Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258509 
Authors: 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 14 [Issue:] 9 [Article No.:] 405 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-9
Publisher: 
MDPI, Basel
Abstract: 
This paper evaluates the first-differenced maximum likelihood (FDML) and the continuously updating system generalized method of moments (CU-GMM) estimators of dynamic panel models when the data is close to non-stationary. This case is far from trivial, as a high degree of persistence is the norm rather than the exception in economic panels, particularly in financial management. While the CU-GMM is shown to have lower bias and higher power, it suffers from severe size distortions, which are exacerbated when the data approaches non-stationarity.
Subjects: 
dynamic panel data
FDML estimation
persistence
JEL: 
C13
C23
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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