Predicting the obvious : a machine learning approach to superstar inventions
Name
1196181684-MIT.pdf
Size
3.87 MB
Format
Adobe PDF
Checksum (MD5)
6035870a37cc228b72c557af226042e7
Author(s)
Raymond, Lindsey Rebecca.
Advisor(s)
Scott Stem.
Alternative Title
Machine learning approach to superstar inventions
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
While patent citations are a common way to measure innovative output, their use as a measure of invention quality involves a paradox. How can we use an ex-post measure of impact (the number of received citations) to identify the ex-ante quality of a given innovation? This paper proposes a novel method of measuring patent quality using patent text and sections of the patent citation distribution with the highest signal to noise ratio. We provide empirical evidence that the bias from using citations to measure quality varies by location in the patent distribution and, contrary to what one might expect, superstar patents are the most predictable while patents in the middle of the distribution are most contaminated with noise. We show predictability of patents increases monotonically over the patent distribution - with the most valuable being the most predictable - and removing the middle of the distribution has little impact on accuracy. We also provide suggestive evidence on the importance of patent text in measuring quality and conclude with suggestive geometric evidence we are capturing differences in underlying patent characteristics. As our model demonstrates, our empirical results generalize to other situations involving highly skewed processes observed with noise. This paper also has implications for empirical work using citation weighted metrics.
Description
Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, September, 2019
Cataloged from PDF of thesis.
Includes bibliographical references (pages 36-41).
Subjects
Sloan School of Management.
MIT Department
Sloan School of Management
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
Persistent DSpace Link
