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the COVID-19 pandemic, economists have struggled to obtain reliable economic predictions, with standard models becoming outdated and their forecasting performance deteriorating rapidly.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. We collect more than a thousand time series from conventional and unconventional sources, complementing traditional macroeconomic variables with timely big data indicators and assessing their added value at nowcasting. The second novelty consists of a methodology to merge an enormous amount of non-encompassing data with a large battery of classical and more sophisticated forecasting methods in a seamlessly dynamic Bayesian framework. Specifically, we introduce an innovative "selection prior" that is used not as a way to influence model outcomes, but as a selecting device among competing models. By applying this methodology to the COVID-19 crisis, we show which variables are good predictors for nowcasting Gross Domestic Product and draw lessons for dealing with possible future crises.

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. We provide extensive model validation, including a comparison to off-model evidence for COVID-related restrictions, and a comparison of different model specifications.

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. Spillover of financial shocks is amplified at the zero lower bound, at which investment risk takes on the characteristics of a general uncertainty shock. The model results suggest that integrated financial markets should provide a powerful motivation for international policy coordination to prevent financial turmoil.

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. The concentrations of four representative persistent organic pollutants (POPs) in two representative species of the coastal marine food web of the Lagoon of Venice (Italy) were taken as model outputs. The screening analysis showed that the ranking was remarkably different in relation to the species and chemical being considered. The subsequent Monte Carlo-based quantitative analysis pointed out that the relationships among some of the parameters and the model outputs were nonlinear. The nonlinear regression showed that the fraction of output variance accounted for by each parameter was strongly dependent on the range of the octanol-water partition coefficient (K(OW)) values being considered. For the less hydrophobic chemicals, the main sources of model uncertainty were the parameters related to the respiratory bioaccumulation, whereas for the more hydrophobic ones, K(OW) and the other parameters related to the dietary uptake explained the largest fractions of the variance of the chemical concentrations in the organisms. The analysis highlighted that efforts are still needed for reducing uncertainty of model parameters to get reliable results from the application of food web bioaccumulation models.

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 = 1.20) equivalence ratios. Uncertainties of laminar flame velocity, maximal flame temperature, and maximal concentrations of radicals H, O, OH, CH, and CH(2) were investigated. Global uncertainty analysis methods included the Morris method, the Monte Carlo analysis with Latin hypercube sampling, and an improved version of the Sobol' method. Assumed probability density functions (pdf's) were assigned to the rate coefficients of all the 175 reactions and to the enthalpies of formation of the 37 species. The analyses provided the following answers: approximate pdf's and standard deviations of the model results, minimum and maximum values of the results at any physically realistic parameter combination, and the contribution of the uncertainty of each parameter to the uncertainty of the model result. The uncertainty of a few rate parameters and a few enthalpies of formation causes most of the uncertainty of the model results. Most uncertainty comes from the inappropriate knowledge of kinetic data, but the uncertainty caused by thermodynamic data is also significant.