Real-Time Forecasting With a Mixed-Frequency VAR
Datos Bibliográficos
| ID | 19418379 |
|---|---|
| Autores | Frank Schorfheide (0000-0002-4256-634X, University of Pennsylvania), Dongho Song (Boston College) |
| Año | 2015 |
| Volumen | 33 |
| Número | 3 |
| Páginas | 366-380 |
| Fecha de publicación | 2015-07-03 |
| Peer Reviewed | Sí |
| Open Access | No |
| Tipo | ARTICLE |
| Revista | Journal of Business and Economic Statistics (JOURNAL) |
| Identificadores de la revista | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Editorial | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2014.954707 |
| OpenAlex | W2233673592 |
| Idioma | EN |
| Citas recibidas | 22 |
| Referencias citadas | 24 |
This article develops a vector autoregression (VAR) for time series which are observed at mixed frequencies--quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to implement a data-driven hyperparameter selection. Using a real-time dataset, we evaluate forecasts from the mixed-frequency VAR and compare them to standard quarterly frequency VAR and to forecasts from MIDAS regressions. We document the extent to which information that becomes available within the quarter improves the forecasts in real time. This article has online supplementary materials
Algorithm · Autoregressive model · Bayesian probability · Bayesian vector autoregression · Econometrics · Economics · Geography · Hyperparameter · Machine learning · Model selection · Nowcasting · Real-time data · State-space representation · Statistics · Time series · Vector autoregression · Computer Science · Financial Risk and Volatility Modeling · Forecasting Techniques and Applications · Mathematics · Monetary Policy and Economic Impact
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| Obras citantes distintas | 22 |
|---|---|
| Citas por año | 2,44 |
| Intervalo de citas | 2017 - 2026 (10) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 22 |