Paponpat Taveeapiradeecharoen, Nattapol Aunsri
arXiv 18 Sep 2025 · Statistics — Applications
arXiv:2509.14805 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Stock, J. H. and Watson, M. W (2002) Macroeconomic forecasting using diffusion indexes | 1.000 | 6 | 4 | 100% |
| 2 | Bernanke, B. S., Boivin, J., and Eliasz, P (2005) Measuring the effects of monetary policy: a factor-augmented vector autoregressive (favar) approach | 1.000 | 6 | 3 | 100% |
| 3 | Carvalho, C. M., Polson, N. G., and Scott, J. G (2010) The horseshoe estimator for sparse signals | 1.000 | 5 | 3 | 100% |
| 4 | Bhattacharya, A., Chakraborty, A., and Mallick, B. K (2016) Fast sampling with gaussian scale mixture priors in high-dimensional regression | 0.874 | 5 | 2 | 100% |
| 5 | Manopimoke, P (2018) Thai inflation dynamics in a globalized economy | 0.874 | 5 | 2 | 100% |
| 6 | Cross, J. L., Hou, C., and Poon, A (2020) Macroeconomic forecasting with large bayesian vars: Global-local priors and the illusion of sparsity | 0.843 | 3 | 3 | 100% |
| 7 | Huber, F. and Feldkircher, M (2019) Adaptive shrinkage in bayesian vector autoregressive models | 0.843 | 3 | 3 | 100% |
| 8 | Gneiting, T. and Ranjan, R (2011) Comparing density forecasts using threshold-and quantile-weighted scoring rules | 0.811 | 4 | 2 | 100% |
| 9 | Nookhwun, N. and Manopimoke, P (2023) Disaggregated inflation dynamics in thailand: Which shocks matter? | 0.737 | 3 | 2 | 100% |
| 10 | Gneiting, T. and Raftery, A. E (2007) Strictly proper scoring rules, prediction, and estimation | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 53 scored citations.