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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 2 ; Year: 2014 ; Issue: 1 ; Pages: 45-71 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
Estimation: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Thema
-
bias reduction
VAR model
analytical bias formula
bootstrap
iteration
Yule-Walker
non-stationary system
skewed and fat-tailed data
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Engsted, Tom
Pedersen, Thomas Q.
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2014
- DOI
-
doi:10.3390/econometrics2010045
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Engsted, Tom
- Pedersen, Thomas Q.
- MDPI
Entstanden
- 2014
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