We study a panel data model with heterogeneous effects, allowing slopes to vary across individuals and time. To reduce dimensionality, we assume these slopes follow a factor structure, so slope matrices can be estimated via low-rank regularized regression. We propose a multi-step estimation procedure incorporating sample splitting and partialing-out to enable valid inference after penalized estimation. We establish the asymptotic normality of the resulting estimator, facilitating inference for individualtime- specific effects and their cross-sectional averages. The method’s performance is illustrated through simulations and an empirical application.

This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an additively nonseparable way, thus incorporating rich forms of unobserved heterogeneity. Our identification strategy exploits multivariate monotonicity in parametric indices, and uses the logical contraposition of an intertemporal inequality on choice probabilities to obtain identifying restrictions. We provide a consistent estimation procedure, and demonstrate the practical advantages of our method with Monte Carlo simulations and an empirical illustration on popcorn sales with the NielsenIQ data.

Practical real-world activities consume energy and emit thermal infrared radiation (TIR). Leveraging this physical fact, we develop a direct, real-time measure of firms’ operating activity using satellite data. Tracking 28,236 factories of Chinese listed firms, we find TIR declines significantly following operational shocks and strongly forecasts subsequent sales growth, costs, investment, employment, and profits. TIR also predicts future stock returns, especially among opaque firms and those with limited investor access, yet sophisticated investors largely ignore this information. Our findings highlight TIR as a distinctive, under-exploited indicator of corporate fundamentals.