Econometria: exercício crescimento municipal em Mato Grosso entre 2001 e 2010

Abstract

This is an undergrad student level exercise for class use. We analyse 139 municipal cross-section data for the Brazilian State of Mato Grosso on a static growth model.

Licença

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

License: CC BY-SA 4.0

Citação

Sugestão de citação: FIGUEIREDO, Adriano Marcos Rodrigues. Econometria: exercício crescimento municipal em Mato Grosso entre 2001 e 2010. Campo Grande-MS,Brasil: RStudio/Rpubs, 2019. Disponível em http://rpubs.com/amrofi/growth_mt2001_2010.

1 Introdução

Exemplo sobre crescimento municipal adaptado da dissertacao de William Marquezin (2014) na UFMT. Dados de 139 municipios de MT, em que 2001 é o ano base e o crescimento refere-se até 2010. A variável dependente do modelo é a taxa de crescimento da renda per capita municipal (barro) conforme Barro e Sala-i-Martin (1992)=“BARRO”. Outras variáveis são:
# “ordem” = ordenacao dos municipios
# “KEY” = ordem
# “MUNICIPIO” = nome do municipio
# “BARRO” = variavel dependente (acima descrita)
# “DASSOW” = alternativa para a variavel dependente (nao utilizada)
# Variáveis explicativas:
# 1) Renda per capita no ano inicial “LNYI_T_1”
# 2) Composição industrial (Sind): “SIND”
# 3) Composição da agropecuária (Sagro): “SAGRO”
# 4) Composição do setor de serviços (Sserv): “SSERV”
# 5) Composição da administração pública (Spub): “SPUB”
# 6) Capital humano (h): “H”
# 7) Densidade demográfica (dd): “DD”
# 8) Despesas orçamentárias (dorc): “DORC”
# 9) Operações de crédito (cred): “CRED”
# 10) Exportações Municipais (expor): “EXPOR”
# 11) Importações Municipais (impor): “IMPOR”
# 12) Mercado Regional (mreg): “MREG”
# 13) Carga tributária total municipal (t): “T”
# 14) Transferências Intergovernamentais do ICMS (ticms): “TICMS”
# 15) Transferências Intergovernamentais do FPM (tfpm): “TFPM”
# 16) O índice de GINI (gini): “GINI”
# 17) índice de THEIL (theil): “THEIL”
# variavel auxiliar não utilizada: “TMREG”
# variavel auxiliar não utilizada: “CCOM” corrente de comercio

Um data.frame com 139 observations para 24 variáveis.

Para reprodução, pode-se fazer o download prévio dos dados a partir de https://github.com/amrofi/crescimento_mt/blob/master/crescimento.rds, e armazenar no diretório do projeto.

library(readxl)
library(foreign)
library(dynlm)
library(car)
library(lmtest)
library(sandwich)
library(tseries)
library(kableExtra)
# o arquivo dados está em formato dput embeded no script, em um chunk oculto que
# o leitor tem acesso ao baixar o Rmd, clicando em code

summary(dados)

     ordem            KEY         MUNICIPIO             BARRO         
 Min.   :  1.0   Min.   :  1.0   Length:139         Min.   :-0.07986  
 1st Qu.: 35.5   1st Qu.: 35.5   Class :character   1st Qu.: 0.03466  
 Median : 70.0   Median : 70.0   Mode  :character   Median : 0.04971  
 Mean   : 70.0   Mean   : 70.0                      Mean   : 0.05413  
     DASSOW            LNYI_T_1           SIND              SAGRO         
 Min.   :-0.05696   Min.   : 8.219   Min.   :-0.14688   Min.   :-0.37391  
 1st Qu.: 0.04068   1st Qu.: 8.840   1st Qu.: 0.02745   1st Qu.: 0.09529  
 Median : 0.06269   Median : 9.077   Median : 0.05442   Median : 0.22181  
 Mean   : 0.08061   Mean   : 9.222   Mean   : 0.06737   Mean   : 0.22179  
     SSERV               SPUB                H                DD          
 Min.   :-0.56295   Min.   :0.006014   Min.   : 2.662   Min.   :  0.2792  
 1st Qu.: 0.07236   1st Qu.:0.077798   1st Qu.:12.590   1st Qu.:  1.1508  
 Median : 0.12822   Median :0.108222   Median :15.828   Median :  2.2449  
 Mean   : 0.14414   Mean   :0.127134   Mean   :17.684   Mean   :  7.2235  
      DORC             CRED            EXPOR              IMPOR         
 Min.   : 532.4   Min.   :   0.0   Min.   :    0.00   Min.   :    0.00  
 1st Qu.:1348.8   1st Qu.:   0.0   1st Qu.:    0.00   1st Qu.:    0.00  
 Median :1606.9   Median : 757.6   Median :   39.21   Median :    0.00  
 Mean   :1800.4   Mean   :1589.0   Mean   : 2128.94   Mean   :  205.89  
      CCOM               MREG             T                TFPM         
 Min.   :    0.00   Min.   : 5863   Min.   :0.03508   Min.   :   60.68  
 1st Qu.:    0.00   1st Qu.:11363   1st Qu.:0.04787   1st Qu.:  300.37  
 Median :   92.58   Median :13452   Median :0.05877   Median :  535.94  
 Mean   : 2334.82   Mean   :16629   Mean   :0.06865   Mean   :  973.11  
     TICMS             TMREG              GINI            THEIL       
 Min.   :  87.68   Min.   :0.00849   Min.   :0.3600   Min.   :0.1900  
 1st Qu.: 243.31   1st Qu.:0.03445   1st Qu.:0.5300   1st Qu.:0.4750  
 Median : 382.59   Median :0.04830   Median :0.5800   Median :0.5500  
 Mean   : 445.03   Mean   :0.04925   Mean   :0.5755   Mean   :0.5809  
 [ reached getOption("max.print") -- omitted 2 rows ]

attach(dados)
# algumas variaveis vou dividir por 1000000 para nivelar expor_6 impor_6 mreg_6
# tfpm_6 ticms_6 cred_6

Estimando o modelo linear de regressao multipla fazendo conforme a expressão do enunciado.

2 Resultados

2.1 Estimação

Fazendo as regressoes. Algumas variáveis foram construídas com uso de logaritmos e portanto, deve-se olhar a especificação destas.

# regressao multipla de
# BARRO~LNYI_T_1+SIND+SAGRO+SSERV+SPUB+H+DD+DORC+I(CRED*10^-6)+I(EXPOR*10^-6)+I(IMPOR*10^-6)+I(MREG*10^-6)+I(TFPM*10^-6)+I(TICMS*10^-6)+GINI
# variaveis transformadas
attach(dados)
Exporta <- I(EXPOR * 10^-6)
Importa <- I(IMPOR * 10^-6)
Mregio <- I(MREG * 10^-6)
FPM <- I(TFPM * 10^-6)
TICMSm <- I(TICMS * 10^-6)
credito <- I(CRED * 10^-6)
mod1 <- lm(BARRO ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + DD + DORC + 
    T + Exporta + Importa + Mregio + FPM + TICMSm + credito, data = dados)

Vamos utilizar o pacote stargazer para organizar as saídas de resultados. Se a saída fosse apenas pelo comando summary, sairia da forma:

summary(mod1)

Call:
lm(formula = BARRO ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + 
    H + DD + DORC + T + Exporta + Importa + Mregio + FPM + TICMSm + 
    credito, data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.039810 -0.008103  0.000716  0.006618  0.031697 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.663e-01  3.869e-02  12.050  < 2e-16 ***
LNYI_T_1    -5.058e-02  4.582e-03 -11.037  < 2e-16 ***
SIND         1.284e-01  1.911e-02   6.721 6.00e-10 ***
SAGRO        1.075e-01  8.151e-03  13.187  < 2e-16 ***
SSERV        5.461e-02  1.970e-02   2.772  0.00644 ** 
SPUB        -1.438e-01  2.029e-02  -7.090 9.22e-11 ***
H            2.522e-04  2.098e-04   1.202  0.23177    
DD           4.799e-05  5.502e-05   0.872  0.38478    
DORC         4.773e-06  2.981e-06   1.601  0.11195    
T            2.015e-02  9.462e-02   0.213  0.83172    
Exporta     -2.968e-01  3.898e-01  -0.761  0.44784    
Importa      4.759e+00  1.377e+00   3.457  0.00075 ***
Mregio      -1.942e-01  1.898e-01  -1.023  0.30838    
FPM         -1.464e+00  8.606e-01  -1.701  0.09155 .  
TICMSm       4.507e+01  9.618e+00   4.686 7.27e-06 ***
credito      1.029e+00  9.060e-01   1.135  0.25843    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01302 on 123 degrees of freedom
Multiple R-squared:  0.8901,    Adjusted R-squared:  0.8767 
F-statistic: 66.39 on 15 and 123 DF,  p-value: < 2.2e-16

