We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

Stock prices declined abruptly in the wake of the Covid-19, reflecting both the deterioration of investors’ expectations of economic activity as well as the surge in risk aversion. In the following months, however, economic activity remained sluggish while equity markets bounced back. This...

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform...

We examine the effect of uncertainty on foreign direct investment inflows for a heterogeneous sample of advanced, emerging market and developing countries over a 25 year long (pre-Covid) sample. Using a push-pull framework, and controlling for both global and local factors, we find policy...

We examine whether the Chinese exchange rate is misaligned and how Chinese trade flows respond to the exchange rate and to economic activity. We find, first, that the Chinese currency, the renminbi (RMB), is substantially below the value predicted by estimates based upon a cross-country sample,...