Artikel
Bias-correction in vector autoregressive models: A simulation study
We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.
- Language
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Englisch
- Bibliographic citation
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Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 2 ; Year: 2014 ; Issue: 1 ; Pages: 45-71 ; Basel: MDPI
- Classification
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Wirtschaft
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Subject
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bias reduction
VAR model
analytical bias formula
bootstrap
iteration
Yule-Walker
non-stationary system
skewed and fat-tailed data
- Event
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Geistige Schöpfung
- (who)
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Engsted, Tom
Pedersen, Thomas Q.
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2014
- DOI
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doi:10.3390/econometrics2010045
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
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Object type
- Artikel
Associated
- Engsted, Tom
- Pedersen, Thomas Q.
- MDPI
Time of origin
- 2014
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