The frontierCost action analyzes stochastic frontier cost models. It estimates the cost efficiency of decision-making units by formulating a cost function that accounts for both random error and non-negative inefficiency components. This action is essential for benchmarking performance and identifying deviations from the optimal cost frontier given input prices and output levels.
| Parameter | Description |
|---|---|
| table | Specifies the input data table to be analyzed. This is a required parameter. |
| model | Specifies the dependent variable, explanatory effects (regressors), and specific model options (like efficiency type). This is a required parameter. |
| class | Specifies the classification variables to be used in the analysis. |
| bounds | Imposes simple boundary constraints on the parameter estimates. |
| optimizer | Controls the nonlinear optimization process, including the algorithm (e.g., Newton-Raphson, Conjugate Gradient) and convergence criteria. |
| output | Specifies details for the output data table, which can contain predicted values, residuals, and technical efficiency estimates (te1, te2). |
| restrictions | Specifies linear restrictions to be imposed on the parameter estimates. |
| tests | Specifies linear hypotheses about the regression parameters to be tested (Wald, Lagrange Multiplier, Likelihood Ratio). |
| weight | Specifies the variable to be used as the observation weight. |
| freq | Specifies the variable to be used as the observation frequency. |
Generates a dataset simulating cost, output, and input prices for 100 firms, including a random inefficiency term.
| 1 | |
| 2 | DATA casuser.cost_data; |
| 3 | call streaminit(12345); |
| 4 | DO i = 1 to 100; |
| 5 | log_output = rand('Normal', 1, 0.5); |
| 6 | log_price1 = rand('Normal', 0.5, 0.2); |
| 7 | log_price2 = rand('Normal', 0.5, 0.2); |
| 8 | v = rand('Normal', 0, 0.1); |
| 9 | u = abs(rand('Normal', 0, 0.2)); |
| 10 | log_cost = 0.5 + 0.8*log_output + 0.4*log_price1 + 0.6*log_price2 + v + u; |
| 11 | OUTPUT; |
| 12 | END; |
| 13 | |
| 14 | RUN; |
| 15 | |
| 16 | PROC CASUTIL; |
| 17 | load |
| 18 | DATA=casuser.cost_data casout="cost_data" replace; |
| 19 | |
| 20 | RUN; |
| 21 |
Estimates a simple Cobb-Douglas stochastic frontier cost model using the default exponential distribution for efficiency.
| 1 | |
| 2 | PROC CAS; |
| 3 | frontier.frontierCost TABLE="cost_data", model={depVars={{name="log_cost"}}, effects={{vars={"log_output", "log_price1", "log_price2"}}}}; |
| 4 | |
| 5 | RUN; |
| 6 |
Estimates a model specifying a Half-Normal efficiency distribution, custom optimization settings, and generates an output table containing technical efficiency scores.
| 1 | |
| 2 | PROC CAS; |
| 3 | frontier.frontierCost TABLE="cost_data", model={depVars={{name="log_cost"}}, effects={{vars={"log_output", "log_price1", "log_price2"}}}, modelOptions={type="HALF"}}, optimizer={algorithm="QUASINEWTON", maxit=200}, OUTPUT={casOut={name="frontier_scores", replace=true}, te1="TechEff", pred="PredictedCost"}; |
| 4 | |
| 5 | RUN; |
| 6 |