Paponpat Taveeapiradeecharoen, Popkarn Arwatchanakarn
arXiv 8 May 2025 · Econometrics
arXiv:2505.05334 · PDF · Extracted main text
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.
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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 | Bedoui, A. and Lazar, N. A (2020) Bayesian empirical likelihood for ridge and lasso regressions | 0.737 | 3 | 2 | 100% |
| 2 | Carvalho, C. M., Polson, N. G., and Scott, J. G (2010) The horseshoe estimator for sparse signals | 0.737 | 3 | 2 | 100% |
| 3 | Cross, J. L., Hou, C., and Poon, A (2019) Macroeconomic forecasting with large bayesian vars: Global-local priors and the illusion of sparsity | 0.737 | 3 | 2 | 100% |
| 4 | Hoerl, A. E. and Kennard, R. W (1970) Ridge regression: Biased estimation for nonorthogonal problems | 0.737 | 3 | 2 | 100% |
| 5 | Makalic, E. and Schmidt, D. F (2015) A simple sampler for the horseshoe estimator | 0.737 | 3 | 2 | 100% |
| 6 | Bhadra, A., Datta, J., Polson, N. G., and Willard, B (2017) The horseshoe+ estimator of ultra-sparse signals | 0.644 | 2 | 2 | 100% |
| 7 | Bhadra, A., Datta, J., Polson, N. G., and Willard, B (2019) Lasso meets horseshoe | 0.644 | 2 | 2 | 100% |
| 8 | Giannone, D., Lenza, M., and Primiceri, G. E (2015) Prior selection for vector autoregressions | 0.644 | 2 | 2 | 100% |
| 9 | Huber, F., Koop, G., Onorante, L., Pfarrhofer, M., and Schreiner, J (2020) Nowcasting in a pandemic using non-parametric mixed frequency vars | 0.644 | 2 | 2 | 100% |
| 10 | Litterman, R. B (1986) Forecasting with bayesian vector autoregressions—five years of experience | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Forecasting in small open emerging economies: Evidence from Thailand | 0.405 | 1 | 1 |