A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Modeling economies of scope in joint production: Convex regression of input distance function
Tekijät: Kuosmanen, Timo; Dai, Sheng
Kustantaja: Springer
Kustannuspaikka: DORDRECHT
Julkaisuvuosi: 2025
Journal: Journal of Productivity Analysis
Tietokannassa oleva lehden nimi: JOURNAL OF PRODUCTIVITY ANALYSIS
Lehden akronyymi: J PROD ANAL
Vuosikerta: 63
Numero: 1
Aloitussivu: 69
Lopetussivu: 86
Sivujen määrä: 18
ISSN: 0895-562X
eISSN: 1573-0441
DOI: https://doi.org/10.1007/s11123-024-00739-x
Verkko-osoite: https://doi.org/10.1007/s11123-024-00739-x
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/459154642
Preprintin osoite: https://arxiv.org/abs/2311.11637
Modeling of joint production has proved a vexing problem. This paper develops a radial convex nonparametric least squares (CNLS) approach to estimate the input distance function with multiple outputs. We document the correct input distance function transformation and prove that the necessary orthogonality conditions can be satisfied in radial CNLS. A Monte Carlo study is performed to compare the finite sample performance of radial CNLS and other deterministic and stochastic frontier approaches in terms of the input distance function estimation. We apply our novel approach to the Finnish electricity distribution network regulation and empirically confirm that the input isoquants become more curved. In addition, we introduce the weight restriction to radial CNLS to mitigate the potential overfitting and increase the out-of-sample performance in energy regulation.
Julkaisussa olevat rahoitustiedot:
Sheng Dai gratefully acknowledges financial support from the OP Group Research Foundation [grant no. 20230008] and the Turku University Foundation [grant no. 081520].
