scholarly journals Adjustment and unobserved heterogeneity in dynamic stochastic frontier models

2011 ◽  
Vol 37 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Grigorios Emvalomatis
2015 ◽  
Vol 44 (1) ◽  
pp. 124-148 ◽  
Author(s):  
Magnus A. Kellermann

This study examines in an empirical comparison how different econometric specifications of stochastic frontier models affect the decomposition of total factor productivity growth. We estimate nine stochastic frontier models, which have been widely used in empirical investigations of sources of productivity growth. Our results show that the relative contribution of components to total factor productivity growth is quite sensitive to the choice of econometric model, which points to the need to select the “right” model. We apply various statistical tests to narrow the range of applicable models and identify additional criteria upon which to base the choice of non-nested models.


2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Jose M. Gavilan ◽  
Francisco J. Ortega

In the setting of the Stochastic Frontier Production Models, the productive efficiency of the 28 countries belonging to the European Union is analysed. To this end, panel data encompassing a broad period of time is selected, which facilitates the study into whether there is greater efficiency in the years of crisis as a consequence of the adjustment measures. A translog specification of a Cobb-Douglas model is considered, in which the output is measured through the GDP of the countries and two productive factors (capital and labour). The model also includes a trend component that addresses the possible presence of technological change, and dummy variables for each country in order to separate unobserved heterogeneity from productive inefficiency. In relation to the perturbation that models the inefficiency, a model of the type Battese and Coelli (1995) is considered with a trend component and a variable related to economic growth. Finally, the growth of productivity is decomposed into the sum of the changes in technology, in economies of scale, and in inefficiency. 


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