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

Laura Magazzini

Chapter Chapter 14 in Applied Econometric Analysis Using Cross Section and Panel Data, 2023, pp 415-444 from Springer

Abstract: 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.

Keywords: Linear panel data; Dynamic modeling; GMM; Maximum likelihood; Bias-corrected methods (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-981-99-4902-1_14

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DOI: 10.1007/978-981-99-4902-1_14

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