Penalized time-varying model averaging

This paper proposes a new penalized time-varying model averaging method to determine optimal time-varying combination weights for candidate models, which avoids over-fitting and yields sparseness from various potential predictive variables. The asymptotic optimality and convergence rate of the selected weights are derived even when all candidate models are misspecified, and the consistency and normality of the proposed time-varying model averaging estimator are obtained when the true model is included in the candidate models. Simulation studies and empirical applications to inflation forecasting highlight the merits of the proposed method.