Comparative analysis of two-dimensional data-driven efficient frontier estimation algorithms

Abstract

In this paper we show how the mathematical apparatus developed originally in the field of econometrics and portfolio optimization can be utilized for purposes of conceptual design, requirements engineering and technology roadmapping. We compare popular frontier estimation models and propose an efficient and robust nonparametric estimation algorithm for twodimensional frontier approximation. The proposed model allows to relax the convexity assumptions and thus enable estimating a broader range of possible technology frontier shapes compared to the state of the art. Using simulated datasets we show how the accuracy and the robustness of alternative methods such as Data Envelopment Analysis and nonparametric and parametric statistical models depend on the size of the dataset and on the shape of the frontier.

Model-based approaches for technology planning and roadmapping: Technology forecasting and game-theoretic modeling

“…Previous work in (Yuskevich et al, 2018a) by the authors of this paper has proposed such evolution, framing technology evolution as a multivariate extrapolation of scattered data. Compared to TFDEA, the approach in (Yuskevich et al, 2018a(Yuskevich et al, , 2018b enables the estimation of both increasing and decreasing returns-to-scale frontiers and adopts growth-curves models as proven patterns of technology performances evolution and infusion rates in a single run of the linear optimization procedure. The paper extends this previous work to a general, n-dimensional formulation of the problem.…”