Forecasting Macroeconomic Variables Under Model Instability
Bibliographic Data
| ID | 19417992 |
|---|---|
| Authors | Davide Pettenuzzo (0000-0002-5784-7945, Sachar International Center, Brandeis University, Waltham, 02453, MA ()), Allan Timmermann (0000-0002-1555-2268, UCSD, CEPR, and CREATES, University of California, San Diego, La Jolla, 92093, CA ()) |
| Year | 2017 |
| Volume | 35 |
| Issue | 2 |
| Pages | 183-201 |
| Publication date | 2017-04-03 |
| 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 | 10.1080/07350015.2015.1051183 |
| OpenAlex | W3122718189 |
| Language | EN |
| Citations received | 2 |
| References cited | 27 |
We compare different approaches to accounting for parameter instability in the context of macroeconomic forecasting models that assume either small, frequent changes versus models whose parameters exhibit large, rare changes. An empirical out-of-sample forecasting exercise for U.S. gross domestic product (GDP) growth and inflation suggests that models that allow for parameter instability generate more accurate density forecasts than constant-parameter models although they fail to produce better point forecasts. Model combinations deliver similar gains in predictive performance although they fail to improve on the predictive accuracy of the single best model, which is a specification that allows for time-varying parameters and stochastic volatility.
Econometrics · Economics · Instability · Lag · Stochastic volatility · Computer Science · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Monetary Policy and Economic Impact
An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
Large Bayesian vector auto regressions
Forecasting inflation
Why Has U.S. Inflation Become Harder to Forecast?
Time Varying Structural Vector Autoregressions and Monetary Policy
Output Fluctuations in the United States
A Comprehensive Look at The Empirical Performance of Equity Premium Prediction
Bayes Factors
Comparing Density Forecasts via Weighted Likelihood Ratio Tests
Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility
Evidence on Structural Instability in Macroeconomic Time Series Relations
| Citations per year | 0.4 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |