This thesis estimates the relative productive efficiency of Turkish wind farms to discover their inefficiency reasons using a two-stage Data Envelopment Analysis (DEA). We choose three input variables and two output variables to conduct 4 different DEA models including input- and output- oriented CCR (Charnes, Cooper, Rhodes) and BCC (Banker, Charnes, Cooper) models. Sensitivity analysis is applied to DEA results to ensure the stability and robustness of the four models. In the second stage Tobit regression models are utilized to explore the exogenous factor that affect the efficiency of Turkish wind farm. DEA results indicate that 40% of Turkish wind farms were operating at preferable levels during 2017. Moreover, 42% of the wind farms should increase their operation levels by adding new installations, and 46% should decrease their capacity due to overinvestments. The sensitivity analysis confirms the robustness of DEA models in this thesis and reveals that amount of electricity generation as an output has substantial impact on the DEA results. Finally, Tobit regression results indicate age and site elevation do not have significant effect on the efficiency of Turkish wind farms. Furthermore, using Tobit regression, we discovered that Chinese and Indian made turbines have negative effect on the performance of Turkish wind farms.