Package dan Data

library(readxl)
library(car)
## Warning: package 'car' was built under R version 4.0.4
## Loading required package: carData
library(lmtest)     
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(plm)
## Warning: package 'plm' was built under R version 4.0.5
datasumbagsel<-read_excel("E:\\NEW.xlsx",col_names =T, sheet="Sheet1") 
## New names:
## * `` -> ...8
#View(datasumbagsel)
str(datasumbagsel)
## tibble [245 x 11] (S3: tbl_df/tbl/data.frame)
##  $ Kab   : chr [1:245] "Ogan Komering Ulu" "Ogan Komering Ulu" "Ogan Komering Ulu" "Ogan Komering Ulu" ...
##  $ Tahun : num [1:245] 2015 2016 2017 2018 2019 ...
##  $ PDRB  : num [1:245] 8230963 8556797 8904371 9349187 9876100 ...
##  $ Krt   : num [1:245] 3.44e+08 3.57e+08 3.72e+08 3.91e+08 4.13e+08 ...
##  $ MPrt  : num [1:245] 3.78e+08 3.93e+08 4.09e+08 4.30e+08 4.54e+08 ...
##  $ KLrt  : num [1:245] 11.9 12.2 13.1 14.2 13 ...
##  $ Lrt   : num [1:245] 67 68.8 68.2 66.4 68.7 ...
##  $ ...8  : logi [1:245] NA NA NA NA NA NA ...
##  $ lnPDRB: num [1:245] 15.9 16 16 16.1 16.1 ...
##  $ lnKrt : num [1:245] 19.7 19.7 19.7 19.8 19.8 ...
##  $ LnMPrt: num [1:245] 19.8 19.8 19.8 19.9 19.9 ...
head(datasumbagsel)
## # A tibble: 6 x 11
##   Kab        Tahun    PDRB     Krt    MPrt  KLrt   Lrt ...8  lnPDRB lnKrt LnMPrt
##   <chr>      <dbl>   <dbl>   <dbl>   <dbl> <dbl> <dbl> <lgl>  <dbl> <dbl>  <dbl>
## 1 Ogan Kome~  2015  8.23e6  3.44e8  3.78e8 11.9   67.0 NA      15.9  19.7   19.8
## 2 Ogan Kome~  2016  8.56e6  3.57e8  3.93e8 12.2   68.8 NA      16.0  19.7   19.8
## 3 Ogan Kome~  2017  8.90e6  3.72e8  4.09e8 13.1   68.2 NA      16.0  19.7   19.8
## 4 Ogan Kome~  2018  9.35e6  3.91e8  4.30e8 14.2   66.4 NA      16.1  19.8   19.9
## 5 Ogan Kome~  2019  9.88e6  4.13e8  4.54e8 13.0   68.7 NA      16.1  19.8   19.9
## 6 Ogan Kome~  2015  1.67e7  7.48e8  3.36e8  4.85  66.3 NA      16.6  20.4   19.6

Pembuatan Model sementara yaitu model (Umum, Tetap, dan Acak)

#model Umum
common<-plm(PDRB ~ Krt + MPrt + KLrt + Lrt, data=datasumbagsel, model="pooling", index = c("Kab","Tahun"))
common
## 
## Model Formula: PDRB ~ Krt + MPrt + KLrt + Lrt
## 
## Coefficients:
## (Intercept)         Krt        MPrt        KLrt         Lrt 
##  1.2504e+07  2.9474e-02 -3.7696e-03 -5.8091e+01 -1.7260e+05
#model tetap
fixed<-plm(PDRB ~ Krt + MPrt + KLrt + Lrt, data=datasumbagsel, model="within", index = c("Kab","Tahun"))
summary(fixed)
## Oneway (individual) effect Within Model
## 
## Call:
## plm(formula = PDRB ~ Krt + MPrt + KLrt + Lrt, data = datasumbagsel, 
##     model = "within", index = c("Kab", "Tahun"))
## 
## Balanced Panel: n = 49, T = 5, N = 245
## 
## Residuals:
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -4569078   -91166    15755        0    95999  2175489 
## 
## Coefficients:
##         Estimate  Std. Error t-value Pr(>|t|)    
## Krt   3.0543e-02  1.0464e-03 29.1876  < 2e-16 ***
## MPrt -2.9603e-03  1.4612e-03 -2.0260  0.04415 *  
## KLrt  9.7342e+00  6.0595e+00  1.6064  0.10982    
## Lrt  -1.6745e+04  1.5293e+04 -1.0949  0.27492    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    6.0063e+14
## Residual Sum of Squares: 5.005e+13
## R-Squared:      0.91667
## Adj. R-Squared: 0.8941
## F-statistic: 528.029 on 4 and 192 DF, p-value: < 2.22e-16
#model acak
random<- plm(PDRB~ Krt + MPrt + KLrt + Lrt, data=datasumbagsel, model="random", index = c("Kab","Tahun"))
summary(random)
## Oneway (individual) effect Random Effect Model 
##    (Swamy-Arora's transformation)
## 
## Call:
## plm(formula = PDRB ~ Krt + MPrt + KLrt + Lrt, data = datasumbagsel, 
##     model = "random", index = c("Kab", "Tahun"))
## 
## Balanced Panel: n = 49, T = 5, N = 245
## 
## Effects:
##                     var   std.dev share
## idiosyncratic 2.607e+11 5.106e+05 0.016
## individual    1.598e+13 3.997e+06 0.984
## theta: 0.943
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -4064682.9  -104152.6    -3505.1    80653.5  2788449.6 
## 
## Coefficients:
##                Estimate  Std. Error z-value Pr(>|z|)    
## (Intercept)  1.1419e+06  1.2202e+06  0.9358  0.34938    
## Krt          3.0338e-02  8.7024e-04 34.8611  < 2e-16 ***
## MPrt        -3.1832e-03  1.2628e-03 -2.5206  0.01171 *  
## KLrt         8.9335e+00  5.8187e+00  1.5353  0.12471    
## Lrt         -1.7678e+04  1.5271e+04 -1.1576  0.24701    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7.9256e+14
## Residual Sum of Squares: 6.3294e+13
## R-Squared:      0.92014
## Adj. R-Squared: 0.91881
## Chisq: 2765.26 on 4 DF, p-value: < 2.22e-16

