Fast and efficient estimation of macromodels with large shocks

We propose a new approach for efficiently estimating and analyzing macroeconomic models subject to large shocks. We apply the methodology to a two-sector model that captures the heterogeneous exposure of different sectors to the COVID-19 pandemic. We solve the model nonlinearly and propose a new nonlinear, non-Gaussian filter designed to handle large shocks and identify their source and time location. Monte Carlo experiments show that the estimation and identification of large shocks are feasible with a massively reduced running time. Empirical results indicate that the pandemic-induced economic downturn can be reconciled with a combination of large demand and supply shocks. Finally, we present a set of counterfactual experiments to filter out potential demand and supply shock complementarities, and perform a robustness exercise to check the sensitivity of the model parameters to large shocks.