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Ekonometrika Ilustrasi Permasalah Multiple Regression Dengan Software

Ekonometrika Ilustrasi Permasalah Multiple Regression Dengan Software. Pendugaan Model Cobb Douglas. Data pada file Excell Tugas , sheet CobbDouglas

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Ekonometrika Ilustrasi Permasalah Multiple Regression Dengan Software

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  1. Ekonometrika Ilustrasi Permasalah Multiple Regression Dengan Software Dr. Rahma Fitriani, S.Si., M.Sc

  2. Pendugaan Model Cobb Douglas • Data pada file ExcellTugas, sheet CobbDouglas • Dari 51 perusahaandiamatiproduktivitas (OUTPUT dalam $), investasiuntuk modal (CAPITAL dalam $) daninvestasitenagakerja (LABOR dalam $) • Dilakukan pendugaan model Dr. Rahma Fitriani, S.Si., M.Sc

  3. UjiKeberartian Model secaraSimultan • Menggunakanujihipotesis • Model unrestricted: • Model restricted • Hipotesis Dr. Rahma Fitriani, S.Si., M.Sc

  4. Output untuk Unrestricted Model • Model 1: OLS, using observations 1-51 • Dependent variable: l_output • coefficient std. error t-ratio p-value • ---------------------------------------------------------- • const 3.88760 0.396228 9.812 4.70e-013 *** • l_labor 0.468332 0.0989259 4.734 1.98e-05 *** • l_capital 0.521279 0.0968871 5.380 2.18e-06 *** • Mean dependent var 16.94139 S.D. dependent var 1.380870 • Sum squared resid 3.415520 S.E. of regression 0.266752 • R-squared 0.964175 Adjusted R-squared 0.962683 • F(2, 48) 645.9311 P-value(F) 2.00e-35 • Log-likelihood -3.426721 Akaike criterion 12.85344 • Schwarz criterion 18.64892 Hannan-Quinn 15.06807 • Log-likelihood for output = -867.437 JKGU= 3.4155 Dr. Rahma Fitriani, S.Si., M.Sc

  5. Output Untuk Restricted Model • Model 2: OLS, using observations 1-51 • Dependent variable: l_output • coefficient std. error t-ratio p-value • --------------------------------------------------------- • const 16.9414 0.193361 87.62 2.12e-056 *** • Mean dependent var 16.94139 S.D. dependent var 1.380870 • Sum squared resid 95.34013 S.E. of regression 1.380870 • R-squared 0.000000 Adjusted R-squared 0.000000 • Log-likelihood -88.31931 Akaike criterion 178.6386 • Schwarz criterion 180.5704 Hannan-Quinn 179.3768 • Log-likelihood for output = -952.33 JKGR= 95.34 Dr. Rahma Fitriani, S.Si., M.Sc

  6. Output Omitted variable Test Samadengan output sebelumnya Restricted Model • Model 3: OLS, using observations 1-51 • Dependent variable: l_output • coefficient std. error t-ratio p-value • --------------------------------------------------------- • const 16.9414 0.193361 87.62 2.12e-056 *** • Mean dependent var 16.94139 S.D. dependent var 1.380870 • Sum squared resid 95.34013 S.E. of regression 1.380870 • R-squared 0.000000 Adjusted R-squared 0.000000 • Log-likelihood -88.31931 Akaike criterion 178.6386 • Schwarz criterion 180.5704 Hannan-Quinn 179.3768 • Log-likelihood for output = -952.33 • Comparison of Model 1 and Model 3: • Null hypothesis: the regression parameters are zero for the variables • l_labor, l_capital • Test statistic: F(2, 48) = 645.931, with p-value = 1.99686e-035 • Of the 3 model selection statistics, 0 have improved. Statistikuji F Dr. Rahma Fitriani, S.Si., M.Sc

  7. Karena p-value relatifkecil, menujunol • Cukupbuktiuntukmenolak H0 • Koefisienbagipeubah Labour dan Capital tidaksamadengannol • Unrestricted model berbedanyatadengan restricted model • Unrestricted model lebihbaikmenjelaskankeragamanOutput produksi Dr. Rahma Fitriani, S.Si., M.Sc

  8. UjiLinear Restriction • Menggunakanujihipotesis • Model unrestricted: • Restritcionpadahipotesis: • Model restricted: Dr. Rahma Fitriani, S.Si., M.Sc

  9. Output untuk Unrestricted Model • Model 1: OLS, using observations 1-51 • Dependent variable: l_output • coefficient std. error t-ratio p-value • ---------------------------------------------------------- • const 3.88760 0.396228 9.812 4.70e-013 *** • l_labor 0.468332 0.0989259 4.734 1.98e-05 *** • l_capital 0.521279 0.0968871 5.380 2.18e-06 *** • Mean dependent var 16.94139 S.D. dependent var 1.380870 • Sum squared resid 3.415520 S.E. of regression 0.266752 • R-squared 0.964175 Adjusted R-squared 0.962683 • F(2, 48) 645.9311 P-value(F) 2.00e-35 • Log-likelihood -3.426721 Akaike criterion 12.85344 • Schwarz criterion 18.64892 Hannan-Quinn 15.06807 • Log-likelihood for output = -867.437 JKGU= 3.4155 Dr. Rahma Fitriani, S.Si., M.Sc

  10. Output Linear Restricted Model • Model 4: OLS, using observations 1-51 • Dependent variable: l_Out_Labor • coefficient std. error t-ratio p-value • -------------------------------------------------------------- • const 3.75624 0.185368 20.26 1.82e-025 *** • l_Capital_Lab 0.523756 0.0958122 5.466 1.54e-06 *** • Mean dependent var 4.749135 S.D. dependent var 0.332104 • Sum squared resid 3.425582 S.E. of regression 0.264405 • R-squared 0.378823 Adjusted R-squared 0.366146 • F(1, 49) 29.88247 P-value(F) 1.54e-06 • Log-likelihood -3.501733 Akaike criterion 11.00347 • Schwarz criterion 14.86712 Hannan-Quinn 12.47988 • Log-likelihood for Out_Labor = -245.708 JKGR= 3.4255 Dr. Rahma Fitriani, S.Si., M.Sc

  11. Output Linear Restriction Test • Restriction: • b[l_labor] + b[l_capital] = 1 • Test statistic: F(1, 48) = 0.141406, with p-value = 0.708544 • Restricted estimates: • coefficient std. error t-ratio p-value • ---------------------------------------------------------- • const 3.75624 0.185368 20.26 1.82e-025 *** • l_labor 0.476244 0.0958122 4.971 8.56e-06 *** • l_capital 0.523756 0.0958122 5.466 1.54e-06 *** • Standard error of the regression = 0.264405 Dr. Rahma Fitriani, S.Si., M.Sc

  12. Karena p-value yang cukupbesar, tidakcukupbuktiuntukmenolak H0 • Restricted dan unrestricted model tidakberbedanyata • Jumlahdarikedua parameter = 1 • Penduga model: ^l_output = 3.89 + 0.468*l_labor + 0.521*l_capital (0.396)(0.0989) (0.0969) n = 51, R-squared = 0.964 (standard errors in parentheses) Dr. Rahma Fitriani, S.Si., M.Sc

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