Do Leading Indicators Lead Peaks More Than Troughs
Bibliographic Data
| ID | 19418122 |
|---|---|
| Authors | Richard Paap (0000-0003-1616-0554, Tinbergen Institute), Rene Segers (0000-0002-6332-7268, Tinbergen Institute), Dick Van Dijk (0000-0002-1880-204X, Tinbergen Institute) |
| Year | 2009 |
| Volume | 27 |
| Issue | 4 |
| Pages | 528-543 |
| Publication date | 2009-10-01 |
| 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/jbes.2009.07061 |
| OpenAlex | W1594166008 |
| Language | EN |
| Citations received | 3 |
| References cited | 36 |
We develop a novel Markov switching vector autoregressive model to investigate the possibility that leading indicators have different lead times at business cycle peaks and at troughs. In this model, coincident and leading indicators share a common Markov state process, but their cycles are nonsynchronous, with the nonsynchronicity varying across regimes. An application shows that on average the Conference Board’s Composite Leading Index leads the Composite Coincident Index by nearly 1 year at peaks but by only 1 quarter at troughs. Allowing for asymmetric lead times yields improved real-time dating and forecasting of business cycle turning points
Autoregressive model · Business cycle · Econometrics · Economic indicator · Economics · Industrial production index · Lead time · Macroeconomics · Markov chain · Operations management · Recession · Statistics · Synchronicity · Complex Systems and Time Series Analysis · Computer Science · Market Dynamics and Volatility · Mathematics · Monetary Policy and Economic Impact
Time Series Analysis
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
The Calculation of Posterior Distributions by Data Augmentation
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
Output Fluctuations in the United States
Business Cycle Asymmetries
Bayes Estimates of Markov Trends in Possibly Cointegrated Series
A Comparison of the Real-Time Performance of Business Cycle Dating Methods
A More Timely and Useful Index of Leading Indicators
Business-Cycle Phases and Their Transitional Dynamics
Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts
Are Statistical Reporting Agencies Getting It Right? Data Rationality and Business Cycle Asymmetry
Inventories and the Three Phases of the Business Cycle
Dynamic Forecasts of Qualitative Variables
The Decline in U.S. Output Volatility
Testing for Volatility Changes in U.S. Macroeconomic Time Series
Measuring Business Cycles
| Citations per year | 0.6 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |