R-sq: within = Obs per group: min = 4 between = avg = 4.0 overall = max = 4

Transkripsi

1 LAMPIRAN Lampiran 1 Pengujian Regresi (Ordinary Least Square) untuk Persamaan dengan Variabel Investasi Pemerintah Total. regress y L.y pad ip is tk to Source SS df MS Number of obs = F( 6, 41) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] L pad ip isr tk to _cons

2 Lampiran 2 Pengujian Panel Statis Fixed Effect untuk Persamaan dengan Variabel Investasi Pemerintah Total xtreg y L.y pad ip is tk to, fe Fixed-effects (within) regression Number of obs = 48 R-sq: within = Obs per group: min = 4 between = avg = 4.0 overall = max = 4 F(6,30) = corr(u_i, Xb) = Prob > F = Coef. Std. Err. t P> t [95% Conf. Interval] L pad ip isr tk to _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i=0: F(11, 30) = 5.65 Prob > F = est sto fixed. xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (12) = Prob>chi2 =

3 Lampiran 3 Pengujian Panel Statis Random Effect untuk Persamaan dengan Variabel Investasi Pemerintah Total. xtreg y L.y pad ip is tk to, re Random-effects GLS regression Number of obs = 48 R-sq: within = Obs per group: min = 4 between = avg = 4.0 overall = max = 4 Random effects u_i ~ Gaussian Wald chi2(6) = corr(u_i, X) = 0 (assumed) Prob > chi2 = Coef. Std. Err. z P> z [95% Conf. Interval] L pad ip isr tk to _cons sigma_u sigma_e rho (fraction of variance due to u_i). est sto random. xttest0 Breusch and Pagan Lagrangian multiplier test for random effects y[prov,t] = Xb + u[prov] + e[prov,t] Estimated results: Var sd = sqrt(var) e u Test: Var(u) = 0 hi2(1) = 2.40 Prob > chi2 =

4 Lampiran 4 Uji Kebaikan (Uji Hausman) antara Fixed dan Random Effect untuk Persamaan dengan Variabel Investasi Pemerintah Total. hausman fixed random ---- Coefficients ---- (b) (B) (b-b) sqrt(diag(v_b-v_b)) fixed random Difference S.E. L pad ip isr tk to b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = Prob>chi2 = (V_b-V_B is not positive definite)

5 Lampiran 5 Pengujian Panel Dinamis (FD-GMM) untuk Persamaan dengan Variabel Investasi Pemerintah Total. xtabond y pad ip is tk to, two Arellano-Bond dynamic panel-data est Number of obs = 36 Time variable: tahun Obs per group: min = 3 avg = 3 max = 3 Number of instruments = 12 Wald chi2(6) = Prob > chi2 = Two-step results Coef. Std. Err. z P> z [95% Conf. Interval] L pad ip is tk to _cons Warning: gmm two-step standard errors are biased; robust standard errors are recommended. Instruments for differenced equation GMM-type: L(2/.).y Standard: D.pad D.ip D.is D.tk D.to Instruments for level equation Standard: _cons. estat abond Arellano-Bond test for zero autocorrelation in first-differenced errors Order z Prob > z H0: no autocorrelation. estat sargan Sargan test of overidentifying restrictions H0: overidentifying restrictions are valid chi2(5) = Prob > chi2 =

6 Lampiran 6 Pengujian Regresi (Ordinary Least Square) untuk Persamaan dengan Variabel Investasi Pemerintah untuk Keperluan Infrastruktur regress y L.y pad ipi isr tk to Source SS df MS Number of obs = F( 6, 41) = Model Prob > F = Residual R-squared = Adj R-squared = Total Root MSE = Coef. Std. Err. t P> t [95% Conf. Interval] L pad ipi isr tk to _cons

7 Lampiran 7 Pengujian Panel Statis Fixed Effect untuk Model Persamaan dengan Variabel Investasi Pemerintah untuk Keperluan Infrastruktur xtreg y L.y pad ipi isr tk to, fe Fixed-effects (within) regression Number of obs = 48 R-sq: within = Obs per group: min = 4 between = avg = 4.0 overall = max = 4 F(6,30) = corr(u_i, Xb) = Prob > F = Coef. Std. Err. t P> t [95% Conf. Interval] L pad ipi is tk to _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i=0: F(11, 30) = 4.91 Prob > F = est sto fixed. xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (12) = Prob>chi2 =

8 Lampiran 8 Pengujian Panel Statis Random Effect untuk Persamaan dengan Variabel Investasi Pemerintah untuk Keperluan Infrastruktur. xtreg y L.y pad ipi isr tk to, re Random-effects GLS regression Number of obs = 48 R-sq: within = Obs per group: min = 4 between = avg = 4.0 overall = max = 4 Random effects u_i ~ Gaussian Wald chi2(6) = corr(u_i, X) = 0 (assumed) Prob > chi2 = Coef. Std. Err. z P> z [95% Conf. Interval] L pad ipi isr tk to _cons sigma_u 0 sigma_e rho 0 (fraction of variance due to u_i). est sto random. xttest0 Breusch and Pagan Lagrangian multiplier test for random effects y[prov,t] = Xb + u[prov] + e[prov,t] Estimated results: Var sd = sqrt(var) e u 0 0 Test: Var(u) = 0 chi2(1) = 3.04 Prob > chi2 =

9 Lampran 9 Uji Kebaikan (Uji Hausman) antara Fixed dan Random Effect untuk Persamaan dengan Variabel Investasi Pemerintah untuk Keperluan Infrastruktur. hausman fixed random ---- Coefficients ---- (b) (B) (b-b) sqrt(diag(v_b-v_b)) fixed random Difference S.E. L pad jlnr isr tk to b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = Prob>chi2 = (V_b-V_B is not positive definite)

10 Lampiran 10 Pengujian Panel Dinamis (FD-GMM) untuk Persamaan dengan Variabel Investasi Pemerintah untuk Keperluan Infrastruktur. xtabond y pad ipi isr tk to, two Arellano-Bond dynamic panel-data est Number of obs = 36 Time variable: tahun Obs per group: min = 3 avg = 3 max = 3 Number of instruments = 12 Wald chi2(6) = Prob > chi2 = Two-step results Coef. Std. Err. z P> z [95% Conf. Interval] L pad ipi isr tk to _cons Warning: gmm two-step standard errors are biased; robust standard errors are recommended. Instruments for differenced equation GMM-type: L(2/.).y Standard: D.pad D.jlnr D.isr D.tk D.to Instruments for level equation Standard: _cons. estat abond Arellano-Bond test for zero autocorrelation in first-differenced errors Order z Prob > z H0: no autocorrelation. estat sargan Sargan test of overidentifying restrictions H0: overidentifying restrictions are valid chi2(5) = Prob > chi2 =