The modal factor model represents a novel statistical framework for dimension reduction in high dimensional panel data analysis. It is specifically designed to capture modal factors which exert a significant influence on the conditional mode of the distribution of the observables. Statistical inference for this model is developed based on a newly proposed modal component analysis, where modal factors and their corresponding loadings are estimated through maximizing a kernel-type objective function. Two computationally efficient and easy-to-implement algorithms-minorization-maximization and alternating maximization-are devised for the numerical computation of these estimators. Furthermore, two model selection criteria are proposed to determine the optimal number of modal factors. Under mild regularity conditions that accommodate both cross-sectional and time-series dependence, the asymptotic properties of the proposed estimators are established. Additionally, the limiting distributions of parameter estimators and forecasts derived from factor-augmented regressions incorporating estimated modal factors are rigorously derived. Simulation studies demonstrate that the proposed estimators exhibit favorable finite-sample performance, even in the presence of heavy-tailed, skewed, multimodal, and heteroskedastic error distributions. Finally, an application to inflation and industrial production index forecasting validates the practical merits and superior predictive power of modal factors in real-world macroeconomic scenarios.
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