Magyar nyelven nem elérhető
Jan Linzenich
- 13 December 2024
- WORKING PAPER SERIES - No. 3004Details
- Abstract
- We provide a versatile nowcasting toolbox that supports three model classes (dynamic factor models, large Bayesian VAR, bridge equations) and offers methods to manage data selection and adjust for Covid-19 observations. The toolbox aims at simplifying two key tasks: creating new nowcasting models and improving the policy analysis. For model creation, the toolbox automatizes testing input variables, assessing model accuracy, and checking robustness to the Covid period. The toolbox is organized along a structured three-step approach: variable pre-selection, model selection, and Covid robustness. Non-specialists can easily follow these steps to develop high-performing models, while experts can leverage the automated tests and analyses. For regular policy use, the toolbox generates a large range of outputs to aid conjunctural analysis like news decomposition, confidence bands, alternative forecasts, and heatmaps. These multiple outputs aim at opening the "black box" often associated with nowcasts and at gauging the reliability of real-time predictions. We showcase the toolbox features to create a nowcasting model for global GDP growth. Overall, the toolbox aims at facilitating creation, evaluation, and deployment of nowcasting models. Code and templates are available on GitHub: https://github.com/baptiste-meunier/Nowcasting_toolbox.
- JEL Code
- C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
- 8 February 2024
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 1, 2024Details
- Abstract
- The pandemic triggered the deepest global recession (albeit short-lived) since the Second World War amid large-scale policy support, and led to a sweeping fall in world trade. Following the initial COVID-19 shock, trade staged a rapid recovery, but from the second half of 2022 world trade growth started to decelerate markedly and in 2023 it is estimated to have been considerably below its pre-pandemic average. This box reviews the factors behind the buoyant recovery of global trade following the initial COVID-19 shock and the reasons for its lacklustre performance in 2023, finding that the latter mainly reflects the unwinding of some specific post-pandemic factors (e.g. the rotation of demand from trade-intensive goods towards services owing to the full relaxation of pandemic containment measures) and a less trade-friendly composition of global activity.
- JEL Code
- F01 : International Economics→General→Global Outlook
F1 : International Economics→Trade
F4 : International Economics→Macroeconomic Aspects of International Trade and Finance