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We estimate the short-run effects of severe weather shocks on local economic activity and cross-border spillovers operating through economic linkages between U.S. states. We measure weather shocks using emergency declarations triggered by natural disasters and estimate their impacts with a monthly Global Vector Autoregressive (GVAR) model for U.S. states. Impulse responses highlight country-wide effects of weather shocks hitting individual regions. Taking into account economic interconnections between states allows capturing much stronger spillovers than those associated with mere spatial adjacency. Also, geographical heterogeneity is critical for assessing country-wide effects of weather shocks, and network effects amplify the local impacts of shocks.

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (``multilayer network"), which are summarized into a smaller number of network matrices (``network factors") through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors and the coefficients of the FNAR. Our approach combines two different dimension-reduction techniques and can be applied to ultra-high-dimensional datasets. Simulation results show the goodness of our approach in finite samples. In an empirical application, we use the FNAR to investigate the cross-country interdependence of GDP growth rates based on a variety of international trade and financial linkages. The model provides a rich characterization of macroeconomic network effects.

This paper investigates the transmission of funding liquidity shocks, credit risk shocks and unconventional monetary policy within the Euro area. To this aim, we estimate a financial GVAR model for Germany, France, Italy and Spain on monthly data over the period 2006-2017. The interactions between repo markets, sovereign bonds and banks' CDS spreads are analyzed, explicitly accounting for the country-specific effects of the ECB's asset purchase programmes. Impulse response analysis signals marginally significant core-periphery heterogeneity, flight-to-quality effects and spillovers between liquidity conditions and credit risk. Simulated reductions in ECB programmes tend to result in higher government bond yields and bank CDS spreads, especially for Italy and Spain, as well as in falling repo trade volumes and rising repo rates across the Euro area. However, only a few responses to shocks achieve statistical significance.

Using a large quarterly macroeconomic dataset for the period 1960-2017, we document the ability of specific financial ratios from the housing market and firms' aggregate balance sheets to predict GDP over medium-term horizons in the United States. A cyclically adjusted house price-to-rent ratio and the liabilities-to-income ratio of the non-financial non-corporate business sector provide the best in-sample and out-of-sample predictions of GDP growth over horizons of one to five years, based on a wide variety of rankings. Small forecasting models that include these indicators outperform popular high-dimensional models and forecast combinations. The predictive power of the two ratios appears strong during both recessions and expansions, stable over time, and consistent with well-established macro-finance theory., Comment: The final version of this paper is in press at the International Journal of Forecasting. Available online 5 July 2023. https://doi.org/10.1016/j.ijforecast.2023.05.007

We propose an approach for generating macroeconomic density forecasts that incorporate information on multiple scenarios defined by experts. We adopt a regime-switching framework in which sets of scenarios ("views") are used as Bayesian priors on economic regimes. Predictive densities coming from different views are then combined by optimizing objective functions of density forecasting. We illustrate the approach with an empirical application to quarterly real-time forecasts of U.S. GDP growth, in which we exploit the Fed's macroeconomic scenarios used for bank stress tests. We show that the approach achieves good accuracy in terms of average predictive scores and good calibration of forecast distributions. Moreover, it can be used to evaluate the contribution of economists' scenarios to density forecast performance.