7 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…
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…
Autotune: fast, accurate, and automatic tuning parameter selection for Lasso
Tathagata Sadhukhan, Ines Wilms, Stephan Smeekes +1
Least absolute shrinkage and selection operator (Lasso), a popular method for high-dimensional regression, is now used widely for estimating high-dimensional time series models suc…
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…