FDML versus GMM for Dynamic Panel Models with Roots Near Unity
(2021) In Journal of Risk and Financial Management 14(9).- 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.
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https://lup.lub.lu.se/record/21cfae27-aac3-4024-b33a-3eda86a2b222
- author
- Mehic, Adrian LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- dynamic panel data, FDML estimation, persistence
- in
- Journal of Risk and Financial Management
- volume
- 14
- issue
- 9
- article number
- 405
- publisher
- MDPI AG
- external identifiers
-
- scopus:85165731365
- ISSN
- 1911-8066
- DOI
- 10.3390/jrfm14090405
- language
- English
- LU publication?
- yes
- id
- 21cfae27-aac3-4024-b33a-3eda86a2b222
- date added to LUP
- 2023-11-22 15:08:23
- date last changed
- 2023-11-22 15:09:09
@article{21cfae27-aac3-4024-b33a-3eda86a2b222, abstract = {{<p>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.</p>}}, author = {{Mehic, Adrian}}, issn = {{1911-8066}}, keywords = {{dynamic panel data; FDML estimation; persistence}}, language = {{eng}}, number = {{9}}, publisher = {{MDPI AG}}, series = {{Journal of Risk and Financial Management}}, title = {{FDML versus GMM for Dynamic Panel Models with Roots Near Unity}}, url = {{http://dx.doi.org/10.3390/jrfm14090405}}, doi = {{10.3390/jrfm14090405}}, volume = {{14}}, year = {{2021}}, }