Tracking Economic Activity With Alternative High‐Frequency Data
Bibliographic Data
| ID | 21653281 |
|---|---|
| Authors | Florian Eckert (0000-0001-6191-0161, KOF Swiss Economic Institute ETH Zurich Zurich Switzerland), Philipp Kronenberg (0000-0002-7009-9048, KOF Swiss Economic Institute ETH Zurich Zurich Switzerland), Heiner Mikosch (0000-0003-0793-9646, KOF Swiss Economic Institute ETH Zurich Zurich Switzerland, corresponding author), Stefan Neuwirth (0000-0001-9139-4911, KOF Swiss Economic Institute ETH Zurich Zurich Switzerland) |
| Year | 2025 |
| Volume | 40 |
| Issue | 3 |
| Pages | 270-290 |
| Publication date | 2025-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.3104 |
| OpenAlex | W4406681654 |
| Language | EN |
| References cited | 40 |
Monthly macroeconomic series captured the sharp fluctuations during the COVID‐19 pandemic only with a lag. The use of alternative high‐frequency data is promising for crisis periods, but it is difficult to extract relevant business cycle information from them. We present a Bayesian mixed‐frequency dynamic factor model with stochastic volatility for measuring GDP growth at high‐frequency intervals. Its novelty is an additional state‐space block, in which the sparse observations in the mixed‐frequency data are augmented to a balanced panel with observed and estimated latent information. The dynamic factors are then estimated conditional on the augmented data. Our model exploits the information in rich datasets of weekly, monthly, and quarterly series, including alternative high‐frequency data. GDP is nowcasted timely and accurately during volatile periods
Econometrics · Economics · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Monetary Policy and Economic Impact · Psychology
State-Space Models with Regime Switching
Stochastic Volatility
The Calculation of Posterior Distributions by Data Augmentation
Midas Regressions
Time Varying Structural Vector Autoregressions and Monetary Policy
On Gibbs sampling for state space models
Nowcasting
Determining the Number of Factors in Approximate Factor Models
Nowcasting tail risk to economic activity at a weekly frequency
Regional output growth in the United Kingdom
Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors
Measuring real activity using a weekly economic index
Comparing Predictive Accuracy
Real-Time Measurement of Business Conditions
Addressing Covid-19 Outliers in BVARs with Stochastic Volatility
A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models
| Highly cited | No |
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