Half-panel jackknife estimation for dynamic panel models
(2020) In Economics Letters- Abstract
- This paper extends the half-panel jackknife (HPJ) estimator to GMM models with fixed effects. The Monte Carlo results show that the HPJ significantly reduces finite-sample bias for both the difference and system GMM estimators of the dynamic panel model.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/44be8a3f-d4f7-4e9f-b770-db1605fc17b3
- author
- Mehic, Adrian LU
- organization
- publishing date
- 2020
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dynamic panel data, GMM, Jack knife, C13, C23
- in
- Economics Letters
- article number
- 109082
- publisher
- Elsevier
- external identifiers
-
- scopus:85081983843
- ISSN
- 0165-1765
- DOI
- 10.1016/j.econlet.2020.109082
- language
- English
- LU publication?
- yes
- id
- 44be8a3f-d4f7-4e9f-b770-db1605fc17b3
- date added to LUP
- 2020-03-14 23:04:08
- date last changed
- 2022-04-18 21:19:00
@article{44be8a3f-d4f7-4e9f-b770-db1605fc17b3,
abstract = {{This paper extends the half-panel jackknife (HPJ) estimator to GMM models with fixed effects. The Monte Carlo results show that the HPJ significantly reduces finite-sample bias for both the difference and system GMM estimators of the dynamic panel model.}},
author = {{Mehic, Adrian}},
issn = {{0165-1765}},
keywords = {{Dynamic panel data; GMM; Jack knife; C13; C23}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Economics Letters}},
title = {{Half-panel jackknife estimation for dynamic panel models}},
url = {{http://dx.doi.org/10.1016/j.econlet.2020.109082}},
doi = {{10.1016/j.econlet.2020.109082}},
year = {{2020}},
}