Agora, criando uma tabela com as várias saídas de modelos, com o pacote stargazer tem-se, com a geração de AIC e BIC:

mod1$AIC <- AIC(mod1)
mod1$BIC <- BIC(mod1)
library(stargazer)
stargazer(mod1, title = "Título: Resultado da Regressão", align = TRUE, type = "text", 
    style = "all", keep.stat = c("aic", "bic", "rsq", "adj.rsq", "n"))

Título: Resultado da Regressão
===============================================
                        Dependent variable:    
                    ---------------------------
                               BARRO           
-----------------------------------------------
LNYI_T_1                     -0.051***         
                              (0.005)          
                            t = -11.037        
                             p = 0.000         
SIND                         0.128***          
                              (0.019)          
                             t = 6.721         
                             p = 0.000         
SAGRO                        0.107***          
                              (0.008)          
                            t = 13.187         
                             p = 0.000         
SSERV                        0.055***          
                              (0.020)          
                             t = 2.772         
                             p = 0.007         
SPUB                         -0.144***         
                              (0.020)          
                            t = -7.090         
                             p = 0.000         
H                             0.0003           
                             (0.0002)          
                             t = 1.202         
                             p = 0.232         
DD                            0.00005          
                             (0.0001)          
                             t = 0.872         
                             p = 0.385         
DORC                          0.00000          
                             (0.00000)         
                             t = 1.601         
                             p = 0.112         
T                              0.020           
                              (0.095)          
                             t = 0.213         
                             p = 0.832         
Exporta                       -0.297           
                              (0.390)          
                            t = -0.761         
                             p = 0.448         
Importa                      4.759***          
                              (1.377)          
                             t = 3.457         
                             p = 0.001         
Mregio                        -0.194           
                              (0.190)          
                            t = -1.023         
                             p = 0.309         
FPM                           -1.464*          
                              (0.861)          
                            t = -1.701         
                             p = 0.092         
TICMSm                       45.070***         
                              (9.618)          
                             t = 4.686         
                            p = 0.00001        
credito                        1.029           
                              (0.906)          
                             t = 1.135         
                             p = 0.259         
Constant                     0.466***          
                              (0.039)          
                            t = 12.050         
                             p = 0.000         
-----------------------------------------------
Observations                    139            
R2                             0.890           
Adjusted R2                    0.877           
Akaike Inf. Crit.            -795.426          
Bayesian Inf. Crit.          -745.540          
===============================================
Note:               *p<0.1; **p<0.05; ***p<0.01

2.2 Correlação

library(corrplot)
corel <- cor(dados[, 6:24])  # somente var. explicativas
corrplot(corel, method = "number", type = "lower", number.digits = 2)

2.3 Teste de Multicolinearidade (vif)

library(car)
reg1.vif <- vif(mod1)
reg1.vif

LNYI_T_1     SIND    SAGRO    SSERV     SPUB        H       DD     DORC 
6.678067 1.759081 1.869240 4.760146 2.633731 2.731471 1.981365 4.035582 
       T  Exporta  Importa   Mregio      FPM   TICMSm  credito 
5.609833 3.865815 1.974317 1.891542 1.787013 7.008875 2.676959 

3 Regressoes auxiliares para a regra de Klein

reg1.LNYI_T_1 <- lm(LNYI_T_1 ~ SIND + SAGRO + SSERV + SPUB + H + DD + DORC + T + 
    I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 
    10^-6) + I(CRED * 10^-6), data = dados)
summary(reg1.LNYI_T_1)

Call:
lm(formula = LNYI_T_1 ~ SIND + SAGRO + SSERV + SPUB + H + DD + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.48705 -0.15067 -0.02865  0.11092  0.89471 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       8.334e+00  1.222e-01  68.187  < 2e-16 ***
SIND              5.504e-01  3.712e-01   1.483  0.14063    
SAGRO             2.255e-01  1.584e-01   1.423  0.15724    
SSERV            -1.699e+00  3.547e-01  -4.789 4.68e-06 ***
SPUB             -2.138e+00  3.481e-01  -6.142 1.02e-08 ***
H                 9.130e-03  4.030e-03   2.266  0.02521 *  
DD               -1.495e-03  1.070e-03  -1.398  0.16475    
DORC             -1.152e-06  5.842e-05  -0.020  0.98429    
T                 8.074e+00  1.707e+00   4.731 5.99e-06 ***
I(EXPOR * 10^-6)  2.171e+01  7.387e+00   2.939  0.00393 ** 
I(IMPOR * 10^-6) -6.258e+01  2.638e+01  -2.372  0.01923 *  
I(MREG * 10^-6)   8.905e+00  3.633e+00   2.451  0.01564 *  
I(TFPM * 10^-6)  -1.899e+01  1.678e+01  -1.132  0.25997    
I(TICMS * 10^-6)  8.754e+02  1.713e+02   5.110 1.19e-06 ***
I(CRED * 10^-6)   3.911e+01  1.740e+01   2.247  0.02639 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2551 on 124 degrees of freedom
Multiple R-squared:  0.8503,    Adjusted R-squared:  0.8333 
F-statistic: 50.29 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.SIND <- lm(SIND ~ LNYI_T_1 + SAGRO + SSERV + SPUB + H + DD + DORC + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.SIND)

Call:
lm(formula = SIND ~ LNYI_T_1 + SAGRO + SSERV + SPUB + H + DD + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.13361 -0.03714 -0.01083  0.02162  0.20721 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -2.462e-01  1.805e-01  -1.364 0.175144    
LNYI_T_1          3.166e-02  2.135e-02   1.483 0.140625    
SAGRO            -2.762e-02  3.823e-02  -0.723 0.471287    
SSERV             3.290e-01  8.776e-02   3.749 0.000271 ***
SPUB              3.043e-01  9.135e-02   3.331 0.001141 ** 
H                 4.966e-04  9.853e-04   0.504 0.615137    
DD               -6.609e-05  2.585e-04  -0.256 0.798648    
DORC              1.397e-05  1.396e-05   1.001 0.318613    
T                -5.123e-01  4.424e-01  -1.158 0.249015    
I(EXPOR * 10^-6)  2.436e+00  1.819e+00   1.339 0.182942    
I(IMPOR * 10^-6)  1.286e+01  6.366e+00   2.019 0.045607 *  
I(MREG * 10^-6)  -1.137e-02  8.922e-01  -0.013 0.989854    
I(TFPM * 10^-6)   5.287e+00  4.017e+00   1.316 0.190568    
I(TICMS * 10^-6) -1.494e+02  4.317e+01  -3.460 0.000740 ***
I(CRED * 10^-6)  -1.890e+00  4.255e+00  -0.444 0.657652    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.06119 on 124 degrees of freedom
Multiple R-squared:  0.4315,    Adjusted R-squared:  0.3673 
F-statistic: 6.723 on 14 and 124 DF,  p-value: 4.992e-10

reg1.SAGRO <- lm(SAGRO ~ SIND + LNYI_T_1 + SSERV + SPUB + H + DD + DORC + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.SAGRO)

