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Comparing predictive accuracy in small samples using fixed-smoothing asymptotic
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Comparing predictive accuracy in small samples usingfixed-smoothing asymptotics
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Publication details
| Journal | Journal of Applied Econometrics |
|---|---|
| Date | Accepted/In press - 19 Jan 2020 |
| Date | E-pub ahead of print - 28 Apr 2020 |
| Date | Published (current) - 1 Jun 2020 |
| Issue number | 4 |
| Volume | 35 |
| Number of pages | 19 |
| Pages (from-to) | 391-405 |
| Early online date | 28/04/20 |
| Original language | English |
Abstract
We consider fixed-smoothing asymptotics for the Diebold and Mariano (1995) test of predictive accuracy. We show that this approach delivers predictive accuracy tests that are correctly sized even when only a small number of out of sample observations is available. We
apply the fixed-smoothing asymptotics to the Diebold and Mariano (1995) test to evaluate the predictive accuracy of the Survey of Professional Forecasters (SPF) and of the ECB Survey of Professional Forecasters (ECB SPF) against a simple random walk. Our results
show that the predictive abilities of the SPF and of the ECB SPF were partially spurious.
apply the fixed-smoothing asymptotics to the Diebold and Mariano (1995) test to evaluate the predictive accuracy of the Survey of Professional Forecasters (SPF) and of the ECB Survey of Professional Forecasters (ECB SPF) against a simple random walk. Our results
show that the predictive abilities of the SPF and of the ECB SPF were partially spurious.
Bibliographical note
© 2020 The Authors
- Diebold and Mariano test, long run variance estimation, fixed-smoothing asymptotics, Heteroskedasticity-Autocorrelation Robust (HAR) inference, SPF
Research areas
Research outputs
Comparing predictive accuracy in small samples
Research output: Working paper › Discussion paper
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ISF2021
Activity: Talk or presentation › Oral presentation
IAAE presentation
Activity: Talk or presentation › Oral presentation
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Modelling government bonds: macroeconomic, financial and international linkages
Project: Research project (funded) › Research
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