In MichaelGurkov/FinSynch:
raw_data = list()
raw_data$HousePrice = import.bis.property.price.data(
countries_vec = countries_list$oecd_countries) %>%
mutate(Date = as.yearqtr(Date, format = "%Y-Q%q"))
raw_data$TotalCredit = import.bis.tot.credit.data(
countries_vec = countries_list$oecd_countries) %>%
mutate(Date = as.yearqtr(Date, format = "%Y-Q%q"))
raw_data$WDI_annual = import_wdi_df(
countries_vec = countries_list$oecd_countries) %>%
mutate(GDP = GDP * 10 ^ (-6)) # Convert to millions USD
raw_data$bis_lbs = import.bis.lbs.data(
countries_vec = countries_list$oecd_countries) %>%
mutate(Date = as.yearqtr(Date, format = "%Y-Q%q"))
raw_data$codes = read.csv(paste0("C:\\Users\\Misha\\Documents",
"\\Data\\ISO\\",
"iso_2digit_alpha_country",
"_codes.csv")) %>%
setNames(c("Code","Country"))
eu_df = read.csv(paste0("C:\\Users\\Misha\\Documents\\",
"Data\\Misc\\EU_membership.csv"),
stringsAsFactors = FALSE) %>%
setNames(c("Country","Euro_area","EU")) %>%
mutate(Country = gsub("\\s","_",Country)) %>%
mutate(Country = sub("The_Netherlands","Netherlands",
Country,fixed = TRUE)) %>%
mutate(Country = sub("Slovakia","Slovak_Republic", Country,fixed = TRUE))
eu_dates_vec = seq.Date(
from = as.Date(min(raw_data$bis_lbs$Date)),
to = as.Date(max(raw_data$bis_lbs$Date)),
by = "year") %>%
format(.,"%Y")
raw_data$EU_both = construct_countrypair_EU_index(
eu_df = eu_df %>%
select(Country, EU) %>%
rename(Date = EU),
dates_vec = eu_dates_vec,
countries = countries_list$oecd_countries) %>%
rename(EU_both = Status) %>%
mutate(Date = as.character(Date))
raw_data$EU_one = construct_countrypair_EU_index(
eu_df = eu_df %>%
select(Country, EU) %>%
rename(Date = EU),
dates_vec = eu_dates_vec,
countries = countries_list$oecd_countries,
index_status = "one") %>%
rename(EU_one = Status) %>%
mutate(Date = as.character(Date))
raw_data$Euro_both = construct_countrypair_EU_index(
eu_df = eu_df %>%
select(Country, Euro_area) %>%
rename(Date = Euro_area) %>%
filter(!Date == "Not a member"),
dates_vec = eu_dates_vec,
countries = countries_list$oecd_countries) %>%
rename(Euro_both = Status) %>%
mutate(Date = as.character(Date))
raw_data$Euro_one = construct_countrypair_EU_index(
eu_df = eu_df %>%
select(Country, Euro_area) %>%
rename(Date = Euro_area),
dates_vec = eu_dates_vec,
countries = countries_list$oecd_countries,
index_status = "one") %>%
rename(Euro_one = Status) %>%
mutate(Date = as.character(Date))
rm(eu_df, eu_dates_vec)
raw_data$geodist = import.geodist.data() %>%
filter(Country %in% countries_list$oecd_countries) %>%
filter(Counter_Country %in% countries_list$oecd_countries) %>%
mutate(CountryPair = ifelse(Country < Counter_Country,
paste(Country, Counter_Country, sep = "-"),
paste(Counter_Country,Country, sep = "-"))) %>%
select(-Country, -Counter_Country) %>%
distinct()
raw_data$crises_df = import.crises.dates.df(countries_vec = countries_list$oecd_countries)
df_list = list(raw_data$TotalCredit %>%
filter(quarters(Date) == "Q4") %>%
mutate(Date = format(Date, "%Y")) %>%
deflate.data(.,vars_to_deflate = "Total_Credit") %>%
select(-Total_Credit),
raw_data$HousePrice %>%
filter(quarters(Date) == "Q4") %>%
mutate(Date = format(Date, "%Y")),
raw_data$WDI_annual %>%
rename(Date = Year) %>%
select(-GDP_per_Capita) %>%
