Time envelopment analysis: A new method for effectively incorporating time series in data envelopment analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F25%3A10257595" target="_blank" >RIV/61989100:27510/25:10257595 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0957417425014137?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0957417425014137?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.eswa.2025.127791" target="_blank" >10.1016/j.eswa.2025.127791</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Time envelopment analysis: A new method for effectively incorporating time series in data envelopment analysis

Popis výsledku v původním jazyce

Data envelopment analysis (DEA) is a non-parametric tool for empirically evaluating the relative efficiency of homogeneous organizational units, i.e., decision-making units, by estimating the production frontiers. Time series analysis is a statistical technique that considers a series of data collected chronologically over time intervals. This study introduces a three-stage method, Time Envelopment Analysis (TEA), to effectively integrate time series analysis into DEA. The three-stage method includes a first-order autoregressive (AR(1)) model followed by DEA and ordinary least squares (OLS). The performance of the TEA method with four different values for the AR(1) parameters is compared with the DEA-OLS procedure using extensive Monte Carlo simulations. The simulation results show that the TEA method outperforms the DEA-OLS procedure. We further demonstrate that TEA is more accurate when the autoregressive parameter is smaller, particularly in scenarios defined by a progressive decrease in the impact of technical inefficiencies. We evaluate the proposed TEA method using a realworld healthcare dataset from 63 countries by estimating the effect of contextual variables on each country’s productivity.

Název v anglickém jazyce

Time envelopment analysis: A new method for effectively incorporating time series in data envelopment analysis

Popis výsledku anglicky

Data envelopment analysis (DEA) is a non-parametric tool for empirically evaluating the relative efficiency of homogeneous organizational units, i.e., decision-making units, by estimating the production frontiers. Time series analysis is a statistical technique that considers a series of data collected chronologically over time intervals. This study introduces a three-stage method, Time Envelopment Analysis (TEA), to effectively integrate time series analysis into DEA. The three-stage method includes a first-order autoregressive (AR(1)) model followed by DEA and ordinary least squares (OLS). The performance of the TEA method with four different values for the AR(1) parameters is compared with the DEA-OLS procedure using extensive Monte Carlo simulations. The simulation results show that the TEA method outperforms the DEA-OLS procedure. We further demonstrate that TEA is more accurate when the autoregressive parameter is smaller, particularly in scenarios defined by a progressive decrease in the impact of technical inefficiencies. We evaluate the proposed TEA method using a realworld healthcare dataset from 63 countries by estimating the effect of contextual variables on each country’s productivity.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50200 - Economics and Business

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

282

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

15

Strana od-do

127791

Kód UT WoS článku

001482119700001

EID výsledku v databázi Scopus

2-s2.0-105003567120