Forecasting in small open emerging economies Evidence from Thailand

Forecasting inflation in small open economies is difficult because limited time series and strong external exposures create an imbalance between few observations and many potential predictors. We study this challenge using Thailand as a representative case, combining more than 450 domestic and international indicators. We evaluate modern Bayesian shrinkage and factor models, including Horseshoe regressions, factor-augmented autoregressions, factor-augmented VARs, dynamic factor models, and Bayesian additive regression trees. Our results show that factor models dominate at short horizons, when global shocks and exchange rate movements drive inflation, while shrinkage-based regressions perform best at longer horizons. These models not only improve point and density forecasts but also enhance tail-risk performance at the one-year horizon. Shrinkage diagnostics, on the other hand, additionally reveal that Google Trends variables, especially those related to food essential goods and housing costs, progressively rotate into predictive importance as the horizon lengthens. This underscores their role as forward-looking indicators of household inflation expectations in small open economies.