sanctions_excess_inflation.R

library(dplyr)
library(readxl)
library(xts)
library(DBI)
library(fUnitRoots)
library(urca)
library(vars)
library(aod)
library(zoo)
library(tseries)
library(bootUR)

# Function to only return positive values
positive_values <- function(x) pmax(x,0)

# Prevent scientific notation
options(scipen=999)

# Connect to DB
data_path <- file.path(getwd(), "elaborations")
cpi_data <- "cpi_dest"
target <- "sanctions_all_excess_inflation"
target_fin <- "sanctions_fin_excess_inflation"
target_trade <- "sanctions_trade_excess_inflation"


con_cpi <- dbConnect(RSQLite::SQLite(),
                     dbname = file.path(data_path, paste0(cpi_data, ".db")))

con_target <- dbConnect(RSQLite::SQLite(),
                        dbname = file.path(data_path, paste0(target, ".db")))

con_target_fin <- dbConnect(RSQLite::SQLite(),
                            dbname = file.path(data_path, paste0(target_fin, ".db")))

con_target_trade <- dbConnect(RSQLite::SQLite(),
                              dbname = file.path(data_path, paste0(target_trade, ".db")))

# Get sanctions data
sanctions_df <- read_excel("official_data/russia-sanction-timeline.xlsx",
                           col_types = c("date", "text", "text"))


sanctions_all_df <- sanctions_df %>%
    group_by(week = cut(Date, "week")) %>%
    mutate(date = as.Date(week) + 6)  %>%  # Move reference to the end of the week
    mutate(total=n()) %>%
    ungroup() %>%
    distinct(date, total) %>%
    filter(date < as.Date("2022-10-10"))  # Removing data after CPI cutoff...to be updated over time

sanctions_fin_df <- sanctions_df %>%
    filter(`Sanction type` == "financial") %>%
    group_by(week = cut(Date, "week")) %>%
    mutate(date = as.Date(week) + 6)  %>%  # Move reference to the end of the week
    mutate(total=n()) %>%
    ungroup() %>%
    distinct(date, total) %>%
    filter(date < as.Date("2022-10-10"))  # Removing data after CPI cutoff...to be updated over time

sanctions_trade_df <- sanctions_df %>%
    filter(!`Sanction type` == "financial") %>%
    group_by(week = cut(Date, "week")) %>%
    mutate(date = as.Date(week) + 6)  %>%  # Move reference to the end of the week
    mutate(total=n()) %>%
    ungroup() %>%
    distinct(date, total) %>%
    filter(date < as.Date("2022-10-10"))  # Removing data after CPI cutoff...to be updated over time


sanctions_all_ts <- as.xts(subset(sanctions_all_df, select=-c(date)), order.by = sanctions_all_df$date)
# add missing dates
sanctions_all_ts <- merge(sanctions_all_ts, seq.Date(as.Date("2021-02-21"), as.Date("2022-10-09"), by = "week"))
sanctions_all_ts <- na.fill(sanctions_all_ts, 0)

sanctions_fin_ts <- as.xts(subset(sanctions_fin_df, select=-c(date)), order.by = sanctions_fin_df$date)
# add missing dates
sanctions_fin_ts <- merge(sanctions_fin_ts, seq.Date(as.Date("2021-02-21"), as.Date("2022-10-09"), by = "week"))
sanctions_fin_ts <- na.fill(sanctions_fin_ts, 0)

sanctions_trade_ts <- as.xts(subset(sanctions_trade_df, select=-c(date)), order.by = sanctions_trade_df$date)
# add missing dates
sanctions_trade_ts <- merge(sanctions_trade_ts, seq.Date(as.Date("2021-02-21"), as.Date("2022-10-09"), by = "week"))
sanctions_trade_ts <- na.fill(sanctions_trade_ts, 0)


# Get excess inflation for CPI
cpi_df <- data.frame(date=as.Date("2021-02-21"))
for (table in dbListTables(con_cpi)) {
    if (!endsWith(table, "official")) {  # Only non-official figures
        df <- dbReadTable(con_cpi, table)
        df$date <- as.Date(df$date, format = "%Y-%m-%d")
        df <- df %>% dplyr::select(date, cpi_diff)
        colnames(df) <- c("date", table)
        cpi_df <- full_join(cpi_df, df, by= "date")
    }
}



cpi_ts <-as.xts(subset(cpi_df, select=-c(date)), order.by = cpi_df$date)

cpi_ts$average <- rowMeans(cpi_ts)
# Replace all negative numbers with zero
cpi_ts <- apply(cpi_ts, c(1,2), positive_values)
cpi_ts <- as.xts(cpi_ts, order.by = cpi_df$date) # Need to reorder again by date


##### ALL SANCTIONS #####
for (tab in names(cpi_ts)){
    tryCatch({
        ##### CPI TY Causality #####
        # Check order of integration for the time series
        cpi_order_integration <- order_integration(
            merge(sanctions_all_ts$total, cpi_ts[,tab]),
            max_order = 5)
        # Select max order of integration
        var_cpi_select <- VARselect(merge(sanctions_all_ts$total, cpi_ts[,tab]),
                                    lag.max = 12,
                                    type = "both")
        # Selecting the lag for VAR
        AIC_cpi <- var_cpi_select$selection[[1]]
        cpi_max_ord <- max(cpi_order_integration$order_int)
        total_cpi_lag <- AIC_cpi + cpi_max_ord

        # Performing VAR
        var_cpi <- VAR(
            merge(sanctions_all_ts$total, cpi_ts[,tab]),
            p=total_cpi_lag,
            type="both")


        cpi_serial <- serial.test(var_cpi, type="BG", lags.pt = 52, lags.bg=52)
        if(1/roots(var_cpi)[[1]] > 1 || 1/roots(var_cpi)[[2]] > 1){
            roots_cpi <- "stable"
        } else {
            roots_cpi <- "not stable"
        }

        # Causality from first column (sanctions) to second (breaks)
        wt2_cpi<-wald.test(
            b=coef(var_cpi$varresult[[2]]),
            Sigma=vcov(var_cpi$varresult[[2]]),
            Terms=c(seq(1, AIC_cpi*2, 2)))
        cpi_causality_p <- wt2_cpi[["result"]][["chi2"]][["P"]]


        # Saving results
        results_df <- data.frame(
            item = c("cpi"),
            aic_lag = c(AIC_cpi),
            max_int_order = c(cpi_max_ord),
            serial_ac_p = c(cpi_serial$serial$p.value),
            root = c(roots_cpi),
            causality_p = c(cpi_causality_p)
        )
        dbWriteTable(con_target, tab, results_df, overwrite = TRUE)
    }, error = function(e) {
        cat("Error for category ", tab, ": ", conditionMessage(e), "\n")
        #next
    })
}

##### FINANCIAL SANCTIONS #####
for (tab in names(cpi_ts)){
    tryCatch({
        ##### CPI TY Causality #####
        # Check order of integration for the time series
        cpi_order_integration <- order_integration(
            merge(sanctions_fin_ts$total, cpi_ts[,tab]),
            max_order = 5)
        # Select max order of integration
        var_cpi_select <- VARselect(merge(sanctions_fin_ts$total, cpi_ts[,tab]),
                                    lag.max = 12,
                                    type = "both")
        # Selecting the lag for VAR
        AIC_cpi <- var_cpi_select$selection[[1]]
        cpi_max_ord <- max(cpi_order_integration$order_int)
        total_cpi_lag <- AIC_cpi + cpi_max_ord

        # Performing VAR
        var_cpi <- VAR(
            merge(sanctions_fin_ts$total, cpi_ts[,tab]),
            p=total_cpi_lag,
            type="both")


        cpi_serial <- serial.test(var_cpi, type="BG", lags.pt = 52, lags.bg=52)
        if(1/roots(var_cpi)[[1]] > 1 || 1/roots(var_cpi)[[2]] > 1){
            roots_cpi <- "stable"
        } else {
            roots_cpi <- "not stable"
        }

        # Causality from first column (sanctions) to second (breaks)
        wt2_cpi<-wald.test(
            b=coef(var_cpi$varresult[[2]]),
            Sigma=vcov(var_cpi$varresult[[2]]),
            Terms=c(seq(1, AIC_cpi*2, 2)))
        cpi_causality_p <- wt2_cpi[["result"]][["chi2"]][["P"]]

        # Saving results
        results_df <- data.frame(
            item = c("cpi"),
            aic_lag = c(AIC_cpi),
            max_int_order = c(cpi_max_ord),
            serial_ac_p = c(cpi_serial$serial$p.value),
            root = c(roots_cpi),
            causality_p = c(cpi_causality_p)
        )
        dbWriteTable(con_target_fin, tab, results_df, overwrite = TRUE)
    }, error = function(e) {
        cat("Error for category ", tab, ": ", conditionMessage(e), "\n")
        #next
    })
}

##### TRADE SANCTIONS #####
for (tab in names(cpi_ts)){
    tryCatch({
        ##### CPI TY Causality #####
        # Check order of integration for the time series
        cpi_order_integration <- order_integration(
            merge(sanctions_trade_ts$total, cpi_ts[,tab]),
            max_order = 5)
        # Select max order of integration
        var_cpi_select <- VARselect(merge(sanctions_trade_ts$total, cpi_ts[,tab]),
                                    lag.max = 12,
                                    type = "both")
        # Selecting the lag for VAR
        AIC_cpi <- var_cpi_select$selection[[1]]
        cpi_max_ord <- max(cpi_order_integration$order_int)
        total_cpi_lag <- AIC_cpi + cpi_max_ord

        # Performing VAR
        var_cpi <- VAR(
            merge(sanctions_trade_ts$total, cpi_ts[,tab]),
            p=total_cpi_lag,
            type="both")


        cpi_serial <- serial.test(var_cpi, type="BG", lags.pt = 52, lags.bg=52)
        if(1/roots(var_cpi)[[1]] > 1 || 1/roots(var_cpi)[[2]] > 1){
            roots_cpi <- "stable"
        } else {
            roots_cpi <- "not stable"
        }

        # Causality from first column (sanctions) to second (breaks)
        wt2_cpi<-wald.test(
            b=coef(var_cpi$varresult[[2]]),
            Sigma=vcov(var_cpi$varresult[[2]]),
            Terms=c(seq(1, AIC_cpi*2, 2)))
        cpi_causality_p <- wt2_cpi[["result"]][["chi2"]][["P"]]

        # Saving results
        results_df <- data.frame(
            item = c("cpi"),
            aic_lag = c(AIC_cpi),
            max_int_order = c(cpi_max_ord),
            serial_ac_p = c(cpi_serial$serial$p.value),
            root = c(roots_cpi),
            causality_p = c(cpi_causality_p)
        )
        dbWriteTable(con_target_trade, tab, results_df, overwrite = TRUE)
    }, error = function(e) {
        cat("Error for category ", tab, ": ", conditionMessage(e), "\n")
        #next
    })
}


# Disconnect from DB
dbDisconnect(conn = con_cpi)
dbDisconnect(conn = con_target)
dbDisconnect(conn = con_target_fin)
dbDisconnect(conn = con_target_trade)