Call:
lm(formula = SAGRO ~ SIND + LNYI_T_1 + SSERV + SPUB + H + DD + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.55310 -0.07118 -0.00489  0.06312  0.35643 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -3.185e-01  4.254e-01  -0.749   0.4554    
SIND             -1.518e-01  2.101e-01  -0.723   0.4713    
LNYI_T_1          7.126e-02  5.008e-02   1.423   0.1572    
SSERV             9.183e-01  2.008e-01   4.573 1.15e-05 ***
SPUB              5.272e-01  2.184e-01   2.413   0.0173 *  
H                 3.591e-04  2.312e-03   0.155   0.8768    
DD               -1.401e-04  6.060e-04  -0.231   0.8176    
DORC             -6.523e-08  3.285e-05  -0.002   0.9984    
T                -2.934e+00  1.009e+00  -2.909   0.0043 ** 
I(EXPOR * 10^-6) -8.076e+00  4.233e+00  -1.908   0.0587 .  
I(IMPOR * 10^-6) -8.829e+00  1.515e+01  -0.583   0.5610    
I(MREG * 10^-6)  -3.302e+00  2.070e+00  -1.595   0.1133    
I(TFPM * 10^-6)  -1.651e+00  9.481e+00  -0.174   0.8621    
I(TICMS * 10^-6) -2.231e+00  1.060e+02  -0.021   0.9832    
I(CRED * 10^-6)  -2.095e+01  9.802e+00  -2.138   0.0345 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1434 on 124 degrees of freedom
Multiple R-squared:  0.465, Adjusted R-squared:  0.4046 
F-statistic: 7.699 on 14 and 124 DF,  p-value: 1.779e-11

reg1.SSERV <- lm(SSERV ~ SAGRO + SIND + LNYI_T_1 + SPUB + H + DD + DORC + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.SSERV)

Call:
lm(formula = SSERV ~ SAGRO + SIND + LNYI_T_1 + SPUB + H + DD + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.255771 -0.035530 -0.002765  0.034105  0.150766 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.006e-01  1.648e-01   4.252 4.13e-05 ***
SAGRO             1.572e-01  3.437e-02   4.573 1.15e-05 ***
SIND              3.094e-01  8.253e-02   3.749 0.000271 ***
LNYI_T_1         -9.190e-02  1.919e-02  -4.789 4.68e-06 ***
SPUB              6.434e-02  9.229e-02   0.697 0.486993    
H                -2.921e-03  9.198e-04  -3.175 0.001888 ** 
DD               -1.229e-04  2.505e-04  -0.491 0.624474    
DORC             -1.360e-05  1.353e-05  -1.005 0.316746    
T                 2.776e+00  3.520e-01   7.886 1.38e-12 ***
I(EXPOR * 10^-6)  4.111e+00  1.738e+00   2.365 0.019581 *  
I(IMPOR * 10^-6)  1.021e+01  6.207e+00   1.644 0.102645    
I(MREG * 10^-6)   1.614e+00  8.530e-01   1.892 0.060854 .  
I(TFPM * 10^-6)  -3.455e+00  3.910e+00  -0.884 0.378645    
I(TICMS * 10^-6)  1.258e+02  4.236e+01   2.970 0.003576 ** 
I(CRED * 10^-6)   1.464e+01  3.914e+00   3.741 0.000279 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.05934 on 124 degrees of freedom
Multiple R-squared:  0.7899,    Adjusted R-squared:  0.7662 
F-statistic:  33.3 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.SPUB <- lm(SPUB ~ SSERV + SAGRO + SIND + LNYI_T_1 + H + DD + DORC + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.SPUB)

Call:
lm(formula = SPUB ~ SSERV + SAGRO + SIND + LNYI_T_1 + H + DD + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.127622 -0.033520 -0.004577  0.024503  0.285854 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       9.712e-01  1.474e-01   6.588 1.14e-09 ***
SSERV             6.069e-02  8.704e-02   0.697  0.48699    
SAGRO             8.510e-02  3.526e-02   2.413  0.01727 *  
SIND              2.699e-01  8.103e-02   3.331  0.00114 ** 
LNYI_T_1         -1.091e-01  1.776e-02  -6.142 1.02e-08 ***
H                 1.142e-03  9.233e-04   1.237  0.21860    
DD               -1.021e-05  2.435e-04  -0.042  0.96661    
DORC              2.831e-05  1.295e-05   2.186  0.03069 *  
T                 1.597e-01  4.186e-01   0.381  0.70357    
I(EXPOR * 10^-6) -2.695e-01  1.725e+00  -0.156  0.87615    
I(IMPOR * 10^-6) -1.027e+01  6.023e+00  -1.705  0.09077 .  
I(MREG * 10^-6)  -1.624e-01  8.402e-01  -0.193  0.84708    
I(TFPM * 10^-6)  -9.515e-01  3.809e+00  -0.250  0.80313    
I(TICMS * 10^-6)  8.983e+01  4.181e+01   2.149  0.03360 *  
I(CRED * 10^-6)   2.790e-01  4.010e+00   0.070  0.94465    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.05763 on 124 degrees of freedom
Multiple R-squared:  0.6203,    Adjusted R-squared:  0.5774 
F-statistic: 14.47 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.H <- lm(H ~ SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + DD + DORC + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.H)

Call:
lm(formula = H ~ SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + DD + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
    Min      1Q  Median      3Q     Max 
-10.773  -3.610   0.288   3.172  15.032 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -3.832e+01  1.620e+01  -2.365 0.019558 *  
SPUB              1.067e+01  8.629e+00   1.237 0.218596    
SSERV            -2.575e+01  8.108e+00  -3.175 0.001888 ** 
SAGRO             5.418e-01  3.488e+00   0.155 0.876807    
SIND              4.117e+00  8.168e+00   0.504 0.615137    
LNYI_T_1          4.354e+00  1.922e+00   2.266 0.025215 *  
DD                2.879e-02  2.340e-02   1.230 0.220856    
DORC              6.823e-03  1.119e-03   6.097 1.26e-08 ***
T                 1.471e+02  3.828e+01   3.843 0.000193 ***
I(EXPOR * 10^-6)  3.319e+01  1.668e+02   0.199 0.842587    
I(IMPOR * 10^-6)  1.730e+03  5.682e+02   3.044 0.002853 ** 
I(MREG * 10^-6)   1.346e+01  8.123e+01   0.166 0.868613    
I(TFPM * 10^-6)  -1.075e+02  3.682e+02  -0.292 0.770786    
I(TICMS * 10^-6) -1.575e+04  3.865e+03  -4.075 8.15e-05 ***
I(CRED * 10^-6)   1.058e+03  3.759e+02   2.815 0.005670 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 5.571 on 124 degrees of freedom
Multiple R-squared:  0.6339,    Adjusted R-squared:  0.5926 
F-statistic: 15.34 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.DD <- lm(DD ~ H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + DORC + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.DD)

Call:
lm(formula = DD ~ H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + 
    DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
    Min      1Q  Median      3Q     Max 
-97.903  -6.240   0.119   5.582 164.373 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       6.148e+01  6.292e+01   0.977 0.330427    
H                 4.189e-01  3.405e-01   1.230 0.220856    
SPUB             -1.389e+00  3.311e+01  -0.042 0.966615    
SSERV            -1.577e+01  3.213e+01  -0.491 0.624474    
SAGRO            -3.075e+00  1.330e+01  -0.231 0.817551    
SIND             -7.971e+00  3.118e+01  -0.256 0.798648    
LNYI_T_1         -1.037e+01  7.422e+00  -1.398 0.164746    
DORC             -2.535e-03  4.861e-03  -0.521 0.603002    
T                 5.249e+02  1.471e+02   3.569 0.000511 ***
I(EXPOR * 10^-6) -1.977e+02  6.361e+02  -0.311 0.756467    
I(IMPOR * 10^-6) -8.741e+02  2.246e+03  -0.389 0.697762    
I(MREG * 10^-6)   5.096e+02  3.065e+02   1.663 0.098843 .  
I(TFPM * 10^-6)   7.768e+03  1.219e+03   6.370 3.35e-09 ***
I(TICMS * 10^-6) -6.884e+03  1.569e+04  -0.439 0.661586    
I(CRED * 10^-6)  -3.894e+03  1.437e+03  -2.710 0.007674 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 21.25 on 124 degrees of freedom
Multiple R-squared:  0.4953,    Adjusted R-squared:  0.4383 
F-statistic: 8.692 on 14 and 124 DF,  p-value: 6.92e-13

reg1.DORC <- lm(DORC ~ DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + T + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.DORC)

Call:
lm(formula = DORC ~ DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + 
    T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + 
    I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), data = dados)

