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Working Paper
Mean Group Estimation in Presence of Weakly Cross-Correlated Estimators
This paper extends the mean group (MG) estimator for random coefficient panel data models by allowing the underlying individual estimators to be weakly cross-correlated. Weak cross-sectional dependence of the individual estimators can arise, for example, in panels with spatially correlated errors. We establish that the MG estimator is asymptotically correctly centered, and its asymptotic covariance matrix can be consistently estimated. The random coefficient specification allows for correct inference even when nothing is known about the weak cross-sectional dependence of the errors. This is ...
Working Paper
Foreign Effects of Higher U.S. Interest Rates
This paper analyzes the spillovers of higher U.S. interest rates on economic activity in a large panel of 50 advanced and emerging economies. We allow the response of GDP in each country to vary according to its exchange rate regime, trade openness, and a vulnerability index that includes current account, foreign reserves, inflation, and external debt. We document large heterogeneity in the response of advanced and emerging economies to U.S. interest rate surprises. In response to a U.S. monetary tightening, GDP in foreign economies drops about as much as it does in the United States, with a ...
Working Paper
Deep Neural Network Estimation in Panel Data Models
In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward neural network estimation of the conditional mean, building on the work of Farrell et al. (2021), and explore latent patterns in the cross-section. We use the proposed estimators to forecast the progression of new COVID-19 cases across the G7 countries during the pandemic. We find significant forecasting gains over both linear panel and nonlinear time-series models. Containment or lockdown policies, as instigated at the national level by governments, ...