Binary response models for heterogeneous panel data with interactive fixed effects

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross sectional dimension and temporal to diverge. From a practical point of view, proposed framework can be applied predict probability corporate failure, conduct credit rating analysis, etc. Theoretically methodologically, establish link between maximum likelihood estimation least squares approach, provide simple information criterion detect number factors, achieve asymptotic distributions accordingly. addition, intensive simulations examine theoretical findings. empirical study, focus on sign prediction stock returns, then use results forecast portfolio analysis. By implementing rolling-window based out-of-sample forecasts, show finite-sample performance demonstrate relevance model method.