Residuals:
    Min      1Q  Median      3Q     Max 
-798.94 -243.99  -23.48  190.30 1292.20 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       7.905e+02  1.163e+03   0.679  0.49810    
DD               -8.631e-01  1.655e+00  -0.521  0.60300    
H                 3.381e+01  5.544e+00   6.097 1.26e-08 ***
SPUB              1.311e+03  5.996e+02   2.186  0.03069 *  
SSERV            -5.942e+02  5.911e+02  -1.005  0.31675    
SAGRO            -4.876e-01  2.455e+02  -0.002  0.99842    
SIND              5.740e+02  5.732e+02   1.001  0.31861    
LNYI_T_1         -2.723e+00  1.380e+02  -0.020  0.98429    
T                -7.731e+03  2.764e+03  -2.797  0.00599 ** 
I(EXPOR * 10^-6) -1.835e+04  1.163e+04  -1.578  0.11708    
I(IMPOR * 10^-6) -2.901e+04  4.138e+04  -0.701  0.48459    
I(MREG * 10^-6)  -3.860e+03  5.708e+03  -0.676  0.50009    
I(TFPM * 10^-6)   3.314e+04  2.575e+04   1.287  0.20058    
I(TICMS * 10^-6)  2.185e+06  2.131e+05  10.252  < 2e-16 ***
I(CRED * 10^-6)  -2.571e+04  2.719e+04  -0.946  0.34615    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 392.2 on 124 degrees of freedom
Multiple R-squared:  0.7522,    Adjusted R-squared:  0.7242 
F-statistic: 26.89 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.T <- lm(T ~ DORC + DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + I(EXPOR * 
    10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.T)

Call:
lm(formula = T ~ DORC + DD + H + SPUB + SSERV + SAGRO + SIND + 
    LNYI_T_1 + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.029579 -0.006728 -0.000136  0.006746  0.044637 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -1.197e-01  3.511e-02  -3.409 0.000880 ***
DORC             -7.675e-06  2.744e-06  -2.797 0.005987 ** 
DD                1.774e-04  4.972e-05   3.569 0.000511 ***
H                 7.236e-04  1.883e-04   3.843 0.000193 ***
SPUB              7.339e-03  1.924e-02   0.381 0.703566    
SSERV             1.203e-01  1.526e-02   7.886 1.38e-12 ***
SAGRO            -2.177e-02  7.484e-03  -2.909 0.004300 ** 
SIND             -2.089e-02  1.804e-02  -1.158 0.249015    
LNYI_T_1          1.894e-02  4.003e-03   4.731 5.99e-06 ***
I(EXPOR * 10^-6) -2.984e-01  3.690e-01  -0.809 0.420232    
I(IMPOR * 10^-6) -1.775e+00  1.297e+00  -1.369 0.173395    
I(MREG * 10^-6)  -2.051e-01  1.792e-01  -1.144 0.254705    
I(TFPM * 10^-6)   8.743e-01  8.130e-01   1.075 0.284298    
I(TICMS * 10^-6)  7.040e+00  9.106e+00   0.773 0.440968    
I(CRED * 10^-6)   1.173e+00  8.533e-01   1.375 0.171682    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01236 on 124 degrees of freedom
Multiple R-squared:  0.8217,    Adjusted R-squared:  0.8016 
F-statistic: 40.83 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.EXPOR <- lm(I(EXPOR * 10^-6) ~ T + DORC + DD + H + SPUB + SSERV + SAGRO + SIND + 
    LNYI_T_1 + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + 
    I(CRED * 10^-6), data = dados)
summary(reg1.EXPOR)

Call:
lm(formula = I(EXPOR * 10^-6) ~ T + DORC + DD + H + SPUB + SSERV + 
    SAGRO + SIND + LNYI_T_1 + I(IMPOR * 10^-6) + I(MREG * 10^-6) + 
    I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0070810 -0.0013795  0.0001258  0.0011624  0.0182828 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -2.715e-02  8.574e-03  -3.167 0.001938 ** 
T                -1.758e-02  2.174e-02  -0.809 0.420232    
DORC             -1.073e-06  6.800e-07  -1.578 0.117077    
DD               -3.937e-06  1.267e-05  -0.311 0.756467    
H                 9.618e-06  4.833e-05   0.199 0.842587    
SPUB             -7.298e-04  4.673e-03  -0.156 0.876153    
SSERV             1.050e-02  4.440e-03   2.365 0.019581 *  
SAGRO            -3.531e-03  1.851e-03  -1.908 0.058744 .  
SIND              5.852e-03  4.370e-03   1.339 0.182942    
LNYI_T_1          3.000e-03  1.021e-03   2.939 0.003927 ** 
I(IMPOR * 10^-6)  1.410e+00  2.907e-01   4.852  3.6e-06 ***
I(MREG * 10^-6)  -4.442e-03  4.373e-02  -0.102 0.919250    
I(TFPM * 10^-6)  -7.272e-02  1.981e-01  -0.367 0.714259    
I(TICMS * 10^-6)  7.906e+00  2.099e+00   3.767 0.000254 ***
I(CRED * 10^-6)  -4.968e-02  2.087e-01  -0.238 0.812195    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.002999 on 124 degrees of freedom
Multiple R-squared:  0.7413,    Adjusted R-squared:  0.7121 
F-statistic: 25.38 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.IMPOR <- lm(I(IMPOR * 10^-6) ~ I(EXPOR * 10^-6) + T + DORC + DD + H + SPUB + 
    SSERV + SAGRO + SIND + LNYI_T_1 + I(MREG * 10^-6) + I(TFPM * 10^-6) + I(TICMS * 
    10^-6) + I(CRED * 10^-6), data = dados)
summary(reg1.IMPOR)

Call:
lm(formula = I(IMPOR * 10^-6) ~ I(EXPOR * 10^-6) + T + DORC + 
    DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), 
    data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0026011 -0.0002794 -0.0000641  0.0002327  0.0053520 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       5.954e-03  2.467e-03   2.413  0.01727 *  
I(EXPOR * 10^-6)  1.131e-01  2.332e-02   4.852  3.6e-06 ***
T                -8.389e-03  6.127e-03  -1.369  0.17340    
DORC             -1.361e-07  1.941e-07  -0.701  0.48459    
DD               -1.396e-06  3.587e-06  -0.389  0.69776    
H                 4.020e-05  1.321e-05   3.044  0.00285 ** 
SPUB             -2.230e-03  1.308e-03  -1.705  0.09077 .  
SSERV             2.091e-03  1.272e-03   1.644  0.10264    
SAGRO            -3.096e-04  5.310e-04  -0.583  0.56099    
SIND              2.477e-03  1.226e-03   2.019  0.04561 *  
LNYI_T_1         -6.935e-04  2.924e-04  -2.372  0.01923 *  
I(MREG * 10^-6)   1.974e-02  1.226e-02   1.610  0.10987    
I(TFPM * 10^-6)   1.021e-02  5.614e-02   0.182  0.85594    
I(TICMS * 10^-6)  3.157e-01  6.269e-01   0.504  0.61537    
I(CRED * 10^-6)  -4.281e-02  5.898e-02  -0.726  0.46935    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0008494 on 124 degrees of freedom
Multiple R-squared:  0.4935,    Adjusted R-squared:  0.4363 
F-statistic:  8.63 on 14 and 124 DF,  p-value: 8.45e-13

reg1.MREG <- lm(I(MREG * 10^-6) ~ I(IMPOR * 10^-6) + I(EXPOR * 10^-6) + T + DORC + 
    DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + I(TFPM * 10^-6) + I(TICMS * 
    10^-6) + I(CRED * 10^-6), data = dados)
summary(reg1.MREG)

Call:
lm(formula = I(MREG * 10^-6) ~ I(IMPOR * 10^-6) + I(EXPOR * 10^-6) + 
    T + DORC + DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + 
    I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6), data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.009773 -0.003721 -0.001170  0.002936  0.023653 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)      -3.035e-02  1.810e-02  -1.677   0.0961 .
I(IMPOR * 10^-6)  1.038e+00  6.445e-01   1.610   0.1099  
I(EXPOR * 10^-6) -1.873e-02  1.844e-01  -0.102   0.9192  
T                -5.095e-02  4.453e-02  -1.144   0.2547  
DORC             -9.521e-07  1.408e-06  -0.676   0.5001  
DD                4.281e-05  2.574e-05   1.663   0.0988 .
H                 1.645e-05  9.926e-05   0.166   0.8686  
SPUB             -1.854e-03  9.596e-03  -0.193   0.8471  
SSERV             1.738e-02  9.189e-03   1.892   0.0609 .
SAGRO            -6.088e-03  3.817e-03  -1.595   0.1133  
SIND             -1.152e-04  9.039e-03  -0.013   0.9899  
LNYI_T_1          5.189e-03  2.117e-03   2.451   0.0156 *
I(TFPM * 10^-6)  -2.437e-01  4.065e-01  -0.599   0.5500  
I(TICMS * 10^-6)  5.447e+00  4.524e+00   1.204   0.2308  
I(CRED * 10^-6)   2.933e-01  4.278e-01   0.686   0.4942  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.006159 on 124 degrees of freedom
Multiple R-squared:  0.4713,    Adjusted R-squared:  0.4116 
F-statistic: 7.897 on 14 and 124 DF,  p-value: 9.217e-12

reg1.TFPM <- lm(I(TFPM * 10^-6) ~ I(MREG * 10^-6) + I(IMPOR * 10^-6) + I(EXPOR * 
    10^-6) + T + DORC + DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + I(TICMS * 
    10^-6) + I(CRED * 10^-6), data = dados)
summary(reg1.TFPM)

