ssys_gmm: Suboptimal multi-step System-GMM estimator for AR(1) panel...
In pmpp: Posterior Mean Panel Predictor
Description
Usage
Arguments
Value
Author(s)
References
Description
Computes an enhanced version of the Blundell-Bond (System-GMM)
estimator for panel data by means of replacing the standard
GMM-weighting matrix by its sub-optimal version, thus increasing
estimator's efficiency.
Usage
1
Arguments
Y
matrix of size (T x N) with the dependent variable
model
one of: onestep, twosteps, threesteps; more steps should
increase efficiency, but might be computationally infeasible
(a singular matrix needs to be inverted); if this is the case,
generalised inverse is used
Value
The estimated value of the auto-regressive parameter.
Author(s)
Michal Oleszak
References
Youssef, A. and Abonazel, M. (2015). Alternative GMM estimators
for first-order autoregressive panel model: An improving
efficiency approach. MPRA Paper No. 68674; Forthcoming in:
Communications in Statistics - Simulation and Computation,
https://mpra.ub.uni-muenchen.de/68674/1/MPRA_paper_68674.pdf
pmpp documentation built on Oct. 30, 2019, 11:35 a.m.
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Description Usage Arguments Value Author(s) References
Description
Computes an enhanced version of the Blundell-Bond (System-GMM) estimator for panel data by means of replacing the standard GMM-weighting matrix by its sub-optimal version, thus increasing estimator's efficiency.
Usage
1 |
Arguments
Y |
matrix of size (T x N) with the dependent variable |
model |
one of: onestep, twosteps, threesteps; more steps should increase efficiency, but might be computationally infeasible (a singular matrix needs to be inverted); if this is the case, generalised inverse is used |
Value
The estimated value of the auto-regressive parameter.
Author(s)
Michal Oleszak
References
Youssef, A. and Abonazel, M. (2015). Alternative GMM estimators for first-order autoregressive panel model: An improving efficiency approach. MPRA Paper No. 68674; Forthcoming in: Communications in Statistics - Simulation and Computation, https://mpra.ub.uni-muenchen.de/68674/1/MPRA_paper_68674.pdf
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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