The frontierProd action estimates the parameters of stochastic frontier production models using maximum likelihood methods. It supports various model types such as exponential, half-normal, and truncated-normal, and allows for the estimation of technical efficiency.
| Parámetro | Descripción |
|---|---|
| bounds | Imposes simple boundary constraints on the parameter estimates. |
| class | Specifies the classification variables. |
| display | Specifies the list of display tables that you want the action to create. |
| freq | Specifies the observation frequency variable. |
| includeinternalnames | When set to True, adds an extra column to the parameter estimates table that shows the internal names for the parameters. |
| initialvalues | Specifies the initial values for parameters. |
| model | Specifies the model definition, including dependent variables, effects, and model options (like type: EXPONENTIAL, HALF, TRUNCATED). |
| optimizer | Specifies parameters that control various aspects of the parameter estimation process, such as the technique (algorithm) and convergence criteria. |
| output | Specifies details for an output data table to contain scores, predicted values, residuals, and technical efficiency estimates. |
| outputTables | Specifies the list of display tables that you want to output as CAS tables. |
| restrictions | Specifies linear restrictions to be imposed on the parameter estimates. |
| table | Specifies the table of data to be analyzed. |
| tests | Specifies linear hypotheses about the regression parameters that are specified in the model and tests to be performed on the hypotheses. |
| weight | Specifies details for the observation weight variable. |
Creates a CAS table with output (log_q) and inputs (log_l, log_k) for production function estimation.
| 1 | DATA mycas.production_data; INPUT firm $ year log_q log_l log_k; DATALINES; |
| 2 | F1 2020 5.2 2.1 3.5 |
| 3 | F1 2021 5.4 2.2 3.6 |
| 4 | F2 2020 4.9 1.9 3.2 |
| 5 | F2 2021 5.1 2.0 3.3 |
| 6 | F3 2020 6.0 2.5 4.0 |
| 7 | F3 2021 6.2 2.6 4.1 |
| 8 | ; RUN; |
Estimates a basic stochastic frontier production model using log inputs.
| 1 | |
| 2 | PROC CAS; |
| 3 | frontier.frontierProd TABLE={name='production_data'} model={depVars={{name='log_q'}}, effects={{vars={'log_l', 'log_k'}}}}; |
| 4 | |
| 5 | RUN; |
| 6 |
Estimates an exponential frontier model, outputs technical efficiency statistics to a table, and uses a specific optimization algorithm.
| 1 | |
| 2 | PROC CAS; |
| 3 | frontier.frontierProd TABLE={name='production_data'} model={depVars={{name='log_q'}}, effects={{vars={'log_l', 'log_k'}}}, modelOptions={type='EXPONENTIAL'}} optimizer={algorithm='NEWTONRAPHSONWITHRIDGING'} OUTPUT={casOut={name='prod_efficiency', replace=true}, te1='TechEff', pred='PredOutput'}; |
| 4 | |
| 5 | RUN; |
| 6 |