Call:
lm(formula = I(TFPM * 10^-6) ~ I(MREG * 10^-6) + I(IMPOR * 10^-6) + 
    I(EXPOR * 10^-6) + T + DORC + DD + H + SPUB + SSERV + SAGRO + 
    SIND + LNYI_T_1 + I(TICMS * 10^-6) + I(CRED * 10^-6), data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0019392 -0.0005669 -0.0001720  0.0002811  0.0104615 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)       4.314e-03  4.019e-03   1.073   0.2852    
I(MREG * 10^-6)  -1.186e-02  1.978e-02  -0.599   0.5500    
I(IMPOR * 10^-6)  2.613e-02  1.436e-01   0.182   0.8559    
I(EXPOR * 10^-6) -1.492e-02  4.066e-02  -0.367   0.7143    
T                 1.057e-02  9.828e-03   1.075   0.2843    
DORC              3.977e-07  3.090e-07   1.287   0.2006    
DD                3.174e-05  4.983e-06   6.370 3.35e-09 ***
H                -6.391e-06  2.189e-05  -0.292   0.7708    
SPUB             -5.287e-04  2.116e-03  -0.250   0.8031    
SSERV            -1.811e-03  2.049e-03  -0.884   0.3786    
SAGRO            -1.481e-04  8.504e-04  -0.174   0.8621    
SIND              2.606e-03  1.980e-03   1.316   0.1906    
LNYI_T_1         -5.383e-04  4.757e-04  -1.132   0.2600    
I(TICMS * 10^-6)  1.968e-01  1.003e+00   0.196   0.8449    
I(CRED * 10^-6)   2.437e-01  9.197e-02   2.650   0.0091 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.001359 on 124 degrees of freedom
Multiple R-squared:  0.4404,    Adjusted R-squared:  0.3772 
F-statistic: 6.971 on 14 and 124 DF,  p-value: 2.114e-10

reg1.TICMS <- lm(I(TICMS * 10^-6) ~ I(TFPM * 10^-6) + I(MREG * 10^-6) + I(IMPOR * 
    10^-6) + I(EXPOR * 10^-6) + T + DORC + DD + H + SPUB + SSERV + SAGRO + SIND + 
    LNYI_T_1 + I(CRED * 10^-6), data = dados)
summary(reg1.TICMS)

Call:
lm(formula = I(TICMS * 10^-6) ~ I(TFPM * 10^-6) + I(MREG * 10^-6) + 
    I(IMPOR * 10^-6) + I(EXPOR * 10^-6) + T + DORC + DD + H + 
    SPUB + SSERV + SAGRO + SIND + LNYI_T_1 + I(CRED * 10^-6), 
    data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.423e-04 -7.760e-05  1.127e-05  5.651e-05  3.378e-04 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -1.803e-03  3.230e-04  -5.583 1.42e-07 ***
I(TFPM * 10^-6)   1.575e-03  8.034e-03   0.196 0.844850    
I(MREG * 10^-6)   2.122e-03  1.762e-03   1.204 0.230831    
I(IMPOR * 10^-6)  6.467e-03  1.284e-02   0.504 0.615372    
I(EXPOR * 10^-6)  1.299e-02  3.448e-03   3.767 0.000254 ***
T                 6.813e-04  8.813e-04   0.773 0.440968    
DORC              2.099e-07  2.048e-08  10.252  < 2e-16 ***
DD               -2.252e-07  5.133e-07  -0.439 0.661586    
H                -7.498e-06  1.840e-06  -4.075 8.15e-05 ***
SPUB              3.996e-04  1.860e-04   2.149 0.033600 *  
SSERV             5.279e-04  1.777e-04   2.970 0.003576 ** 
SAGRO            -1.602e-06  7.610e-05  -0.021 0.983237    
SIND             -5.895e-04  1.704e-04  -3.460 0.000740 ***
LNYI_T_1          1.987e-04  3.889e-05   5.110 1.19e-06 ***
I(CRED * 10^-6)  -1.716e-02  8.317e-03  -2.063 0.041232 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0001216 on 124 degrees of freedom
Multiple R-squared:  0.8573,    Adjusted R-squared:  0.8412 
F-statistic: 53.22 on 14 and 124 DF,  p-value: < 2.2e-16

reg1.CRED <- lm(I(CRED * 10^-6) ~ I(TICMS * 10^-6) + I(TFPM * 10^-6) + I(MREG * 10^-6) + 
    I(IMPOR * 10^-6) + I(EXPOR * 10^-6) + T + DORC + DD + H + SPUB + SSERV + SAGRO + 
    SIND + LNYI_T_1, data = dados)
summary(reg1.CRED)

Call:
lm(formula = I(CRED * 10^-6) ~ I(TICMS * 10^-6) + I(TFPM * 10^-6) + 
    I(MREG * 10^-6) + I(IMPOR * 10^-6) + I(EXPOR * 10^-6) + T + 
    DORC + DD + H + SPUB + SSERV + SAGRO + SIND + LNYI_T_1, data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-0.0024009 -0.0007871 -0.0003046  0.0004341  0.0044864 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)      -9.026e-03  3.749e-03  -2.408 0.017524 *  
I(TICMS * 10^-6) -1.934e+00  9.375e-01  -2.063 0.041232 *  
I(TFPM * 10^-6)   2.199e-01  8.299e-02   2.650 0.009100 ** 
I(MREG * 10^-6)   1.288e-02  1.878e-02   0.686 0.494246    
I(IMPOR * 10^-6) -9.882e-02  1.362e-01  -0.726 0.469347    
I(EXPOR * 10^-6) -9.199e-03  3.863e-02  -0.238 0.812195    
T                 1.280e-02  9.309e-03   1.375 0.171682    
DORC             -2.785e-07  2.945e-07  -0.946 0.346154    
DD               -1.436e-05  5.299e-06  -2.710 0.007674 ** 
H                 5.677e-05  2.017e-05   2.815 0.005670 ** 
SPUB              1.399e-04  2.011e-03   0.070 0.944655    
SSERV             6.926e-03  1.851e-03   3.741 0.000279 ***
SAGRO            -1.696e-03  7.935e-04  -2.138 0.034515 *  
SIND             -8.407e-04  1.892e-03  -0.444 0.657652    
LNYI_T_1          1.001e-03  4.453e-04   2.247 0.026392 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.001291 on 124 degrees of freedom
Multiple R-squared:  0.6264,    Adjusted R-squared:  0.5843 
F-statistic: 14.85 on 14 and 124 DF,  p-value: < 2.2e-16

3.1 Resumo dos \(R^2\) DAS REGRESSOES AUXILIARES

r2.LNYI_T_1 <- summary(reg1.LNYI_T_1)$r.squared
r2.SIND <- summary(reg1.SIND)$r.squared
r2.SAGRO <- summary(reg1.SAGRO)$r.squared
r2.SSERV <- summary(reg1.SSERV)$r.squared
r2.SPUB <- summary(reg1.SPUB)$r.squared
r2.H <- summary(reg1.H)$r.squared
r2.DD <- summary(reg1.DD)$r.squared
r2.DORC <- summary(reg1.DORC)$r.squared
r2.T <- summary(reg1.T)$r.squared
r2.EXPOR <- summary(reg1.EXPOR)$r.squared
r2.IMPOR <- summary(reg1.IMPOR)$r.squared
r2.MREG <- summary(reg1.MREG)$r.squared
r2.TFPM <- summary(reg1.TFPM)$r.squared
r2.TICMS <- summary(reg1.TICMS)$r.squared
r2.CRED <- summary(reg1.CRED)$r.squared

tabela <- rbind(r2.LNYI_T_1, r2.SIND, r2.SAGRO, r2.SSERV, r2.SPUB, r2.H, r2.DD, r2.DORC, 
    r2.T, r2.EXPOR, r2.IMPOR, r2.MREG, r2.TFPM, r2.TICMS, r2.CRED)
library(knitr)
kable(tabela, col.names = "R2")

