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Efficiency Measurement

William Greene Stern School of Business New York University. Efficiency Measurement. Lab Session 2. Stochastic Frontier Estimation. Application to Spanish Dairy Farms. N = 247 farms, T = 6 years (1993-1998). Using Farm Means of the Data. OLS vs. Frontier/MLE. JLMS Inefficiency Estimator.

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Efficiency Measurement

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  1. William Greene Stern School of Business New York University Efficiency Measurement

  2. Lab Session 2 Stochastic Frontier Estimation

  3. Application to Spanish Dairy Farms N = 247 farms, T = 6 years (1993-1998)

  4. Using Farm Means of the Data

  5. OLS vs. Frontier/MLE

  6. JLMS Inefficiency Estimator FRONTIER ; LHS = the variable ; RHS = ONE, the variables ; EFF = the new variable $ Creates a new variable in the data set. FRONTIER ; LHS = YIT ; RHS = X ; EFF = U_i $ Use ;Techeff = variable to compute exp(-u).

  7. Confidence Intervals for Technical Inefficiency, u(i)

  8. Prediction Intervals for Technical Efficiency, Exp[-u(i)]

  9. Prediction Intervals for Technical Efficiency, Exp[-u(i)]

  10. Compare SF and DEA

  11. Similar, but differentwith a crucial pattern

  12. The Dreaded Error 315 – Wrong Skewness

  13. Cost Frontier Model

  14. Linear Homogeneity Restriction

  15. Translog vs. Cobb Douglas

  16. Cost Frontier Command FRONTIER ; COST ; LHS = the variable ; RHS = ONE, the variables ; EFF = the new variable $ ε(i) = v(i) + u(i) [u(i) is still positive]

  17. Estimated Cost Frontier: C&G

  18. Cost Frontier Inefficiencies

  19. Normal-Truncated NormalFrontier Command FRONTIER [; COST] ; LHS = the variable ; RHS = ONE, the variables ; Model = Truncation ; EFF = the new variable $ ε(i) = v(i) +/- u(i) u(i) = |U(i)|, U(i) ~ N[μ,2] The half normal model has μ = 0.

  20. Observations • Truncation Model estimation is often unstable • Often estimation is not possible • When possible, estimates are often wild • Estimates of u(i) are usually only moderately affected • Estimates of u(i) are fairly stable across models (exponential, truncation, etc.)

  21. Truncated Normal Model ; Model = T

  22. Truncated Normal vs. Half Normal

  23. Multiple Output Cost Function

  24. Ranking Observations CREATE ; newname = Rnk ( Variable ) $ Creates the set of ranks. Use in any subsequent analysis.

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