deflate.data(.,vars_to_deflate = c("GDP"),
cpi = raw_data$CPI) %>%
select(-GDP))
country_df = df_list %>%
reduce(right_join, by = c("Date", "Country")) %>%
group_by(Country) %>%
arrange(Date) %>%
# filter(!is.na(Total_Credit_real)) %>%
# filter(!is.na(HousePrice)) %>%
mutate_at(.vars = c("Total_Credit_real","HousePrice"),
.funs = list(ret = ~ . / dplyr::lag(., order_by = Date) - 1)) %>%
# mutate_at(.vars = vars("Total_Credit_real","HousePrice"),
# .funs = list(cycle = ~cffilter(., pl = 2 * 4,
# pu = 8* 4 )[["cycle"]] / .)) %>%
mutate(Fin_ret = rowMeans(data.frame(Total_Credit_real_ret,
HousePrice_ret),na.rm = TRUE)) %>%
# mutate(Fin_cycle = rowMeans(data.frame(Total_Credit_real_cycle,
# HousePrice_cycle),na.rm = TRUE)) %>%
ungroup() %>%
filter(is.finite(Fin_ret)) %>%
filter(Date >=1978)
rm(df_list)
country_df = left_join(country_df, import.trilemma.ind(),
by = c("Country","Date"))
country_df = left_join(country_df, import.fin.dev.ind(),
by = c("Country","Date"))
bank_list = list()
raw_data$bank_balance_real_norm = raw_data$bis_lbs %>%
filter(quarters(Date) == "Q4") %>%
mutate(Date = format(Date, "%Y")) %>%
deflate.data(.,vars_to_deflate = "Balance") %>%
select(Date, CountryPair,Balance_Pos, Balance_real) %>%
normalize.bis.data(.,norm_df = country_df[,c("Date","Country", "GDP_real")],
norm_val = "GDP_real")
bank_list$bank_gdp = raw_data$bank_balance_real_norm %>%
group_by(Date, CountryPair) %>%
summarise(bank_gdp = mean(log(Balance_real), na.rm = TRUE)) %>%
filter(!is.na(bank_gdp))
# bank_pop = bank_balance_real %>%
# normalize.bis.data(.,norm_df = df[,c("Date","Country", "Pop")],
# norm_val = "Pop") %>%
# group_by(Date, CountryPair) %>%
# summarise(bank_pop = mean(log(Balance_real), na.rm = TRUE)) %>%
# filter(!is.na(bank_pop))
trade_list = list()
export_df = lapply(list.files(paste0("C:\\Users\\Misha\\Documents\\Data",
"\\IMF\\Export-Import\\Export"),
full.names = TRUE),
import_imf_df,
countries_vec = countries_list$oecd_countries) %>%
bind_rows() %>%
mutate(Exports = as.numeric(Exports)) %>%
group_by(Date, CountryPair) %>%
summarise(Exports = sum(Exports, na.rm = TRUE))
import_df = lapply(list.files(paste0("C:\\Users\\Misha\\Documents\\Data",
"\\IMF\\Export-Import\\Import"),
full.names = TRUE),
import_imf_df,
countries_vec = countries_list$oecd_countries) %>%
bind_rows() %>%
mutate(Imports = as.numeric(Imports)) %>%
group_by(Date, CountryPair) %>%
summarise(Imports = sum(Imports, na.rm = TRUE))
trade_df = full_join(export_df,import_df, by = c("Date", "CountryPair")) %>%
gather(.,key = Balance_Pos, value = Trade, -Date, - CountryPair) %>%
deflate.data(.,vars_to_deflate = "Trade") %>%
select(-Trade)
trade_list$trade_gdp = trade_df %>%
ungroup() %>%
normalize.imf.data(.,wdi_df = country_df[,c("Date","Country", "GDP_real")],
norm_val = "GDP_real") %>%
group_by(Date, CountryPair) %>%
summarise(trade_gdp = mean(log(Trade_real), na.rm = TRUE)) %>%
filter(!is.na(trade_gdp)) %>%
filter(is.finite(trade_gdp))
rm(export_df, import_df, trade_df)
ind_list = list()
