Tracking the Business Cycle of the Euro Area
A Multivariate Model-Based Bandpass Filter
Bibliographic Data
| ID | 19420043 |
|---|---|
| Authors | João Valle e Azevedo (0000-0002-3538-3860, Stanford University), Siem Jan Koopman (0000-0002-4440-9524, Vrije Universiteit Amsterdam), Antonio Rúa (0000-0002-6915-2067, Banco de Portugal) |
| Year | 2006 |
| Volume | 24 |
| Issue | 3 |
| Pages | 278-290 |
| Publication date | 2006-07-01 |
| 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 | 10.1198/073500105000000261 |
| OpenAlex | W2068326366 |
| Language | EN |
| Citations received | 3 |
| References cited | 17 |
This article proposes a multivariate bandpass filter based on the trend plus cycle decomposition model. The underlying multivariate dynamic factor model relies on specific formulations for trend and cycle components and produces smooth business cycle indicators with bandpass filter properties. Furthermore, cycle shifts for individual time series are incorporated as part of the multivariate model and estimated simultaneously with the remaining parameters. The inclusion of leading, coincident, and lagging variables for the measurement of the business cycle is therefore possible without a prior analysis of lead–lag relationships between economic variables. This method also permits the inclusion of time series recorded with mixed frequencies. For example, quarterly and monthly time series can be considered simultaneously without ad hoc interpolations. The multivariate approach leads to a business cycle indicator that is less subject to revisions than those produced by univariate filters. The reduction of revisions is a key feature in real-time assessment of the economy. Finally, the proposed method computes a growth indicator as a byproduct. The new approach of tracking business cycle and growth indicators is illustrated in detail for the Euro area. The analysis is based on nine key economic time series
Business cycle · Dynamic factor · Econometrics · Economics · Hodrick–Prescott filter · Lag · Lagging · Macroeconomics · Multivariate statistics · Statistics · Univariate · Computer Science · Global Financial Crisis and Policies · Italy: Economic History and Contemporary Issues · Mathematics · Monetary Policy and Economic Impact
The Band Pass Filter
Postwar U.S. Business Cycles
Detrending, stylized facts and the business cycle
The Use of Butterworth Filters for Trend and Cycle Estimation in Economic Time Series
Macroeconomic Forecasting Using Diffusion Indexes
The Unreliability of Output-Gap Estimates in Real Time
General Model-Based Filters for Extracting Cycles and Trends in Economic Time Series
The Generalized Dynamic-Factor Model
Measuring Business Cycles
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,19 |
| Citation span | 2010 - 2022 (13) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |