Testing big data in a big crisis: Nowcasting under COVID-19.

During the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly. This paper presents two novelties that could be adopted by forecasting institutions in unconventional times. The first innovation is the construction of an extensive data set for macroeconomic forecasting in Europe.

The euro area's pandemic recession: A DSGE-based interpretation.

This paper augments the European Commission's open-economy DSGE model (GM) with COVID-specific shocks ('forced savings', labour hoarding) and financially-constrained investors to account for the extreme volatility of private domestic demand and hours worked during COVID-19, and it estimates the model on euro area data for the period 1998q4-2021q4. It takes a pragmatic approach of adapting the workhorse model of a policy institution to COVID-19 data. 'Forced savings' are central to explain quarterly real GDP growth during the pandemic, complemented by contributions from foreign demand and trade, and the negative impact of persistently higher savings after the first wave.

Financial spillover and global risk in a multi-region model of the world economy.

This paper estimates a three-region DSGE model (EA, US, RoW) with international financial linkages in the form of cross-border equity holding and allowing for region-specific as well as global financial shocks, which match empirical measures of financial tightness and global stock market valuation. Spillover from financial shocks increases with international financial integration and is practically zero under full home bias in normal times. The global risk captures international synchronisation of financial cycles.

Global uncertainty and sensitivity analysis of a food-web bioaccumulation model.

A global uncertainty and sensitivity analysis (UA/SA) of a state-of-the-art, food-web bioaccumulation model was carried out. We used an efficient screening analysis technique to identify the subset of the most relevant input factors among the whole set of 227 model parameters. A quantitative UA/SA was then applied to this subset to rank the relevance of the parameters and to partition the variance of the model output among them by means of a nonlinear regression of the outcomes of 1,000 Monte Carlo simulations.

Local and global uncertainty analyses of a methane flame model.

Local and global uncertainty analyses of a flat, premixed, stationary, laminar methane flame model were carried out using the Leeds methane oxidation mechanism at lean (phi = 0.70), stoichiometric (phi = 1.00), and rich (phi = 1.