In the DFG funded project "GLASS - The Global Augmented State Space Error Correction Model: Structure Theory, Estimation and Inference" we develop econometric modeling methodology for (macro-) economic multivariate time series. Such data typically shows cointegrating behaviour which is often modeled using the vector error correction representation (VECM). The VECM relying on the vector autoregressive framework leads to huge models when several regions are modeled simultaneously. Typically some regions will be more influential than others. The corresponding high-dimensional vector error correction models can accordingly be restricted in various ways to account for this asymmetry, the globally augmented vector autoregressive model and the factor augmented models being two alternatives.
In the project we replace the VECM by the more flexibel state space error correction model (SPECM) setting. In the SPECM we then investigate the relations between globally augmented models and factor modeling.
For more details see https://gepris.dfg.de/gepris/projekt/469278259?language=en.
We are searching for a motivated PhD candidate with a strong background in time series analysis and statistics in general. Knowledge of econometric modeling of co-integrated processes is an advantage but not a requirement. The successful candidate is expected to define his/her PhD thesis topic from within the goals of the project.
The position is embedded in the econometrics research group in Bielefeld.
The project is conducted in close cooperation with the project partners, Dietmar Bauer in Bielefeld and Martin Wagner in Klagenfurt.
Bielefeld offers a rich research environment for doctoral candidates including a structured PhD program in the BigSEM (https://www.uni-bielefeld.de/fakultaeten/wirtschaftswissenschaften/einrichtungen/bigsem/). Depending on the background two different tracks will be open, Economics or Data Science (to be opened soon).
The position amounts to 75% of E13-TV-L and is initially limited to three years (extensions are possible subject to funding availability). This does not involve any teaching load. There might be possibilities to add funding for a full position, then involving teaching load.