FDH: Output orientation¶
The FDH estimator was first proposed by Deprins et al. (1984), and the mixed integer linear program (MILP) formulation of FDH was introduced by Tulkens (1993).
Given the input variables \(X = [x_1, x_2, \cdots, x_n]\) and output variables \(Y = [y_1, y_2, \cdots, y_n]\), we measure the output oriented efficiency for observation \(i\) by solving the following MILP problem:
\begin{align*}
\underset{\mathbf{\phi},\mathbf{\lambda}}max \quad \phi_i \\
\mbox{s.t.} \quad
X\lambda \le x_i \\
Y\lambda \ge y_i \phi_i \\
\sum \lambda = 1 \\
\lambda_j \in \{0, 1\}, \forall j
\end{align*}
where \(\lambda = [\lambda_1, \lambda_2, \cdots, \lambda_n]\) is the vector of intensity weights. The efficiency of observation \(i\) is \(\phi^*_i\). The corresponding calculation processes are as follow:
Example: Output oriented FDH [.ipynb]¶
# import packages from pystoned import FDH from pystoned import dataset as dataset from pystoned.constant import ORIENT_OO, OPT_LOCAL # import the data provided with Tim Coelli’s Frontier 4.1 data = dataset.load_Tim_Coelli_frontier() # define and solve the FDH model model = FDH.FDH(data.y, data.x, orient=ORIENT_OO, yref=None, xref=None) model.optimize(OPT_LOCAL) # display the technical efficiency model.display_theta() # display the intensity variables model.display_lamda()