	R2
r2.LNYI_T_1	0.8502561
r2.SIND	0.4315212
r2.SAGRO	0.4650231
r2.SSERV	0.7899224
r2.SPUB	0.6203105
r2.H	0.6338969
r2.DD	0.4952973
r2.DORC	0.7522043
r2.T	0.8217416
r2.EXPOR	0.7413223
r2.IMPOR	0.4934957
r2.MREG	0.4713308
r2.TFPM	0.4404069
r2.TICMS	0.8573237
r2.CRED	0.6264417

c(R2_mod1 = summary(mod1)$r.squared)

 R2_mod1 
0.890063 

4 Heterocedasticidade

4.1 Teste de White no modelo 1

# teste de White para heterocedasticidade, sem termos cruzados por causa do grau
# de liberdade do modelo (n=78obs)

m <- mod1
data <- dados
# rotina do teste com base em m e data
u2 <- m$residuals^2

# reg1<-lm(BARRO~LNYI_T_1+SIND+SAGRO+SSERV+SPUB+H+DD+DORC+T
# +I(EXPOR*10^-6)+I(IMPOR*10^-6)+I(MREG*10^-6)+I(TFPM*10^-6)+I(TICMS*10^-6)+I(CRED*10^-6),
# data=dados)

reg.auxiliar <- lm(u2 ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + DD + DORC + 
    T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 10^-6) + I(TFPM * 10^-6) + 
    I(TICMS * 10^-6) + I(CRED * 10^-6) + I(LNYI_T_1^2) + I(SIND^2) + I(SAGRO^2) + 
    I(SSERV^2) + I(SPUB^2) + I(H^2) + I(DD^2) + I(DORC^2) + I(T^2) + I((EXPOR * 10^-6)^2) + 
    I((IMPOR * 10^-6)^2) + I((MREG * 10^-6)^2) + I((TFPM * 10^-6)^2) + I((TICMS * 
    10^-6)^2) + I((CRED * 10^-6)^2), data = dados)
summary(reg.auxiliar)

Call:
lm(formula = u2 ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + 
    DD + DORC + T + I(EXPOR * 10^-6) + I(IMPOR * 10^-6) + I(MREG * 
    10^-6) + I(TFPM * 10^-6) + I(TICMS * 10^-6) + I(CRED * 10^-6) + 
    I(LNYI_T_1^2) + I(SIND^2) + I(SAGRO^2) + I(SSERV^2) + I(SPUB^2) + 
    I(H^2) + I(DD^2) + I(DORC^2) + I(T^2) + I((EXPOR * 10^-6)^2) + 
    I((IMPOR * 10^-6)^2) + I((MREG * 10^-6)^2) + I((TFPM * 10^-6)^2) + 
    I((TICMS * 10^-6)^2) + I((CRED * 10^-6)^2), data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-3.866e-04 -1.215e-04 -2.876e-05  3.989e-05  1.334e-03 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)  
(Intercept)          -1.097e-02  6.328e-03  -1.734   0.0858 .
LNYI_T_1              2.139e-03  1.320e-03   1.621   0.1080  
SIND                 -1.829e-04  5.802e-04  -0.315   0.7532  
SAGRO                -2.743e-04  2.804e-04  -0.978   0.3300  
SSERV                -5.981e-04  6.825e-04  -0.876   0.3827  
SPUB                  1.494e-03  9.921e-04   1.505   0.1351  
H                     6.335e-06  1.108e-05   0.572   0.5686  
DD                    5.351e-06  5.781e-06   0.926   0.3567  
DORC                  2.974e-07  1.899e-07   1.566   0.1203  
T                     2.862e-03  6.383e-03   0.448   0.6548  
I(EXPOR * 10^-6)      1.097e-03  1.905e-02   0.058   0.9542  
I(IMPOR * 10^-6)      7.625e-02  8.514e-02   0.896   0.3725  
I(MREG * 10^-6)       6.498e-03  1.814e-02   0.358   0.7208  
I(TFPM * 10^-6)      -1.728e-02  4.717e-02  -0.366   0.7149  
I(TICMS * 10^-6)     -3.359e-02  3.788e-01  -0.089   0.9295  
I(CRED * 10^-6)       5.388e-03  4.557e-02   0.118   0.9061  
I(LNYI_T_1^2)        -1.081e-04  6.939e-05  -1.559   0.1220  
I(SIND^2)            -1.535e-04  2.382e-03  -0.064   0.9487  
I(SAGRO^2)            6.544e-04  4.498e-04   1.455   0.1486  
I(SSERV^2)            1.167e-03  1.304e-03   0.895   0.3727  
 [ reached getOption("max.print") -- omitted 11 rows ]
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0002467 on 108 degrees of freedom
Multiple R-squared:  0.226, Adjusted R-squared:  0.01103 
F-statistic: 1.051 on 30 and 108 DF,  p-value: 0.4104

Ru2 <- summary(reg.auxiliar)$r.squared
LM <- nrow(data) * Ru2
# obtendo o numero de regressores menos o intercepto
k <- length(coefficients(reg.auxiliar)) - 1
k

[1] 30

p.value <- 1 - pchisq(LM, k)  # O TESTE TEM k TERMOS REGRESSORES EM reg.auxiliar
# c('LM','p.value')
#'Resultado do teste de White sem termos cruzados
c(LM = LM, p.value = p.value)

        LM    p.value 
31.4172947  0.3951057 

5 Modelo 2 com menos variáveis

# rodando com menos variaveis
mod2 <- lm(BARRO ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + Importa + TICMSm, 
    data = dados)
summary(mod2)

Call:
lm(formula = BARRO ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + 
    H + Importa + TICMSm, data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.038231 -0.007231 -0.000428  0.006942  0.036033 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.4758882  0.0330407  14.403  < 2e-16 ***
LNYI_T_1    -0.0515334  0.0037531 -13.731  < 2e-16 ***
SIND         0.1257862  0.0186154   6.757 4.27e-10 ***
SAGRO        0.1093928  0.0073513  14.881  < 2e-16 ***
SSERV        0.0496722  0.0116988   4.246 4.11e-05 ***
SPUB        -0.1362400  0.0198922  -6.849 2.67e-10 ***
H            0.0004369  0.0001632   2.677  0.00839 ** 
Importa      3.8473462  1.2070479   3.187  0.00180 ** 
TICMSm      50.2771919  6.0990409   8.243 1.58e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01303 on 130 degrees of freedom
Multiple R-squared:  0.8836,    Adjusted R-squared:  0.8764 
F-statistic: 123.3 on 8 and 130 DF,  p-value: < 2.2e-16

mod2$AIC <- AIC(mod2)
mod2$BIC <- BIC(mod2)
stargazer(mod1, mod2, title = "Título: Resultado da Regressão", align = TRUE, type = "text", 
    style = "all", keep.stat = c("aic", "bic", "rsq", "adj.rsq", "n"))

