Nonlinearities in macroeconomic tail risk through the lens of big data quantile regressions
Bibliographic Data
| ID | 21653446 |
|---|---|
| Authors | Jan Prüser (0000-0001-7435-7334, Department of Statistics TU Dortmund University Dortmund Germany, corresponding author), Florian Huber (0000-0002-2896-7921, Department of Economics University of Salzburg Salzburg Austria) |
| Year | 2024 |
| Volume | 39 |
| Issue | 2 |
| Pages | 269-291 |
| Publication date | 2024-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.3018 |
| OpenAlex | W4390270116 |
| Language | EN |
| Citations received | 1 |
| References cited | 55 |
Modeling and predicting extreme movements in GDP is notoriously difficult, and the selection of appropriate covariates and/or possible forms of nonlinearities are key in obtaining precise forecasts. In this paper, our focus is on using large datasets in quantile regression models to forecast the conditional distribution of US GDP growth. To capture possible nonlinearities, we include several nonlinear specifications. The resulting models will be huge dimensional, and we thus rely on a set of shrinkage priors. Since Markov chain Monte Carlo estimation becomes slow in these dimensions, we rely on fast variational Bayes approximations to the posterior distribution of the coefficients and the latent states. We find that our proposed set of models produces precise forecasts. These gains are especially pronounced in the tails. Using Gaussian processes to approximate the nonlinear component of the model further improves the good performance, in particular in the right tail
Bayesian probability · Conditional probability distribution · Covariate · Econometrics · Gaussian · Markov chain Monte Carlo · Monte Carlo method · Prior probability · Quantile · Quantile regression · Statistics · Computer Science · Forecasting Techniques and Applications · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact · Artificial Intelligence
Midas Regressions
Vulnerable Growth
Testing the equality of prediction mean squared errors
Regression Quantiles
Combining shrinkage and sparsity in conjugate vector autoregressive models
Adaptive Shrinkage in Bayesian Vector Autoregressive Models
Fred-Md
Common Drifting Volatility in Large Bayesian VARs
Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |