The commonly used 1-step and 2-step System GMM estimators for the panel AR(1) model are inconsistent under mean stationarity when the ratio of the variance of the individual effects to the variance of the idiosyncratic errors is unbounded when N→∞. The reason for their inconsistency is ...
摘要: In this chapter we discusses methods of dynamic panel data estimation. It is well known that the use of the lagged dependent variable as a right hand side variable introduces specific estimation problems, especially the fixed effects estimator becoming biased....
Dynamic panel-data estimation Number of obs = 751 Group variable: id Number of groups = 140 Time variable: year Obs per group: min = 5 avg = 5.364286 max = 7 Number of instruments = 47 Wald chi2(13) = 2579.96 Prob > chi2 = 0.0000 One-step results (Std. err. adjusted for cluster...
Bond (1998) "Dynamic Panel Data Estimation using DPD98 for GAUSS," mimeo. Bartelsman, Eric J. and Mark Doms (2000) "Understanding Productivity: ... K Amess - 《Scottish Journal of Political Economy》 被引量: 74发表: 2010年 Induced road traffic in Spanish regions: A dynamic panel data ...
Title xtdpd — Linear dynamic panel-data estimation stata.com Description Options Acknowledgment Quick start Remarks and examples References Menu Stored results Also see Syntax Methods and formulas Description xtdpd fits a linear dynamic panel-data model where the unobserved panel-level effects are ...
MLtime-varying parameterIn this paper, we consider dynamic panel data models where the autoregressive parameter changes over time. We propose the GMM and ML estimators for this model. We conduct Monte Carlo simulation to compare the performance of these two estimators. The simulation results show ...
First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling because differencing eliminates fixed effects and, in the case of a unit root, differencing transforms the data to stationarity, thereby addressing both incidental parameter problems and the...
Quasi–maximum likelihood estimation of linear dynamic short-T panel-data models In this article, I describe the xtdpdqml command for the quasi– maximum likelihood estimation of linear dynamic panel-data models when the time horizon is short and the number of cross-sectional units is large. Base...
aUsing a dynamic panel data model and employing the GMM method of estimation we control for unobservable heterogeneity and for potential endogeneity problems. The results reveal that firms have a target level of accounts receivable and take decisions in order to achieve that level. In addition, we...
Table 6. Dynamic panel-data estimation: two-step system GMM. Dep. Var: TFPCoefficientStd. ErrorTP>|t|[95% Conf. Interval] lag1_ltfp 0.9204*** 0.1016 9.06 0.000 0.7119356 1.128768 Lfo 0.1090 0.0825 1.32 0.197 −0.0601982 0.2782748 lins 1.7377* 0.9865 1.76 0.089 −0.2864838 3.761884 lins2...