The HPQLIM Procedure

Stochastic Frontier Production and Cost Models

Subsections:

Stochastic frontier production models were first developed by Aigner, Lovell, and Schmidt (1977); Meeusen and van den Broeck (1977). Specification of these models allow for random shocks of the production or cost but also include a term for technical or cost inefficiency. Assuming that the production function takes a log-linear Cobb-Douglas form, the stochastic frontier production model can be written as

\[ ln({y_ i}) = \beta _0+\sum _{n} \bbeta _ n\ln (x_{ni})+\epsilon _ i \]

where $\epsilon _ i=v_ i-u_ i$. The $v_ i$ term represents the stochastic error component, and the $u_ i$ term represents the nonnegative, technical inefficiency error component. The $v_ i$ error component is assumed to be distributed iid normal and independent from $u_ i$. If $u_ i>0$, the error term $\epsilon _ i$ is negatively skewed and represents technical inefficiency. If $u_ i<0$, the error term $\epsilon _ i$ is positively skewed and represents cost inefficiency. PROC HPQLIM models the $u_ i$ error component as a half-normal, exponential, or truncated normal distribution.

The Normal-Half-Normal Model

When $v_ i$ is iid $N(0,\sigma _ v^2)$ in a normal-half-normal model, $u_ i$ is iid $N^+(0,\sigma _ u^2)$, with $v_ i$ and $u_ i$ independent of each other. Given the independence of error terms, the joint density of v and u can be written as

\[ f(u,v) = \frac{2}{2\pi \sigma _ u\sigma _ v} \exp \left\{ -\frac{u^2}{2\sigma _ u^2} - \frac{v^2}{2\sigma _ v^2} \right\} \]

Substituting $v=\epsilon +u$ into the preceding equation and integrating u out gives

\begin{eqnarray*} f(\epsilon ) & = & \frac{2}{\sigma }\phi \left( \frac{\epsilon }{\sigma } \right) \Phi \left( -\frac{\epsilon \lambda }{\sigma } \right) \end{eqnarray*}

where $\lambda =\sigma _ u/\sigma _ v$ and $\sigma =\sqrt {\sigma _ u^2+\sigma _ v^2}$.

In the case of a stochastic frontier cost model, $v=\epsilon -u$ and

\[ f(\epsilon ) = \frac{2}{\sigma }\phi \left( \frac{\epsilon }{\sigma } \right) \Phi \left( \frac{\epsilon \lambda }{\sigma } \right) \]

For more information, see the section Stochastic Frontier Production and Cost Models in SAS/ETS 14.1 User's Guide.

The Normal-Exponential Model

Under the normal-exponential model, $v_ i$ is iid $N(0,\sigma _ v^2)$ and $u_ i$ is iid exponential. Given the independence of error term components $u_ i$ and $v_ i$, the joint density of v and u can be written as

\[ f(u,v) = \frac{1}{\sqrt {2\pi }\sigma _ u\sigma _ v} \exp \left\{ -\frac{u}{\sigma _ u} - \frac{v^2}{2\sigma _ v^2} \right\} \]

The marginal density function of $\epsilon $ for the production function is

\begin{eqnarray*} f(\epsilon ) & = & \left( \frac{1}{\sigma _ u} \right) \Phi \left( -\frac{\epsilon }{\sigma _ v}-\frac{\sigma _ v}{\sigma _ u} \right) \exp \left\{ \frac{\epsilon }{\sigma _ u}+\frac{\sigma _ v^2}{2\sigma _ u^2} \right\} \end{eqnarray*}

The marginal density function for the cost function is equal to

\[ f(\epsilon ) = \left( \frac{1}{\sigma _ u} \right) \Phi \left( \frac{\epsilon }{\sigma _ v}-\frac{\sigma _ v}{\sigma _ u} \right) \exp \left\{ -\frac{\epsilon }{\sigma _ u}+\frac{\sigma _ v^2}{2\sigma _ u^2} \right\} \]

For more information, see the section Stochastic Frontier Production and Cost Models in SAS/ETS 14.1 User's Guide.

The Normal–Truncated Normal Model

The normal–truncated normal model is a generalization of the normal-half-normal model that allows the mean of $u_ i$ to differ from zero. Under the normal–truncated normal model, the error term component $v_ i$ is iid $N^+(0,\sigma _ v^2)$ and $u_ i$ is iid $N(\mu ,\sigma _ u^2)$. The joint density of $v_ i$ and $u_ i$ can be written as

\[ f(u,v) = \frac{1}{\sqrt { 2\pi }\sigma _ u\sigma _ v\Phi \left( \mu /\sigma _ u \right) } \exp \left\{ -\frac{(u-\mu )^2}{2\sigma _ u^2}-\frac{v^2}{2\sigma _ v^2} \right\} \]

The marginal density function of $\epsilon $ for the production function is

\begin{eqnarray*} f(\epsilon ) & = & \frac{1}{\sigma }\phi \left( \frac{\epsilon +\mu }{\sigma } \right) \Phi \left( \frac{\mu }{\sigma \lambda }-\frac{\epsilon \lambda }{\sigma } \right) \left[ \Phi \left( \frac{\mu }{\sigma _ u} \right) \right]^{-1} \end{eqnarray*}

The marginal density function for the cost function is

\begin{eqnarray*} f(\epsilon ) & = & \frac{1}{\sigma }\phi \left( \frac{\epsilon -\mu }{\sigma } \right) \Phi \left( \frac{\mu }{\sigma \lambda }+\frac{\epsilon \lambda }{\sigma } \right) \left[ \Phi \left( \frac{\mu }{\sigma _ u} \right) \right]^{-1} \end{eqnarray*}

For more information, see the section Stochastic Frontier Production and Cost Models in SAS/ETS 14.1 User's Guide.

For more information about normal-half-normal, normal-exponential, and normal–truncated normal models, see Kumbhakar and Lovell (2000); Coelli, Prasada Rao, and Battese (1998).