Sylvia Kaufmann

Article Frontmatter was published on April 1, 2023 in the journal Jahrbücher für Nationalökonomie und Statistik (volume 243, issue 2).

We document whether a simple, univariate model for quarterly GDP growth is able to deliver forecasts of yearly GDP growth in a crisis period like the Covid-19 pandemic, which may serve cross-checking forecasts obtained from elaborate and expert models used by forecasting institutions. We include shocks to the log number of short-time workers as timely available current-quarter indicator. Yearly GDP growth forecasts serve cross-checking, in particular at the outbreak of the pandemic.

Article Frontmatter was published on February 1, 2023 in the journal Jahrbücher für Nationalökonomie und Statistik (volume 243, issue 1).

Summary: Induced sparsity in the factor loading matrix identifies the factor basis, while rotational identification is obtained ex post by clustering methods closely related to machine learning. We extract meaningful economic concepts from a high‐dimensional data set, which together with observed variables follow an unrestricted, reduced‐form VAR process. Including a comprehensive set of economic concepts allows reliable, fundamental structural analysis, even of the factor augmented VAR itself. We illustrate this by combining two structural identification methods to further analyze the model. To account for the shift in monetary policy instruments triggered by the Great Recession, we follow separate strategies to identify monetary policy shocks. Comparing ours to other parametric and non‐parametric factor estimates uncovers advantages of parametric sparse factor estimation in a high dimensional data environment. Besides meaningful factor extraction, we gain precision in the estimation of factor loadings.

The number of short-time workers from January to April 2020 is used to now- and forecast quarterly GDP growth. We purge the monthly log level series from the systematic component to extract unexpected changes or shocks to log short-time workers. These monthly shocks are included in a univariate model for quarterly GDP growth to capture timely, current-quarter unexpected changes in growth dynamics. Included shocks additionally explain 24% in GDP growth variation. The model is able to forecast quite precisely the decrease in GDP during the financial crisis. It predicts a mean decline in GDP of 5.7% over the next two quarters. Without additional growth stimulus, the GDP level forecast remains persistently 4% lower in the long run. The uncertainty is large, as the 95% highest forecast density interval includes a decrease in GDP as large as 9%. A recovery to pre-crisis GDP level in 2021 lies only in the upper tail of the 95% highest forecast density interval.

Tensor-valued data are becoming increasingly available in economics and this calls for suitable econometric tools. We propose a new dynamic linear model for tensor-valued response variables and covariates that encompasses some well-known econometric models as special cases. Our contribution is manifold. First, we define a tensor autoregressive process (ART), study its properties and derive the associated impulse response function. Second, we exploit the PARAFAC low-rank decomposition for providing a parsimonious parametrization and to incorporate sparsity effects. We also contribute to inference methods for tensors by developing a Bayesian framework which allows for including extra-sample information and for introducing shrinking effects. We apply the ART model to time-varying multilayer networks of international trade and capital stock and study the propagation of shocks across countries, over time and between layers.