Forecasting Thai inflation from univariate Bayesian regression perspective

TL;DR

This paper evaluates Bayesian shrinkage priors for univariate Thai inflation forecasting, finding that simpler models with broad predictors often outperform complex SV models, especially in high-dimensional settings.

Contribution

It demonstrates the limited effectiveness of SV models in high-dimensional inflation forecasting and advocates for using advanced shrinkage priors with extensive predictors.

Findings

HS, DL, and LASSO outperform in large models without SV

Advanced priors better control left-tail risks (deflation)

Simpler models provide more reliable forecasts during crises

Abstract

This study investigates the forecasting performance of Bayesian shrinkage priors in predicting Thai inflation in a univariate setup, with a particular interest in comparing those more advance shrinkage prior to a likelihood dominated/noninformative prior. Our forecasting exercises are evaluated using Root Mean Squared Error (RMSE), Quantile-Weighted Continuous Ranked Probability Scores (qwCRPS), and Log Predictive Likelihood (LPL). The empirical results reveal several interesting findings: SV-augmented models consistently underperform compared to their non-SV counterparts, particularly in large predictor settings. Notably, HS, DL and LASSO in large-sized model setting without SV exhibit superior performance across multiple horizons. This indicates that a broader range of predictors captures economic dynamics more effectively than modeling time-varying volatility. Furthermore, while left-tail risks (deflationary pressures) are well-controlled by advanced priors (HS, HS+, and DL), right-tail risks (inflationary surges) remain challenging to forecast accurately. The results underscore the trade-off between model complexity and forecast accuracy, with simpler models delivering more reliable predictions in both normal and crisis periods (e.g., the COVID-19 pandemic). This study contributes to the literature by highlighting the limitations of SV models in high-dimensional environments and advocating for a balanced approach that combines advanced shrinkage techniques with broad predictor coverage. These insights are crucial for policymakers and researchers aiming to enhance the precision of inflation forecasts in emerging economies.