uji chow >>> untuk menentukan model yang baik digunakan antar fixed dgn common

# Menguji chow
pooltest(common,fixed)
## 
##  F statistic
## 
## data:  PDRB ~ Krt + MPrt + KLrt + Lrt
## F = 307.82, df1 = 48, df2 = 192, p-value < 2.2e-16
## alternative hypothesis: unstability

Model yang bagus digunakan dari hasil diatas yaitu model tetap/Fixed

uji hausman >>> untuk menentukan model yang baik digunakan antar fixed dgn random

# Menguji Hausmaan
phtest(fixed,random)
## 
##  Hausman Test
## 
## data:  PDRB ~ Krt + MPrt + KLrt + Lrt
## chisq = 4.7257, df = 4, p-value = 0.3166
## alternative hypothesis: one model is inconsistent

Model yang bagus digunakan dari hasil diatas yaitu model acak/Random

uji Breusch Pagan >>> menentukan apakah ada efek time maupun individu atau bahkan tidak ada pada model.

(Ketika nilai p-value kurang dari alpha maka dapat disimpulkan ada efek)

#menguji Breusch Pagan
BP<- plm(PDRB~ Krt + MPrt + KLrt + Lrt, data=datasumbagsel, model="random", index = c("Kab","Tahun"))

#Efek Dua Arah
plmtest(BP, effect="twoways", type="bp")
## 
##  Lagrange Multiplier Test - two-ways effects (Breusch-Pagan) for
##  balanced panels
## 
## data:  PDRB ~ Krt + MPrt + KLrt + Lrt
## chisq = 457.35, df = 2, p-value < 2.2e-16
## alternative hypothesis: significant effects
#Efek Individu/Cross Section
plmtest(BP, effect="individual", type="bp")
## 
##  Lagrange Multiplier Test - (Breusch-Pagan) for balanced panels
## 
## data:  PDRB ~ Krt + MPrt + KLrt + Lrt
## chisq = 455.14, df = 1, p-value < 2.2e-16
## alternative hypothesis: significant effects
#Efek Waktu/Time
plmtest(BP, effect="time", type="bp")
## 
##  Lagrange Multiplier Test - time effects (Breusch-Pagan) for balanced
##  panels
## 
## data:  PDRB ~ Krt + MPrt + KLrt + Lrt
## chisq = 2.2049, df = 1, p-value = 0.1376
## alternative hypothesis: significant effects

dari model diatas dipilih model acak individual saja.

