  Accurately modeling the production of new ideas is crucial for innovation
theory and endogenous growth models. This paper provides a comprehensive
methodological survey of strategies for estimating idea production functions.
We explore various methods, including naive approaches, linear regression,
maximum likelihood estimation, and Bayesian inference, each suited to different
data availability settings. Through case studies ranging from total factor
productivity to software R&D, we show how to apply these methodologies in
practice. Our synthesis provides researchers with guidance on strategies for
characterizing idea production functions and highlights obstacles that must be
addressed through further empirical validation.