Título: Resultado da Regressão
================================================
                        Dependent variable:     
                    ----------------------------
                               BARRO            
                         (1)            (2)     
------------------------------------------------
LNYI_T_1              -0.051***      -0.052***  
                       (0.005)        (0.004)   
                     t = -11.037    t = -13.731 
                      p = 0.000      p = 0.000  
SIND                   0.128***      0.126***   
                       (0.019)        (0.019)   
                      t = 6.721      t = 6.757  
                      p = 0.000      p = 0.000  
SAGRO                  0.107***      0.109***   
                       (0.008)        (0.007)   
                      t = 13.187    t = 14.881  
                      p = 0.000      p = 0.000  
SSERV                  0.055***      0.050***   
                       (0.020)        (0.012)   
                      t = 2.772      t = 4.246  
                      p = 0.007     p = 0.00005 
SPUB                  -0.144***      -0.136***  
                       (0.020)        (0.020)   
                      t = -7.090    t = -6.849  
                      p = 0.000      p = 0.000  
H                       0.0003       0.0004***  
                       (0.0002)      (0.0002)   
                      t = 1.202      t = 2.677  
                      p = 0.232      p = 0.009  
DD                     0.00005                  
                       (0.0001)                 
                      t = 0.872                 
                      p = 0.385                 
DORC                   0.00000                  
                      (0.00000)                 
                      t = 1.601                 
                      p = 0.112                 
T                       0.020                   
                       (0.095)                  
                      t = 0.213                 
                      p = 0.832                 
Exporta                 -0.297                  
                       (0.390)                  
                      t = -0.761                
                      p = 0.448                 
Importa                4.759***      3.847***   
                       (1.377)        (1.207)   
                      t = 3.457      t = 3.187  
                      p = 0.001      p = 0.002  
Mregio                  -0.194                  
                       (0.190)                  
                      t = -1.023                
                      p = 0.309                 
FPM                    -1.464*                  
                       (0.861)                  
                      t = -1.701                
                      p = 0.092                 
TICMSm                45.070***      50.277***  
                       (9.618)        (6.099)   
                      t = 4.686      t = 8.243  
                     p = 0.00001     p = 0.000  
credito                 1.029                   
                       (0.906)                  
                      t = 1.135                 
                      p = 0.259                 
Constant               0.466***      0.476***   
                       (0.039)        (0.033)   
                      t = 12.050    t = 14.403  
                      p = 0.000      p = 0.000  
------------------------------------------------
Observations             139            139     
R2                      0.890          0.884    
Adjusted R2             0.877          0.876    
Akaike Inf. Crit.      -795.426      -801.461   
Bayesian Inf. Crit.    -745.540      -772.117   
================================================
Note:                *p<0.1; **p<0.05; ***p<0.01

5.1 Heterocedasticidade

5.1.1 Teste de White: mod2

# teste de White para heterocedasticidade, sem termos cruzados por causa do grau
# de liberdade do modelo (n=78obs)

m <- mod2
data <- dados
# rotina do teste com base em m e data
u2 <- m$residuals^2

# mod2<-lm(BARRO~LNYI_T_1+SIND+SAGRO+SSERV+SPUB+H +I(IMPOR*10^-6)+I(TICMS*10^-6),
# data=dados)

reg.auxiliar <- lm(u2 ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + I(IMPOR * 10^-6) + 
    I(TICMS * 10^-6) + I(LNYI_T_1^2) + I(SIND^2) + I(SAGRO^2) + I(SSERV^2) + I(SPUB^2) + 
    I(H^2) + I((IMPOR * 10^-6)^2) + I((TICMS * 10^-6)^2), data = dados)
summary(reg.auxiliar)

Call:
lm(formula = u2 ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + 
    I(IMPOR * 10^-6) + I(TICMS * 10^-6) + I(LNYI_T_1^2) + I(SIND^2) + 
    I(SAGRO^2) + I(SSERV^2) + I(SPUB^2) + I(H^2) + I((IMPOR * 
    10^-6)^2) + I((TICMS * 10^-6)^2), data = dados)

Residuals:
       Min         1Q     Median         3Q        Max 
-4.086e-04 -1.125e-04 -4.973e-05  3.466e-05  1.263e-03 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)
(Intercept)          -6.271e-03  5.703e-03  -1.100    0.274
LNYI_T_1              1.248e-03  1.186e-03   1.052    0.295
SIND                 -1.581e-04  5.627e-04  -0.281    0.779
SAGRO                -1.316e-04  2.434e-04  -0.541    0.590
SSERV                -4.672e-04  4.337e-04  -1.077    0.283
SPUB                  1.434e-03  9.900e-04   1.449    0.150
H                     4.082e-06  9.731e-06   0.419    0.676
I(IMPOR * 10^-6)      1.228e-01  8.075e-02   1.521    0.131
I(TICMS * 10^-6)      2.136e-01  2.744e-01   0.779    0.438
I(LNYI_T_1^2)        -6.218e-05  6.152e-05  -1.011    0.314
I(SIND^2)             8.270e-04  2.066e-03   0.400    0.690
I(SAGRO^2)            3.149e-04  4.290e-04   0.734    0.464
I(SSERV^2)            5.858e-04  9.808e-04   0.597    0.551
I(SPUB^2)            -1.148e-03  1.497e-03  -0.767    0.445
I(H^2)               -1.250e-07  1.914e-07  -0.653    0.515
I((IMPOR * 10^-6)^2) -1.365e+01  8.659e+00  -1.576    0.118
I((TICMS * 10^-6)^2)  2.194e+01  1.724e+02   0.127    0.899

Residual standard error: 0.0002587 on 122 degrees of freedom
Multiple R-squared:  0.1514,    Adjusted R-squared:  0.04008 
F-statistic:  1.36 on 16 and 122 DF,  p-value: 0.173

Ru2 <- summary(reg.auxiliar)$r.squared
LM <- nrow(data) * Ru2
# obtendo o numero de regressores menos o intercepto
k <- length(coefficients(reg.auxiliar)) - 1
k

[1] 16

p.value <- 1 - pchisq(LM, k)  # O TESTE TEM k TERMOS REGRESSORES EM reg.auxiliar

#'Resultado do teste de White sem termos cruzados
c(LM = LM, p.value = p.value)

        LM    p.value 
21.0413499  0.1769266 

Ou pelo bptest:

bptest(mod2, ~LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + H + I(IMPOR * 10^-6) + I(TICMS * 
    10^-6) + I(LNYI_T_1^2) + I(SIND^2) + I(SAGRO^2) + I(SSERV^2) + I(SPUB^2) + I(H^2) + 
    I((IMPOR * 10^-6)^2) + I((TICMS * 10^-6)^2), data = dados)

    studentized Breusch-Pagan test

data:  mod2
BP = 21.041, df = 16, p-value = 0.1769

5.1.2 Correção de Var-cov conforme White

# mod2<-lm(BARRO~LNYI_T_1+SIND+SAGRO+SSERV+SPUB+H +I(IMPOR*10^-6)+I(TICMS*10^-6),
# data=dados) library(car) possibilidades:
# hccm(regressao1,type=c('hc0','hc1','hc2','hc3','hc4'))
vcov.white0 <- hccm(mod2, type = c("hc1"))
# 
coeftest(mod2, vcov.white0)

t test of coefficients:

               Estimate  Std. Error  t value  Pr(>|t|)    
(Intercept)  0.47588824  0.03277583  14.5195 < 2.2e-16 ***
LNYI_T_1    -0.05153343  0.00380593 -13.5403 < 2.2e-16 ***
SIND         0.12578625  0.02055138   6.1206 1.024e-08 ***
SAGRO        0.10939283  0.00884334  12.3701 < 2.2e-16 ***
SSERV        0.04967220  0.01090307   4.5558 1.188e-05 ***
SPUB        -0.13624003  0.02609054  -5.2218 6.838e-07 ***
H            0.00043686  0.00015281   2.8589  0.004954 ** 
Importa      3.84734619  1.26552423   3.0401  0.002860 ** 
TICMSm      50.27719190  7.21006346   6.9732 1.407e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.1.2.1 Revendo a saída do modelo 2 sem correcao de White

summary(mod2)

Call:
lm(formula = BARRO ~ LNYI_T_1 + SIND + SAGRO + SSERV + SPUB + 
    H + Importa + TICMSm, data = dados)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.038231 -0.007231 -0.000428  0.006942  0.036033 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.4758882  0.0330407  14.403  < 2e-16 ***
LNYI_T_1    -0.0515334  0.0037531 -13.731  < 2e-16 ***
SIND         0.1257862  0.0186154   6.757 4.27e-10 ***
SAGRO        0.1093928  0.0073513  14.881  < 2e-16 ***
SSERV        0.0496722  0.0116988   4.246 4.11e-05 ***
SPUB        -0.1362400  0.0198922  -6.849 2.67e-10 ***
H            0.0004369  0.0001632   2.677  0.00839 ** 
Importa      3.8473462  1.2070479   3.187  0.00180 ** 
TICMSm      50.2771919  6.0990409   8.243 1.58e-13 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.01303 on 130 degrees of freedom
Multiple R-squared:  0.8836,    Adjusted R-squared:  0.8764 
F-statistic: 123.3 on 8 and 130 DF,  p-value: < 2.2e-16

