Half-panel jackknife estimation of GMM models with fixed effects
(2018) NEKN01 20181Department of Economics
- Abstract
- In empirical economics, the generalized method of moments (GMM) is one of the most widely used methods for estimating models with fixed effects, such as the dynamic panel model. For the dynamic panel model, the two most widely used GMM-based estimators are the so-called difference GMM and system GMM estimators. However, it is typically the case that such estimators are asymptotically biased of order N^-1. To remedy this problem, this thesis extends the half-panel jackknife (HPJ) estimator of Dhaene and Jochmans (2015) to GMM models with fixed effects and O(N^-1) bias. In theory, this should reduce asymptotic bias from O(N^−1) to O(N^−2). The Monte Carlo results show that the HPJ gives satisfactory finite-sample bias improvements only for the... (More)
- In empirical economics, the generalized method of moments (GMM) is one of the most widely used methods for estimating models with fixed effects, such as the dynamic panel model. For the dynamic panel model, the two most widely used GMM-based estimators are the so-called difference GMM and system GMM estimators. However, it is typically the case that such estimators are asymptotically biased of order N^-1. To remedy this problem, this thesis extends the half-panel jackknife (HPJ) estimator of Dhaene and Jochmans (2015) to GMM models with fixed effects and O(N^-1) bias. In theory, this should reduce asymptotic bias from O(N^−1) to O(N^−2). The Monte Carlo results show that the HPJ gives satisfactory finite-sample bias improvements only for the difference GMM. For the system GMM, using the HPJ results in bias reductions only under very special circumstances. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8957670
- author
- Mehic, Adrian LU
- supervisor
- organization
- course
- NEKN01 20181
- year
- 2018
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- econometrics, dynamic panel data, jackknife
- language
- English
- id
- 8957670
- date added to LUP
- 2018-09-26 10:47:11
- date last changed
- 2018-09-26 10:47:11
@misc{8957670, abstract = {{In empirical economics, the generalized method of moments (GMM) is one of the most widely used methods for estimating models with fixed effects, such as the dynamic panel model. For the dynamic panel model, the two most widely used GMM-based estimators are the so-called difference GMM and system GMM estimators. However, it is typically the case that such estimators are asymptotically biased of order N^-1. To remedy this problem, this thesis extends the half-panel jackknife (HPJ) estimator of Dhaene and Jochmans (2015) to GMM models with fixed effects and O(N^-1) bias. In theory, this should reduce asymptotic bias from O(N^−1) to O(N^−2). The Monte Carlo results show that the HPJ gives satisfactory finite-sample bias improvements only for the difference GMM. For the system GMM, using the HPJ results in bias reductions only under very special circumstances.}}, author = {{Mehic, Adrian}}, language = {{eng}}, note = {{Student Paper}}, title = {{Half-panel jackknife estimation of GMM models with fixed effects}}, year = {{2018}}, }