Comparisons of the some estimators for the transcendental logarithmic (translog) model*

ÖRK ÖZEL S., ÇABUK H. A.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/03610918.2021.1960999
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Anahtar Kelimeler: Multicollinearity, Translog model, Generalized maximum entropy estimator, Restricted generalized maximum entropy estimator, Ridge estimator, RIDGE REGRESSION, DEMAND
  • Çukurova Üniversitesi Adresli: Evet

Özet

Flexible functions in economics are functions that do not require a priority restrictor about various substitution elasticities. One of these functions is Christensen et al. proposed by the transcendental logarithmic (translog) function. Translog model suffers from the multicollinearity problem since the squares are added to the model and cross products of variables. Since classical estimators can not be used under multicollinearity, biased estimators can be used to overcome the problem. In this study; ridge estimator, restricted ridge (RRidge) estimator, generalized maximum entropy (GME) estimator, restricted GME (RGME) estimator, ordinary least squares (OLS) estimator and restricted OLS (ROLS) estimator are compared according to the mean squared error (MSE) criteria. We compare aforementioned estimators with Monte Carlo simulation studies and a numerical example. In conclusion, GME and RGME estimators are decided as the most efficient estimators than rest of estimators in terms of MSE criteria when appropriate support matrices and restrictions are selected.