Working Papers

Working Papers

We develop a multi-sector-country model with input-output linkages to study the effects of sectoral demand and supply shocks within the global trade and production network. Using the model, we quantify output losses of advanced economies (AEs) stemming from lack of vaccinations in the emerging markets and developing economies (EMDEs) during Covid-19. The sectoral shocks for 65 countries and 35 sectors are based on changes in sectoral consumption demand and labor supply as a function of infections. Endogenous lockdowns triggered by lack of vaccinations in EMDEs hurt AEs via a shortage of intermediate inputs, higher import prices, and weak demand for their exports. We provide upper and lower bound estimates for negative output effects of global supply chain disruptions, depending on the degree of complementarity across factors of production. Vaccinating EMDEs is a high return investment for AEs to smooth out the economic impact of the pandemic in their home countries.

Media coverage: New York Times, Economist, Guardian, Washington Post, Financial Times, Voice of America, Irish Times, BBC World, BBC Radio, CBC, Channel 4 (UK), France 24, Manila Times, Rio Times, and many other  leading global and local media outlets.

See the VOX (EU) piece on the findings of the paper here


We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of US real GDP. Specifically, we use the conventional dynamic factor model together with a stochastic volatility component as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants’ predictions, often used as a measure of ‘ambiguity’, conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.


We propose a joint modeling strategy for timing the joint distribution of the returns and their volatility. We do this by incorporating the potentially asymmetric links into the system of ‘independent’ predictive regressions of returns and volatility, allowing for asymmetric cross-correlations, denoted as instantaneous leverage effects, in addition to cross-autocorrelations between returns and volatility, denoted as intertemporal leverage effects. We show that while the conventional intertemporal leverage effects bear little economic value, our results point to the sizeable value of exploiting the contemporaneous asymmetric link between returns and volatility. Specifically, a mean-variance investor would be willing to pay several hundred basis points to switch from the strategies based on conventional predictive regressions of mean and volatility in isolation of each other to the joint models of returns and its volatility, taking the link between these two moments into account. Moreover, our findings are robust to various effects documented in the literature.