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

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Presentasi berjudul: "Ekonometrika Ilustrasi Permasalah Multiple Regression Dengan Software Dr. Rahma Fitriani, S.Si., M.Sc."— Transcript presentasi:

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

2 Pendugaan Model Cobb Douglas Dr. Rahma Fitriani, S.Si., M.Sc  Data pada file Excell Tugas, sheet CobbDouglas  Dari 51 perusahaan diamati produktivitas (OUTPUT dalam $), investasi untuk modal (CAPITAL dalam $) dan investasi tenaga kerja (LABOR dalam $)  Dilakukan pendugaan model

3 Uji Keberartian Model secara Simultan Dr. Rahma Fitriani, S.Si., M.Sc  Menggunakan uji hipotesis  Model unrestricted:  Model restricted  Hipotesis

4 Output untuk Unrestricted Model Dr. Rahma Fitriani, S.Si., M.Sc  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 JKG U = 3.4155

5 Output Untuk Restricted Model Dr. Rahma Fitriani, S.Si., M.Sc  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 JKG R = 95.34

6 Output Omitted variable Test Dr. Rahma Fitriani, S.Si., M.Sc  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. Sama dengan output sebelumnya Restricted Model Statistik uji F

7 Dr. Rahma Fitriani, S.Si., M.Sc  Karena p-value relatif kecil, menuju nol  Cukup bukti untuk menolak H 0  Koefisien bagi peubah Labour dan Capital tidak sama dengan nol  Unrestricted model berbeda nyata dengan restricted model  Unrestricted model lebih baik menjelaskan keragaman Output produksi

8 Uji Linear Restriction Dr. Rahma Fitriani, S.Si., M.Sc  Menggunakan uji hipotesis  Model unrestricted:  Restritcion pada hipotesis:  Model restricted:

9 Output untuk Unrestricted Model Dr. Rahma Fitriani, S.Si., M.Sc  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 JKG U = 3.4155

10 Output Linear Restricted Model Dr. Rahma Fitriani, S.Si., M.Sc  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 JKG R = 3.4255

11 Output Linear Restriction Test Dr. Rahma Fitriani, S.Si., M.Sc  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

12 Dr. Rahma Fitriani, S.Si., M.Sc  Karena p-value yang cukup besar, tidak cukup bukti untuk menolak H 0  Restricted dan unrestricted model tidak berbeda nyata  Jumlah dari kedua 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)


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