5.1.3 Saída do stargazer com modelo 1 e modelo 2 (com e sem correção de White)

cov <- vcov.white0
robust.se <- sqrt(diag(cov))

stargazer(mod1, mod2, mod2, se = list(NULL, NULL, robust.se), column.labels = c("MQO-mod1", 
    "MQO-mod2", "robusto"), title = "Título: Resultado da Regressão", align = TRUE, 
    type = "text", style = "all", keep.stat = c("aic", "bic", "rsq", "adj.rsq", "n"))

Título: Resultado da Regressão
=======================================================
                            Dependent variable:        
                    -----------------------------------
                                   BARRO               
                     MQO-mod1    MQO-mod2     robusto  
                        (1)         (2)         (3)    
-------------------------------------------------------
LNYI_T_1             -0.051***   -0.052***   -0.052*** 
                      (0.005)     (0.004)     (0.004)  
                    t = -11.037 t = -13.731 t = -13.540
                     p = 0.000   p = 0.000   p = 0.000 
SIND                 0.128***    0.126***    0.126***  
                      (0.019)     (0.019)     (0.021)  
                     t = 6.721   t = 6.757   t = 6.121 
                     p = 0.000   p = 0.000   p = 0.000 
SAGRO                0.107***    0.109***    0.109***  
                      (0.008)     (0.007)     (0.009)  
                    t = 13.187  t = 14.881  t = 12.370 
                     p = 0.000   p = 0.000   p = 0.000 
SSERV                0.055***    0.050***    0.050***  
                      (0.020)     (0.012)     (0.011)  
                     t = 2.772   t = 4.246   t = 4.556 
                     p = 0.007  p = 0.00005 p = 0.00001
SPUB                 -0.144***   -0.136***   -0.136*** 
                      (0.020)     (0.020)     (0.026)  
                    t = -7.090  t = -6.849  t = -5.222 
                     p = 0.000   p = 0.000  p = 0.00000
H                     0.0003     0.0004***   0.0004*** 
                     (0.0002)    (0.0002)    (0.0002)  
                     t = 1.202   t = 2.677   t = 2.859 
                     p = 0.232   p = 0.009   p = 0.005 
DD                    0.00005                          
                     (0.0001)                          
                     t = 0.872                         
                     p = 0.385                         
DORC                  0.00000                          
                     (0.00000)                         
                     t = 1.601                         
                     p = 0.112                         
T                      0.020                           
                      (0.095)                          
                     t = 0.213                         
                     p = 0.832                         
Exporta               -0.297                           
                      (0.390)                          
                    t = -0.761                         
                     p = 0.448                         
Importa              4.759***    3.847***    3.847***  
                      (1.377)     (1.207)     (1.266)  
                     t = 3.457   t = 3.187   t = 3.040 
                     p = 0.001   p = 0.002   p = 0.003 
Mregio                -0.194                           
                      (0.190)                          
                    t = -1.023                         
                     p = 0.309                         
FPM                   -1.464*                          
                      (0.861)                          
                    t = -1.701                         
                     p = 0.092                         
TICMSm               45.070***   50.277***   50.277*** 
                      (9.618)     (6.099)     (7.210)  
                     t = 4.686   t = 8.243   t = 6.973 
                    p = 0.00001  p = 0.000   p = 0.000 
credito                1.029                           
                      (0.906)                          
                     t = 1.135                         
                     p = 0.259                         
Constant             0.466***    0.476***    0.476***  
                      (0.039)     (0.033)     (0.033)  
                    t = 12.050  t = 14.403  t = 14.519 
                     p = 0.000   p = 0.000   p = 0.000 
-------------------------------------------------------
Observations            139         139         139    
R2                     0.890       0.884       0.884   
Adjusted R2            0.877       0.876       0.876   
Akaike Inf. Crit.    -795.426    -801.461    -801.461  
Bayesian Inf. Crit.  -745.540    -772.117    -772.117  
=======================================================
Note:                       *p<0.1; **p<0.05; ***p<0.01

5.2 Autocorrelação dos resíduos (modelos 1 e 2)

library(car)
library(lmtest)
library(sandwich)

dw.mod2 <- dwtest(mod2)
dw.mod2

    Durbin-Watson test

data:  mod2
DW = 2.0758, p-value = 0.6859
alternative hypothesis: true autocorrelation is greater than 0

dw.mod1 <- dwtest(mod1)
dw.mod1

    Durbin-Watson test

data:  mod1
DW = 2.0732, p-value = 0.685
alternative hypothesis: true autocorrelation is greater than 0

Fiz uma rotina para rodar vários BGtest até ordem 12. Fiz para o modelo 2.

# padrao do teste de BG, com distribuição qui-quadrado
bgorder = 1:12  # definindo até a máxima ordem do bgtest
d = NULL
for (p in bgorder) {
    bgtest.chi <- bgtest(mod2, order = p, type = c("Chisq"), data = dados)
    print(bgtest.chi)
    d = rbind(d, data.frame(bgtest.chi$statistic, bgtest.chi$p.value))
}

    Breusch-Godfrey test for serial correlation of order up to 1

data:  mod2
LM test = 0.21823, df = 1, p-value = 0.6404


    Breusch-Godfrey test for serial correlation of order up to 2

data:  mod2
LM test = 0.88901, df = 2, p-value = 0.6411


    Breusch-Godfrey test for serial correlation of order up to 3

data:  mod2
LM test = 0.90746, df = 3, p-value = 0.8236


    Breusch-Godfrey test for serial correlation of order up to 4

data:  mod2
LM test = 1.9776, df = 4, p-value = 0.7399


    Breusch-Godfrey test for serial correlation of order up to 5

data:  mod2
LM test = 3.5133, df = 5, p-value = 0.6214


    Breusch-Godfrey test for serial correlation of order up to 6

data:  mod2
LM test = 3.7397, df = 6, p-value = 0.7118


    Breusch-Godfrey test for serial correlation of order up to 7

data:  mod2
LM test = 4.3217, df = 7, p-value = 0.7421


    Breusch-Godfrey test for serial correlation of order up to 8

data:  mod2
LM test = 5.751, df = 8, p-value = 0.6751


    Breusch-Godfrey test for serial correlation of order up to 9

data:  mod2
LM test = 9.2059, df = 9, p-value = 0.4185


    Breusch-Godfrey test for serial correlation of order up to 10

data:  mod2
LM test = 13.135, df = 10, p-value = 0.2162


    Breusch-Godfrey test for serial correlation of order up to 11

data:  mod2
LM test = 13.447, df = 11, p-value = 0.2651


    Breusch-Godfrey test for serial correlation of order up to 12

data:  mod2
LM test = 13.448, df = 12, p-value = 0.3373

d

Não concluiu por autocorrelação residual!

5.3 Teste de Jarque-Bera para normalidade (modelo 2)

u.hat <- resid(mod2)
# library(tseries)
JB.mod2 <- jarque.bera.test(u.hat)
JB.mod2

    Jarque Bera Test

data:  u.hat
X-squared = 3.4026, df = 2, p-value = 0.1824

5.4 Teste RESET de Ramsey com potencias de 2 e 3 (modelo 2)

TesteRESET.power <- lmtest::resettest(mod2, power = 2:3)
TesteRESET.power

    RESET test

data:  mod2
RESET = 7.4622, df1 = 2, df2 = 128, p-value = 0.0008603

5.5 Investigação de outliers - teste de Bonferroni para outlier (modelo 2)

outlierTest(mod2)

No Studentized residuals with Bonferroni p < 0.05
Largest |rstudent|:
    rstudent unadjusted p-value Bonferroni p
58 -3.125207          0.0021953      0.30514

qqPlot(mod2)

[1]  58 121

vif(mod2)

LNYI_T_1     SIND    SAGRO    SSERV     SPUB        H  Importa   TICMSm 
4.470832 1.666601 1.517527 1.675043 2.527361 1.648744 1.515172 2.812693 

O outlier 58 é o município de Juruena.

Referências

MARQUEZIN, William Ricardo. O Fundo de Participação dos Municípios e sua contribuição para a redução da desigualdade econômica em Mato Grosso. Universidade Federal de Mato Grosso, Faculdade de Economia, Programa de Pós-Graduação em Agronegócio e Desenvolvimento Regional. UFMT: Cuiabá-MT, 2014. Dissertação (Mestrado). Disponível em: https://www.ufmt.br/adr/arquivos/6b93f9815cfad275fb05f3502deffda6.pdf.