The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Macroeconomic forecasting and structural change
Abstract
SUMMARY: The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real‐time out‐of‐sample exercise using a time‐varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time‐varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard. Copyright © 2011 John Wiley & Sons, Ltd.
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Cites
Bates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
Cited by
A new index of financial conditions
Koop, G., & Korobilis, D. (2014) · European Economic Review · doi:10.1016/j.euroecorev.2014.07.002 · Cited by 707
Adrian, T., Boyarchenko, N., & Giannone, D. (2019) · American Economic Review · doi:10.1257/aer.20161923 · Cited by 696
Self-citationClark, T. E., & Ravazzolo, F. (2015) · Journal of Applied Econometrics · doi:10.1002/jae.2379 · Cited by 317
Metadata
- Authors
- D'Agostino, A., Gambetti, L., & Giannone, D.
- Venue
- Journal of Applied Econometrics
- Publisher
- Wiley
- Published
- 2011-07-14
- Volume
- 28
- Issue
- 1
- Pages
- 82-101
- ISSN
- 0883-7252 (ISSN-L)
- 1099-1255
- OpenAlex
- W2119327830
- Cited by
- 359
- References
- 1
- FWCI
- 25.14
- Type
- journal article
- Language
- en
- Open access
- closed
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- D'Agostino, A., Gambetti, L., & Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of Applied Econometrics. https://doi.org/10.1002/jae.1257
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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