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Linear Dynamic Panel Data Models: Exploring the Patent-R&D Relationship in Europe

In: Applied Econometric Analysis Using Cross Section and Panel Data

Author

Listed:
  • Laura Magazzini

    (Sant’ Anna School of Advanced Studies)

Abstract

This chapter discusses the econometric tools for the empirical analysis of linear dynamic panel data (DPD) models. Many economic relationships are dynamic in nature, and, as a major advantage, panel data allow modeling the evolution of economic phenomena at the microeconomic level. Operationally, this is accomplished by including the lag(s) of the dependent variable on the right-hand side of the estimated equation. As a result, ordinary least squares, and panel fixed effect methods are inconsistent. Even though biased, the estimators are a useful benchmark for assessing the small sample properties of available estimation procedures. Among these, empirical analysis largely relies on the generalized method of moments (GMM). Estimation procedures for DPD models other than GMM, including maximum likelihood and bias-corrected estimation, are also discussed. Theoretical developments are complemented by the empirical analysis of a simplified regional knowledge production function in Europe, aimed at helping students in understanding the proposed estimators. The example is developed using Stata and R.

Suggested Citation

  • Laura Magazzini, 2023. "Linear Dynamic Panel Data Models: Exploring the Patent-R&D Relationship in Europe," Contributions to Economics, in: Deep Mukherjee (ed.), Applied Econometric Analysis Using Cross Section and Panel Data, chapter 0, pages 415-444, Springer.
    • Handle: RePEc:spr:conchp:978-981-99-4902-1_14
    DOI: 10.1007/978-981-99-4902-1_14
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