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Half-panel jackknife estimation for dynamic panel models

Mehic, Adrian LU (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:
author
organization
publishing date
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
2025-10-14 11:13:27
@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}},
}