The R Journal: article published in 2021, volume 13:1

pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions
Markus Fritsch, Andrew Adrian Yu Pua and Joachim Schnurbus , The R Journal (2021) 13:1, pages 218-231.

Abstract 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).

Received: 2020-06-03; online 2021-06-07
CRAN packages: pdynmc, OrthoPanels, plm, panelvar, optimx
CRAN Task Views implied by cited CRAN packages: Econometrics, Optimization, SpatioTemporal

This article is licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-035,
  author = {Markus Fritsch and Andrew Adrian Yu Pua and Joachim
          Schnurbus},
  title = {{pdynmc: A Package for Estimating Linear Dynamic Panel Data
          Models Based on Nonlinear Moment Conditions}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-035},
  url = {https://doi.org/10.32614/RJ-2021-035},
  pages = {218--231},
  volume = {13},
  number = {1}
}