3 citations · 5 across the 2 of their papers we have counts for
7 papers
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…