This paper introduces pdynmc, an R package that provides users sufficient flexibility and precise control over the estimation and inference in linear dynamic panel data models. The package primarily allows for the inclusion of nonlinear moment conditions and the use of iterated GMM; additionally, visualizations for data structure and estimation results are provided. The current implementation reflects recent developments in literature, uses sensible argument defaults, and aligns commercial and noncommercial estimation commands. Since the understanding of the model assumptions is vital for setting up plausible estimation routines, we provide a broad introduction of linear dynamic panel data models directed towards practitioners before concisely describing the functionality available in pdynmc regarding instrument type, covariate type, estimation methodology, and general configuration. We then demonstrate the functionality by revisiting the popular firm-level dataset of Arellano and Bond (1991).
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For attribution, please cite this work as
Fritsch, et al., "The R Journal: pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions", The R Journal, 2021
BibTeX citation
@article{RJ-2021-035,
author = {Fritsch, Markus and Pua, Andrew Adrian Yu and Schnurbus, Joachim},
title = {The R Journal: pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-035},
doi = {10.32614/RJ-2021-035},
volume = {13},
issue = {1},
issn = {2073-4859},
pages = {218-231}
}