pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions

Authors

Markus Fritsch, University of Passau
Andrew Adrian Yu Pua, Xiamen University
Joachim Schnurbus, University of Passau

Date of this Version

6-2021

Document Type

Article

Citation

The R Journal (June 2021) 13(1); Editor: Dianne Cook

Comments

Copyright 2021, The R Foundation. Open access material. License: CC BY 4.0 International

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).