Factorial-Decay Lag Adaptive LASSO in High-Dimensional ADL Models

This paper proposes the factorial-decay lag adaptive LASSO (FLAdaLASSO), a penalization approach for variable selection in high-dimensional autoregressive time-series models. The method extends existing lag-weighted LASSO procedures by introducing a factorial-decay weighting structure that imposes increasingly strong shrinkage on higher-order lags, thereby enhancing discrimination between relevant and irrelevant predictors in persistent and highly collinear settings. Monte Carlo simulation experiments across a range of sample sizes, lag dimensions, and dependence structures show that FLAdaLASSO improves lag-selection accuracy, reduces overselection, and stabilizes parameter estimation relative to standard LASSO-type methods, while delivering forecasting performance comparable to or better than existing lag-weighted adaptive approaches. Empirical applications to inflation data from Thailand, China, and the United States further illustrate the practical relevance of the proposed method, highlighting its ability to achieve low forecasting error with relatively parsimonious model specifications across heterogeneous macroeconomic environments.