Malmquist Index with Time Series to Data Envelopment Analysis

Abstract

This chapter presents a new temporal data envelopment analysis (DEA) model that overcomes some weaknesses of the window analysis and Malmquist index. New model allows to work with time series. For each series the best of a set of ARIMA models is selected, and a forecast for two periods it is possible. Changes in efficiency of different decision making units (DMUs) are analyzed and the use of temporal series makes it easy to include Malmquist forecasts. The implementation of the new model in business administration or supply chain management can be useful because it considers more than two periods in contrast with classical Malmquist method, for that, control of efficiency over time is improved by changing deterministic univariate variables for time series. The last them have the structure of correlation and they get even more real modeling.

Unbalanced Development – Regional Disparity Analysis in Serbia

“…The frontier shift effect measures the shift (change) in the frontiers of the production possibilities (technological limitations) over time (that is, the changes in technology), and in this paper, it shows if a country applies new information technologies (innovations) or sticks to the existing ones. Further in the text, the MI calculation, the changes in relative efficiency of input use, and the shift in the frontiers of the production possibilities are presented based on the methodological explanation given in Sánchez (2018).…”

“…toSánchez (2018), the numerator and the denominator in the previous equation are both divided by the difference between the point A and the values of the abscise of the point (EMP, GDPpc)2009 , the following relation is obtained:…”

Investigation of efficiency in the UK hotel industry: a network data envelopment analysis approach

“…Another extension of the traditional DEA is the DEA window analysis (Pulina et al, 2010;Huang et al, 2012). This method has the advantage of making it feasible to evaluate and compare the performance of decision-making units (DMUs) in different periods by regarding them as separate entities in different periods (Yang and Chang, 2009); however, it suffers from the limitations that this technique was designed for a short period of time and the random error in the variables was not considered and the dependence structure to estimate the efficiencies were not used (Sanchez, 2018).…”

Hotel Performance in the UK: The Role of Information Entropy in a Novel Slack-Based Data Envelopment Analysis

“…Recently, a number of research articles extended the traditional Data Envelopment Analysis or Stochastic Frontier Analysis by introducing some advanced techniques in estimating the level of efficiency in the hotel industry. This includes the use of (1) Data Envelopment Analysis metafrontier analysis [ 27 ], this method benefits from the advantage of being able to compare the performance between different groups without any ignorance of heterogeneity between them [ 28 ]; however, the existing study suffers from the drawbacks of being unable to integrate the meta-frontier and undesirable output together; (2) Data Envelopment Analysis window analysis [ 29 , 30 ]; this method has the advantage of making it feasible to evaluate and compare the performance of Decision Making Units in different periods through regarding them as a separate entity in different periods [ 31 ], however, Data Envelopment Analysis window analysis suffers from the limitations that this technique was designed for a short period of time and the random error in the variables was not considered and the dependence structure to estimate the efficiencies was not used [ 32 ].…”