Add to ORKGA bias correction estimator (BCE) for a dynamic panel data model with fixed effects is given, based on the alternating iterative maximum likelihood estimator (AIMLE). The new estimator is asymptotically un-biased and consistent. Monte Carlo studies are conducted to evaluate the finite sample properties of the MLE, AIMLE and BCE. It is shown that the BCE based on AIMLE appears to dominate the AIMLE ap-proach both in terms of the median bias (Bias) and median absolute error (MAE) of the estimators
We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that redu...
This paper is concerned with the estimation of the autoregressive parameter of dynamic panel data mo...
The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed e...
In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias...
A computationally simple bias correction for linear dynamic panel data models is proposed and its as...
In this note we extend the method proposed in Bun and Carree (2006) to the more general PVARX(1) mod...
Fixed effects estimators in nonlinear panel models with fixed and short time series length T usually...
The fixed effects estimator of panel models can be severely biased because of well-known incidental ...
It is well-known that maximum likelihood (ML) estimation of the autoregres-sive parameter of a dynam...
This study extends earlier results on bias-corrected estimators for the fixed-effects dynamic panel ...
This study develops a new bias-corrected estimator for the fixed-effects dynamic panel data model an...
This paper introduces two easy to calculate estimators with desirable properties for theautoregressi...
This article compares the performance of three recently proposed estimators for dynamic panel data m...
The maximum-likelihood estimator of nonlinear panel data models with fixed effects is asymptotically...
Fixed e¤ects estimators in nonlinear panel models with \u85xed and short time series length T usuall...
We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that redu...
This paper is concerned with the estimation of the autoregressive parameter of dynamic panel data mo...
The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed e...
In this article, we describe a new command, xtbcfe, that performs the iterative bootstrap-based bias...
A computationally simple bias correction for linear dynamic panel data models is proposed and its as...
In this note we extend the method proposed in Bun and Carree (2006) to the more general PVARX(1) mod...
Fixed effects estimators in nonlinear panel models with fixed and short time series length T usually...
The fixed effects estimator of panel models can be severely biased because of well-known incidental ...
It is well-known that maximum likelihood (ML) estimation of the autoregres-sive parameter of a dynam...
This study extends earlier results on bias-corrected estimators for the fixed-effects dynamic panel ...
This study develops a new bias-corrected estimator for the fixed-effects dynamic panel data model an...
This paper introduces two easy to calculate estimators with desirable properties for theautoregressi...
This article compares the performance of three recently proposed estimators for dynamic panel data m...
The maximum-likelihood estimator of nonlinear panel data models with fixed effects is asymptotically...
Fixed e¤ects estimators in nonlinear panel models with \u85xed and short time series length T usuall...
We propose jackknife estimators for nonlinear dynamic panel data models with fixed effects that redu...
This paper is concerned with the estimation of the autoregressive parameter of dynamic panel data mo...
The purpose of this paper is to review recently developed methods of estimation of nonlinear fixed e...