…
Autoregressive Wild Bootstrap Inference for Nonparametric Trends
Marina Friedrich, Stephan Smeekes, Jean-Pierre Urbain
In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and do…
A Justification of Conditional Confidence Intervals
Eric Beutner, Alexander Heinemann, Stephan Smeekes
To quantify uncertainty around point estimates of conditional objects such as conditional means or variances, parameter uncertainty has to be taken into account. Attempts to incorp…
Inference for Impulse Responses under Model Uncertainty
Lenard Lieb, Stephan Smeekes
In many macroeconomic applications, confidence intervals for impulse responses are constructed by estimating VAR models in levels - ignoring cointegration rank uncertainty. We inve…