Moment Condition Based Estimation of Linear Dynamic Panel Data Models
Extract Weighting Matrix of Fitted Model.
Extract Input Parameters of Numeric Optimization of Fitted Model.
Extract Input Parameters of Numeric Optimization of Fitted Model.
Case and Variable Names of Fitted Model.
Extract Coefficient Estimates of Fitted Model.
Show Basic Structure of Panel Dataset.
Extract Coefficient Estimates of Time Dummies of Fitted Model.
First Difference Least Squares (FDLS) Estimator of Han and Phillips (2...
Extract Fitted Values of Fitted Model.
Hansen J-Test.
Extract Instrument Matrix of Fitted Model.
Arellano and Bond Serial Correlation Test.
pdynmc: A package for moment conditions based estimation of linear dyn...
Extract Instrument Count of Fitted Model.
Extract Instrument Count of Fitted Model.
Nonlinear Instrumental Variables Estimator - t-Version (NLIV.alt).
Nonlinear Instrumental Variables Estimator - T-Version (NLIV).
Extract Number of Observations of Fitted Model.
Plot Empirical Density of a Column of a Panel Dataset over Time.
Generalized Method of Moments (GMM) Estimation of Linear Dynamic Panel...
Plot Coefficient Estimates and Corresponding Ranges of Fitted Model.
Print Fitted Model Object.
Print Summary of Fitted Model Object.
Extract Residuals of Fitted Model.
Plot on Structure of Unbalanced Panel Dataset.
Summary for Fitted Model Object.
Extract Names of Explanatory Variables of Fitted Model.
Extract Variance Covariance Matrix of Fitted Model.
Wald Test.
Extract Weighting Matrix of Fitted Model.
Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>, Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>, and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>. Estimation of the model parameters relies on the Generalized Method of Moments (GMM) and instrumental variables (IV) estimation, numerical optimization (when nonlinear moment conditions are employed) and the computation of closed form solutions (when estimation is based on linear moment conditions). One-step, two-step and iterated estimation is available. For inference and specification testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005> and doubly corrected standard errors (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>) are available. Additionally, serial correlation tests, tests for overidentification, and Wald tests are provided. Functions for visualizing panel data structures and modeling results obtained from GMM estimation are also available. The plot methods include functions to plot unbalanced panel structure, coefficient ranges and coefficient paths across GMM iterations (the latter is implemented according to the plot shown in Hansen and Lee, 2021 <doi:10.3982/ECTA16274>). For a more detailed description of the GMM-based functionality, please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>. For more detail on the IV-based estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2024) and Han and Phillips (2010) <doi:10.1017/S026646660909063X>.