Benchmarking econometric and machine learning methodologies in nowcasting GDP
Bibliographic Data
| ID | 21542339 |
|---|---|
| Authors | Daniel Hopp (0000-0003-2725-0256, corresponding author) |
| Year | 2024 |
| Volume | 66 |
| Issue | 5 |
| Pages | 2191-2247 |
| Publication date | 2024-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Empirical Economics (JOURNAL) |
| Journal identifiers | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-023-02515-6 |
| OpenAlex | W4388337275 |
| Language | EN |
| References cited | 46 |
Bayesian probability · Bayesian vector autoregression · Benchmarking · Econometrics · Economics · Geography · Machine learning · Nowcasting · Predictive power · Vector autoregression · Complex Systems and Time Series Analysis · Computer Science · Market Dynamics and Volatility · Monetary Policy and Economic Impact · Artificial Intelligence
Regularization and Variable Selection Via the Elastic Net
Large Bayesian vector auto regressions
Gradient boosting machines, a tutorial
Machine Learning
Macroeconomics and Reality
Regression Shrinkage and Selection Via the Lasso
Machine Learning Time Series Regressions With an Application to Nowcasting
Real-Time Forecasting With a Mixed-Frequency VAR
Macroeconomic Forecasting With Mixed-Frequency Data
Vector Autoregressions
| Highly cited | No |
|---|