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This open-access book explores macroeconomic modelling of R&D and innovation policies, presenting cutting-edge contributions from leading economists and providing a comprehensive overview of challenges and techniques. It highlights the importance of R&D and innovation policy and the rise of endogenous technological change in macroeconomic models.

Format: Paperback / softback
Length: 212 pages
Publication date: 04 November 2021
Publisher: Springer Nature Switzerland AG

This open-access book is a comprehensive collection of in-depth analyses that explore the challenges and opportunities in macroeconomic modelling of R&D and innovation policies. Drawing from the proceedings of the EC-DG JRC-IEA workshop held in Brussels in 2017, it presents cutting-edge contributions from renowned economists in the field. The book offers a comprehensive overview of the current academic and policy challenges surrounding R&D, as well as the state-of-the-art modelling techniques employed to evaluate its macroeconomic impact. It highlights the rising importance of R&D and innovation policies in today's global economy, and the proliferation of macroeconomic models that incorporate endogenous technological change. The contents of this book are of interest to both academic and policy audiences engaged in research and development, as well as innovation policy.

Introduction:
The field of macroeconomic modelling has witnessed significant advancements in recent years, driven by the growing importance of R&D and innovation policies in shaping economic growth and development. Macroeconomic models are crucial tools for policymakers to assess the impact of these policies on the overall economy, and to identify potential policy interventions to enhance their effectiveness.

Challenges in Macroeconomic Modelling of R&D and Innovation Policies:
Macroeconomic modelling of R&D and innovation policies presents several challenges. Firstly, R&D is a complex and multifaceted phenomenon that involves multiple actors, institutions, and processes. It is challenging to capture the full range of interactions and feedbacks that occur within the R&D ecosystem, and to incorporate these into macroeconomic models. Secondly, the impact of R&D on economic growth is not immediate, and it often takes several years for new technologies and innovations to diffuse and impact the economy. This delay makes it difficult to predict the long-term effects of R&D policies and to evaluate their effectiveness in a timely manner.

Ways Forward:
To address these challenges, there are several ways forward. Firstly, researchers can develop more sophisticated and detailed macroeconomic models that incorporate the specific dynamics of the R&D ecosystem. This can involve developing new modelling techniques, such as agent-based modelling or dynamic stochastic general equilibrium models, that can capture the complex interactions and feedbacks between different actors in the R&D process. Secondly, policymakers can collaborate with researchers to improve the data and information available for modelling R&D and innovation policies. This can involve collecting more detailed data on R&D investments, innovation outputs, and their economic impact, as well as developing new indicators and metrics to measure the effectiveness of R&D policies.

State-of-the-Art Modelling Techniques:
In recent years, there have been significant developments in modelling techniques that can help evaluate the macroeconomic impact of R&D and innovation policies. These include:

• Agent-Based Modelling: Agent-based modelling is a simulation-based approach that involves the creation of virtual agents that represent different actors in the R&D ecosystem, such as researchers, firms, and policymakers. These agents can interact with each other and with the environment, and can be used to simulate the dynamics of the R&D process and its impact on the economy.
  


• Dynamic Stochastic General Equilibrium Models: Dynamic stochastic general equilibrium models are a type of macroeconomic model that incorporates both stochastic dynamics and general equilibrium principles. These models can capture the complex interactions and feedbacks between different sectors of the economy, and can be used to evaluate the impact of R&D and innovation policies on economic growth and employment.
  


• Big Data Analytics: Big data analytics involves the use of advanced statistical and machine learning techniques to analyze large and complex datasets. These techniques can help identify patterns and trends in R&D investments, innovation outputs, and their economic impact, and can be used to inform policy decisions.
  

Conclusion:
Macroeconomic modelling of R&D and innovation policies is a complex and challenging field, but it is essential for policymakers to have accurate and reliable estimates of the impact of these policies on the economy. By developing more sophisticated and detailed models, collaborating with researchers to improve the data and information available, and employing state-of-the-art modelling techniques, policymakers can better understand the challenges and opportunities in R&D and innovation policy, and can develop more effective policies to promote economic growth and development.

Weight: 313g
Dimension: 210 x 148 (mm)
ISBN-13: 9783030714567
Edition number: 1st ed. 2022