Forecasting Thai inflation from univariate Bayesian regression perspective
从单变量贝叶斯回归视角预测泰国通胀AI 解读 ↓
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从单变量贝叶斯回归视角预测泰国通胀
- 研究问题
- 在泰国通胀预测中，不同贝叶斯收缩先验、预测变量规模与随机波动设定如何影响点预测、密度预测及两侧尾部表现？
- 方法与思路
- 作者比较多种收缩先验与非信息先验，考察加入或不加入随机波动的模型，并以 RMSE、qwCRPS 和预测对数似然评价。
- 材料陈述的贡献
- 摘要称无随机波动的大规模模型中若干收缩先验表现较好，随机波动模型总体较弱；通缩尾部比通胀上行尾部更容易预测。
- 阅读关注点
- 应核对所谓单变量设定与大量预测变量的关系、预测窗口及先验调参，并检查尾部评分权重、危机样本和不同预测期的结果。
- 阅读边界
- 本文为 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明样本时期或显著性，不能把泰国样本结论推广为随机波动普遍无效。
原文摘要
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.