TEMEL BİLEŞENLER ANALİZİ İLE VZA MODELLERİNİN SEÇİLMESİ VE BİRİMLERİN SIRALANMASI: ŞEHİRLERİN EKONOMİK PERFORMANSI ÜZERİNE BİR UYGULAMA

Veri Zarflama Analizi, karar verme birimlerinin göreli etkinliklerinin ölçen doğrusal programlamaya dayalı bir parametrik olmayan yöntemdir. Bu yöntem çeşitli girdilerle bazı çıktıları üreten üniversiteler, hastaneler ve bankalar gibi homojen karar verme birimlerinin etkinliklerinin değerlendirilmesi sıklıkla kullanılmaktadır. Etkinlik skorlarının hesaplanmasında seçilen girdi ve çıktıların oldukça büyük öneme sahiptir. Bu yüzden doğru girdi ve çıktıları seçmek için literatürde veri zarflama analizinin çok farklı modelleri bulunmaktadır. Bu çalışmada 28 şehre ait veriler kullanılarak ekonomik performanslar hesaplanmıştır. Olası bütün veri zarflama analizi modelleri etkinlik skorları hesaplanarak sonuçlar Temel Bileşen Analizi kullanılarak analiz edilmiştir.

Anahtar Kelimeler:

Data Envelopment Analysis, Principal Component Analysis, Economic Performance

SELECTING DEA MODEL SPECIFICATIONS AND RANKING UNITS VIA PRINCIPAL COMPONENT ANALYSIS: A APPLICATION OF ECONOMIC PERFORMANCE OF CITIES

Data Envelopment Analysis (DEA) is non-parametric mathematical tool a linear programming-based approach for measuring the relative efficiency of decision makin gunits (DMUs). DEA is becoming widely used to evaluate the efficiency of organizations with multiple homogeneous DMUs such as universities, hospitals, and banks that produce several outputs with a variety of inputs. Different model selection methods have been suggested for DEA in the literature. Model selection in DEA is a very important problem. Efficiency score of DMU takes different values based on input and output. Variable selection is crucial to the process as the omission of some of the inputs can have a large effect on efficiency score. In this study, an example deals with the efficiency in the economic performance of 28 Chinese cities. Efficiency scores are calculated for all possible DEA model specifications. The results are analyzed using Principal Component Analysis and a new method for model selection is proposed in this paper.

Keywords:

Data Envelopment Analysis, Principal Component Analysis,

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