Factor-Augmented Varma Models With Macroeconomic Applications
Bibliographic Data
| ID | 19418937 |
|---|---|
| Authors | Jean‐Marie Dufour (0000-0002-7731-2278, McGill University), Jean-Marie Dufour, Dalibor Stevanović (0000-0002-0084-4831, Université du Québec à Montréal) |
| Year | 2013 |
| Volume | 31 |
| Issue | 4 |
| Pages | 491-506 |
| Publication date | 2013-10-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2013.818005 |
| OpenAlex | W1996340620 |
| Language | EN |
| Citations received | 3 |
| References cited | 36 |
We study the relationship between vector autoregressive moving-average (VARMA) and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors cannot both follow finite-order VAR models. Instead, a VAR factor dynamics induces a VARMA process, while a VAR process entails VARMA factors. We propose to combine factor and VARMA modeling by using factor-augmented VARMA (FAVARMA) models. This approach is applied to forecasting key macroeconomic aggregates using large U.S. and Canadian monthly panels. The results show that FAVARMA models yield substantial improvements over standard factor models, including precise representations of the effect and transmission of monetary policy
Autoregressive model · Autoregressive–moving-average model · Dynamic factor · Econometrics · Factor analysis · Moving average · Statistics · Vector autoregression · Computer Science · Economic theories and models · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact · Applied Mathematics
New Introduction to Multiple Time Series Analysis
Nowcasting
Determining the Number of Factors in Approximate Factor Models
Inferential Theory for Factor Models of Large Dimensions
Forecasting Using Principal Components From a Large Number of Predictors
Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel
Forecasting Contemporaneously Aggregated Vector Arma Processes
Practical Methods for Modeling Weak Varma Processes
Macroeconomic Forecasting Using Diffusion Indexes
Real-Time Measurement of Business Conditions
Determining the Number of Primitive Shocks in Factor Models
The Generalized Dynamic-Factor Model
Latent Variables in Socio-Economic Models
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2017 - 2024 (8) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |