@import "./styles.css";


:root{
    --h-title-color:#3948d2;
}

.header {
    border-bottom: solid 2px var(--bs-blue);
    z-index: 10000;
}

.bg-bottom {
    background-color: var(--h-title-color);
}

.top-section {
    position: relative;
    padding: 10rem 0;
    background-position: center;
    background-size: cover;
    height: 25rem;
}

.top-section h2 {
    font-weight: 400;
}

.top-section .top-section-content {
    position: relative;
    z-index: 1;
}

.top-section:before {
    content: "";
    position: absolute;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    background: rgba(0, 0, 0, 0.5);
}

section header h2{
    font-family: "Kanit", -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji" !important;
    color: var(--h-title-color);
    font-size: 2.3rem;
    line-height: 4rem;
}

section .main-body{
    font-size: 1.3rem;
    font-weight: 200;
    line-height: 2.4rem;
}


.bg-gradient-primary-to-secondary-light {
    background: linear-gradient(45deg, #fcfcfc, #ffffff) !important;
}

.bg-gradient-primary-to-secondary-gray {
    background: linear-gradient(45deg, #f0f0f0, #f1f1f1) !important;
}



.on-top {
    position: relative;
    z-index: 10000;
}

.top-bg {
    background-position: center;
    background-size: fill;
    width: 100%;
    height: 100%;
}

.top-bg::before {
    content: "";
    position: absolute;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    background: rgba(0, 0, 0, 0.4);
}


#avatar {
    position: relative;
    height: 0;
    top:min(-65px,max(-100px,calc(-1*100vw/12)));
    max-width: 55em;
    margin: 0 1.5rem 0 0;
    float: right;
    z-index: 9000;
}

#avatar img {
    height: max(130px,min(200px,calc(100vw/6)));
    box-shadow: 5px 5px 5px rgba(0, 0, 0, 0.2);
}

@media screen and (max-width: 991px) {
    #avatar {
        margin: 0;
    }
}
library(readxl)



# Keep only observations where vd_regime == 1
df <- df %>% filter(vd_regime == 1)

# Convert necessary variables to numeric (force conversion)
df <- df %>%
  mutate(across(c(m1, m2, inflation, cbi_w_garriga, credit, 
                  inflation_latest, MilitaryexpenditureofGDP, 
                  GDP_perca_constant, GDPgrowthannual, fix, 
                  Ygap, fiscal_bal_gdp, pg, TradeofGDP, 
                  Coverageofsocialinsurancepro, 
                  Coverageofsocialsafetynetpr), 
                as.numeric, .names = "clean_{.col}"))

# Correlation matrix with significance levels
cor_matrix <- cor(df[, c("m1", "m2", "inflation", "cbi_w_garriga", "credit")], use="pairwise.complete.obs")
print(cor_matrix)

# Encoding country_name as a factor and setting panel data structure
df$country_id <- as.numeric(as.factor(df$country_name))
df <- pdata.frame(df, index = c("country_id", "year"))

# Difference-GMM Estimation using pgmm()
gmm_model1 <- pgmm(
  m1 ~ lag(m1, 1) + ele + inflation_latest + fix + Ygap + credit + cbi_w_garriga |
    lag(m1, 1:75) + lag(inflation_latest, 1:17),
  data = df, effect = "individual", model = "twosteps"
)
summary(gmm_model1)


# Second GMM Model with additional variable ele_p
gmm_model2 <- pgmm(
  m1 ~ lag(m1, 1) + ele + ele_p + inflation_latest + fix + Ygap + credit + cbi_w_garriga |
    lag(m1, 1:75) + lag(inflation_latest, 1:17),
  data = df, effect = "individual", model = "twosteps"
)
summary(gmm_model2)

library(plm)
library(AER)
library(pgmm)