pembuatan Model >>> Model yang terpilih

random1<- plm(PDRB~ Krt + MPrt + KLrt + Lrt, data=datasumbagsel, model="random", effect="individual", index = c("Kab","Tahun"))
summary(random1)
## Oneway (individual) effect Random Effect Model 
##    (Swamy-Arora's transformation)
## 
## Call:
## plm(formula = PDRB ~ Krt + MPrt + KLrt + Lrt, data = datasumbagsel, 
##     effect = "individual", model = "random", index = c("Kab", 
##         "Tahun"))
## 
## Balanced Panel: n = 49, T = 5, N = 245
## 
## Effects:
##                     var   std.dev share
## idiosyncratic 2.607e+11 5.106e+05 0.016
## individual    1.598e+13 3.997e+06 0.984
## theta: 0.943
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -4064682.9  -104152.6    -3505.1    80653.5  2788449.6 
## 
## Coefficients:
##                Estimate  Std. Error z-value Pr(>|z|)    
## (Intercept)  1.1419e+06  1.2202e+06  0.9358  0.34938    
## Krt          3.0338e-02  8.7024e-04 34.8611  < 2e-16 ***
## MPrt        -3.1832e-03  1.2628e-03 -2.5206  0.01171 *  
## KLrt         8.9335e+00  5.8187e+00  1.5353  0.12471    
## Lrt         -1.7678e+04  1.5271e+04 -1.1576  0.24701    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7.9256e+14
## Residual Sum of Squares: 6.3294e+13
## R-Squared:      0.92014
## Adj. R-Squared: 0.91881
## Chisq: 2765.26 on 4 DF, p-value: < 2.22e-16
#Melihat seberapa besar pengaruh masing-masing cross section
ranef(random1)
##              Bangka        Bangka Barat      Bangka Selatan       Bangka Tengah 
##           3145344.7           3908336.0           1808269.5           1674966.9 
##           Banyuasin            Belitung      Belitung Timur    Bengkulu Selatan 
##           5461593.6           2381118.8           2122414.5            122648.6 
##     Bengkulu Tengah      Bengkulu Utara        Empat Lawang                Kaur 
##           -625983.3           2414940.4          -1513359.4           -288758.9 
##           Kepahiang Kota Bandar Lampung       Kota Bengkulu  Kota Lubuk Linggau 
##          -1263493.2           -447563.4          -2810740.5           -685570.5 
##          Kota Metro     Kota Pagar Alam      Kota Palembang  Kota Pangkalpinang 
##          -1495898.7           -320300.1           9162467.9           3453692.4 
##     Kota Prabumulih               Lahat       Lampung Barat     Lampung Selatan 
##          -1111590.4          -3275106.2          -1341272.4           -605893.7 
##      Lampung Tengah       Lampung Timur       Lampung Utara              Lebong 
##          -8604401.8         -12869589.9          -4101378.3            593772.8 
##              Mesuji          Muara Enim            Mukomuko      Musi Banyuasin 
##          -1551420.8          -2827290.9           1580999.5          10006526.2 
##          Musi Rawas    Musi Rawas Utara           Ogan Ilir  Ogan Komering Ilir 
##          12334852.8           1195250.1          -1870791.0          -5364241.0 
##   Ogan Komering Ulu         Oku Selatan           Oku Timur                Pali 
##          -1026190.6             90444.2          -1785437.1           -137053.2 
##           Pesawaran       Pesisir Barat           Pringsewu       Rejang Lebong 
##          -2357408.9           -992148.1          -5461965.1           -534888.1 
##              Seluma           Tanggamus Tulang Bawang Barat        Tulangbawang 
##           -546172.6           1373845.9           1387658.3           1191354.8 
##           Way Kanan 
##            405410.2

Menguji Model yang terpilih

1. Uji Autokorelasi

# Uji Korelasi Serial
pbgtest(random1)
## 
##  Breusch-Godfrey/Wooldridge test for serial correlation in panel models
## 
## data:  PDRB ~ Krt + MPrt + KLrt + Lrt
## chisq = 37.441, df = 5, p-value = 4.887e-07
## alternative hypothesis: serial correlation in idiosyncratic errors

2. Uji Homoskedastisitas

bptest(random1)
## 
##  studentized Breusch-Pagan test
## 
## data:  random1
## BP = 79.418, df = 4, p-value = 2.314e-16

3. Uji Overall

summary(random1)
## Oneway (individual) effect Random Effect Model 
##    (Swamy-Arora's transformation)
## 
## Call:
## plm(formula = PDRB ~ Krt + MPrt + KLrt + Lrt, data = datasumbagsel, 
##     effect = "individual", model = "random", index = c("Kab", 
##         "Tahun"))
## 
## Balanced Panel: n = 49, T = 5, N = 245
## 
## Effects:
##                     var   std.dev share
## idiosyncratic 2.607e+11 5.106e+05 0.016
## individual    1.598e+13 3.997e+06 0.984
## theta: 0.943
## 
## Residuals:
##       Min.    1st Qu.     Median    3rd Qu.       Max. 
## -4064682.9  -104152.6    -3505.1    80653.5  2788449.6 
## 
## Coefficients:
##                Estimate  Std. Error z-value Pr(>|z|)    
## (Intercept)  1.1419e+06  1.2202e+06  0.9358  0.34938    
## Krt          3.0338e-02  8.7024e-04 34.8611  < 2e-16 ***
## MPrt        -3.1832e-03  1.2628e-03 -2.5206  0.01171 *  
## KLrt         8.9335e+00  5.8187e+00  1.5353  0.12471    
## Lrt         -1.7678e+04  1.5271e+04 -1.1576  0.24701    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Total Sum of Squares:    7.9256e+14
## Residual Sum of Squares: 6.3294e+13
## R-Squared:      0.92014
## Adj. R-Squared: 0.91881
## Chisq: 2765.26 on 4 DF, p-value: < 2.22e-16

4. Uji Multikolinieritas

vif(random1)
##      Krt     MPrt     KLrt      Lrt 
## 2.569635 2.338407 1.295007 1.024896