Regime-switching empirical similarity model
A comparison with baseline models
Bibliographic Data
| ID | 21542124 |
|---|---|
| Authors | Jamol Bahromov (0000-0002-5618-5126, Ruhr University Bochum, corresponding author) |
| Year | 2022 |
| Volume | 63 |
| Issue | 5 |
| Pages | 2655-2674 |
| Publication date | 2022-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Empirical Economics (JOURNAL) |
| Journal identifiers | ISSN: 0377-7332 • E-ISSN: 1435-8921 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00181-022-02214-8 |
| PMID | 35194304 |
| OpenAlex | W4213372091 |
| Language | EN |
| References cited | 16 |
In this paper, I extend the standard specification of the empirical similarity (ES) model of Gilboa et al. (Rev Econ Stat 88:433–444, 2006) to account for changes in parameters. I implement this by allowing for a combination of component ES models in the spirit of Gaussian mixture models. The predictive power of the modified model, along with that of the standard specification, will be assessed and compared to the baseline models consisting of autoregressions and Markov-switching autoregressions within a simulation exercise. Finally, we also compare the predictive ability of models using data on quarterly US real GDP growth. The results indicate that in situations of a more complex regime-switching behavior and a moderate to high autocorrelation in series, modified ES model demonstrates a better empirical fit. In addition, results of the empirical example show that modified ES models can better predict more extreme observations
Autocorrelation · Econometrics · Markov chain · Mixture model · Predictive power · Statistics · Computer Science · Economic Policies and Impacts · Economic theories and models · Mathematics · Monetary Policy and Economic Impact · Artificial Intelligence
| Highly cited | No |
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