arXiv 5 Mar 2021 · Econometrics · publishedJournal of Economic Dynamics and Control (2022) · 18 citations (OpenAlex)
arXiv:2103.03632 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regression (QR) models featuring conditional heteroskedasticity. I use data augmentation schemes to render the model conditionally Gaussian and develop an efficient Gibbs sampling algorithm. Regularization of the high-dimensional parameter space is achieved via flexible dynamic shrinkage priors. A simple version of TVP-QR based on an unobserved component model is applied to dynamically trace the quantiles of the distribution of inflation in the United States, the United Kingdom and the euro area. In an out-of-sample forecast exercise, I find the proposed model to be competitive and perform particularly well for higher-order and tail forecasts. A detailed analysis of the resulting predictive distributions reveals that they are sometimes skewed and occasionally feature heavy tails.
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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 | Chan JC (2017) The stochastic volatility in mean model with time-varying parameters: An application to inflation modeling | 1.000 | 7 | 3 | 100% |
| 2 | Huber F, and Pfarrhofer M (2021) Dynamic shrinkage in time-varying parameter stochastic volatility in mean models | 0.843 | 4 | 3 | 75% |
| 3 | Jarociński M, and Lenza M (2018) An inflation-predicting measure of the output gap in the euro area | 0.843 | 3 | 3 | 100% |
| 4 | Stock JH, and Watson MW (2007) Why has US inflation become harder to forecast? | 0.811 | 4 | 2 | 100% |
| 5 | Kozumi H, and Kobayashi G (2011) Gibbs sampling methods for Bayesian quantile regression | 0.737 | 4 | 3 | 50% |
| 6 | Korobilis D (2017) Quantile regression forecasts of inflation under model uncertainty | 0.737 | 3 | 2 | 100% |
| 7 | Korobilis D, Landau B, Musso A, and Phella A (2021) The time-varying evolution of inflation risks | 0.737 | 3 | 2 | 100% |
| 8 | Rodrigues T, and Fan Y (2017) Regression adjustment for noncrossing Bayesian quantile regression | 0.693 | 5 | 1 | 100% |
| 9 | Caldara D, Scotti C, and Zhong M (2021) Macroeconomic and Financial Risks: A Tale of Volatility | 0.644 | 2 | 2 | 100% |
| 10 | Chan JC, Koop G, and Potter SM (2013) A new model of trend inflation | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 70 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions | 0.644 | 2 | 2 |
| 2 | Inflation Target at Risk: A Time-varying Parameter Distributional Regression | 0.405 | 1 | 1 |
| 3 | 1.4cm bred Maximally Forward-Looking Core Inflation | 0.405 | 1 | 1 |
| 4 | Momentum Informed Inflation-at-Risk | 0.405 | 1 | 1 |
| 5 | International vulnerability of inflation | 0.405 | 1 | 1 |