Moment Condition Based Estimation of Linear Dynamic Panel Data Models

Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) , Ahn and Schmidt (1995) , and Arellano and Bover (1995) . 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) and doubly corrected standard errors (Hwang, Kang, Lee, 2021 ) 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 ). For a more detailed description of the GMM-based functionality, please see Fritsch, Pua, Schnurbus (2021) . For more details on the IV-based estimation routines, see Fritsch, Pua, and Schnurbus (WP, 2026) and Han and Phillips (2010) .

pdynmc: Dynamic linear panel estimation based on linear and nonlinear moment conditions

Linear dynamic panel data modeling based on linear and nonlinear moment conditions as proposed by Holtz-Eakin, Newey, and Rosen (1988) https://doi.org/10.2307/1913103, Ahn and Schmidt (1995) https://doi.org/10.1016/0304-4076(94)01641-C, and Arellano and Bover (1995) https://doi.org/10.1016/0304-4076(94)01642-D.

Estimation of the model parameters relies on the Generalized Method of Moments (GMM), 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) https://doi.org/10.1016/j.jeconom.2004.02.005 - and doubly corrected standard errors introduced by Hwang, Kang, and Lee (2021) https://doi.org/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 https://doi.org/10.3982/ECTA16274).

See also: https://cran.r-project.org/web/packages/pdynmc/index.html. For further details on the implementation, see Fritsch, Pua, and Schnurbus (2021) https://journal.r-project.org/articles/RJ-2021-035/index.html.

To install the latest development version of the package, please use:

library(devtools)
install_github("markusfritsch/pdynmc")