Template-Type: ReDIF-Paper 1.0
Title: Technological interdependencies predict innovation dynamics
Abstract: We propose a simple model where the innovation rate of a technological domain depends on the innovation rate of the technological domains it relies on. Using data on US patents from 1836 to 2017, we make out-of-sample predictions and fond that the predictability of innovation rates can be boosted substantially when network effects are taken into account. In the case where a technology's neighbourhood further innovation rates are known, the average predictability gain is 28% compared to simpler time series model with do not incorporate network effects. Even when nothing is known about the future, we find positive average predictability gains of 20%. The results have important policy implications, suggesting that the effective support of a given technology must take into account the technological ecosystem surrounding the targeted technology.
Author-Name: Pichler, Anton
Author-Name: Lafond, François
Author-Name: Farmer, J. Doyne
File-URL: https://oms-inet.files.svdcdn.com/production/files/Pichler-Lafond-Farmer-Technological-interdependencies-predict-innovation-dynamics.pdf
File-Format: Application/pdf
File-Function: 
Keywords: innovation, technology, network, forecasting, patents, spacial econometrics
Length: 26 pages
Creation-Date: 2020-03
Handle: RePEc:amz:wpaper:2020-04