R&D-based Calibrated Growth Models with Finite-Length Patents: A Novel Relaxation Algorithm for Solving an Autonomous FDE System of Mixed Type
Hwan Lin and
L. F. Shampine ()
Additional contact information
L. F. Shampine: Southern Methodist University
Computational Economics, 2018, vol. 51, issue 1, 123-158
Abstract The statutory patent length is 20 years in most countries. R&D-based growth models, however, often presume an infinite patent length. In this paper, finite-length patents are embedded in a non-scale R&D-based growth model, while allowing any patent’s effective life to be terminated prematurely, subject to two idiosyncratic hazards from imitation and creative destruction. This gives rise to an autonomous system of mixed-type functional differential equations (FDEs) that had never been encountered in the growth literature. Its dynamics are driven by current, delayed and advanced states. We present a relaxation algorithm to solve these FDEs by solving a sequence of standard boundary value problems for systems of ordinary differential equations. We use this algorithm to simulate a calibrated U.S. economy’s transitional dynamics by making discrete changes from the baseline 20 years patent length. We find that if transitional impacts are taken into account, the switch to the long-run optimal patent length can incur a welfare loss, albeit rather small.
Keywords: Patent length; Innovation; Delay differential equation; Advance differential equation; Dynamics; Endogenous growth; Relaxation algorithm (search for similar items in EconPapers)
JEL-codes: C63 O31 O34 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s10614-016-9597-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:51:y:2018:i:1:d:10.1007_s10614-016-9597-9
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla ().