In this paper we study neural networks and their approximating power in panel data models. We provide asymptotic guarantees on deep feed-forward network estimation of the conditional mean, building work Farrell et al. (2021), explore latent patterns cross-section. use proposed estimators to forecast progression new COVID-19 cases across G7 countries during pandemic. find significant forecasting...

this paper investigates the relationship between inflation and growth uncertainty in iran for the period of 1988-2008 by using quarterly data. we employ generalized autoregressive conditional heteroscedasticity in mean (garch-m) model to estimate time-varying conditional residual variance of growth, as a standard measures of growth uncertainty. the empirical evidence shows that growth uncertain...