# ind_list$Harmon_both = raw_data$Harmon_both_quarter %>%
# mutate(Date = format(Date, "%Y")) %>%
# group_by(CountryPair, Date, Directive) %>%
# summarise(Transposed = max(Transposed)) %>%
# group_by(Date,CountryPair) %>%
# summarise(Harmon_both_Index = log(
# sum(Transposed + 1,na.rm = TRUE)))
#
# ind_list$Harmon_one = raw_data$Harmon_one_quarter %>%
# mutate(Date = format(Date, "%Y")) %>%
# group_by(CountryPair, Date, Directive) %>%
# summarise(Transposed = max(Transposed)) %>%
# group_by(Date,CountryPair) %>%
# summarise(Harmon_one_Index = log(
# sum(Transposed + 1,na.rm = TRUE)))
ind_list$EU_both = raw_data$EU_both
ind_list$EU_one = raw_data$EU_one
ind_list$Euro_both = raw_data$Euro_both
ind_list$Euro_one = raw_data$Euro_one
country_pair_df = unlist(list(bank_list, trade_list, ind_list),
recursive = FALSE) %>%
reduce(left_join, by = c("Date","CountryPair"))
country_pair_df = country_pair_df %>%
separate(col = CountryPair,into = c("Country_A","Country_B"),
sep = "-", remove = FALSE) %>%
group_by(Country_A) %>%
mutate(Country_A_crises = classify_crises_dates(
Target_Country = Country_A[1],
dates_vec = Date,
crises_df = raw_data$crises_df[,1:3])) %>%
group_by(Country_B) %>%
mutate(Country_B_crises = classify_crises_dates(
Target_Country = Country_B[1],
dates_vec = Date,
crises_df = raw_data$crises_df[,1:3])) %>%
ungroup() %>%
rowwise() %>%
mutate(Crises_tot = sum(Country_A_crises, Country_B_crises)) %>%
mutate(Crises_one = as.numeric(Crises_tot ==1)) %>%
mutate(Crises_both = as.numeric(Crises_tot ==2)) %>%
mutate(Crises = min(Crises_tot,1)) %>%
ungroup() %>%
select(-Country_A,-Country_B)
country_pair_df = country_pair_df %>%
left_join(.,raw_data$geodist %>%
select(CountryPair, comlang_ethno) %>%
rename(Common_Lang = comlang_ethno) %>%
distinct(), by = "CountryPair")
controls = c("FX_stab","MI_ind","FO_ind","FD","GDP_real","Pop")
country_pair_df = append.countrypair.dataframe(
country_pair_df,
country_df %>%
select(Date,Country,controls) %>%
mutate(GDP_real = log(GDP_real))) %>%
collapse_pair_controls(controls,collapse_funcs = "sum")
rm(controls)
fin_reg_df = list(country_pair_df,
get.neg.abs.diff(country_df[, c("Date","Country",
"Fin_ret")]) %>%
rename(Fin_synch = Fin_ret)) %>%
reduce(left_join, by = c("CountryPair","Date")) %>%
group_by(CountryPair) %>%
mutate(Time_Trend = seq_along(Date)) %>%
ungroup() %>%
mutate(Fin_synch = 100 * Fin_synch)
country_pairs_table = table(fin_reg_df$CountryPair)
fin_reg_df = fin_reg_df %>%
filter(CountryPair %in% names(country_pairs_table)[
country_pairs_table > 10])
# fin_reg_df = fin_reg_df %>%
# filter(bank_gdp >= quantile(bank_gdp,0.025) &
# bank_gdp <= quantile(bank_gdp,0.975))
MichaelGurkov/FinSynch documentation built on Oct. 30, 2019, 9:27 p.m.
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raw_data = list() raw_data$HousePrice = import.bis.property.price.data( countries_vec = countries_list$oecd_countries) %>% mutate(Date = as.yearqtr(Date, format = "%Y-Q%q")) raw_data$TotalCredit = import.bis.tot.credit.data( countries_vec = countries_list$oecd_countries) %>% mutate(Date = as.yearqtr(Date, format = "%Y-Q%q")) raw_data$WDI_annual = import_wdi_df( countries_vec = countries_list$oecd_countries) %>% mutate(GDP = GDP * 10 ^ (-6)) # Convert to millions USD raw_data$bis_lbs = import.bis.lbs.data( countries_vec = countries_list$oecd_countries) %>% mutate(Date = as.yearqtr(Date, format = "%Y-Q%q")) raw_data$codes = read.csv(paste0("C:\\Users\\Misha\\Documents", "\\Data\\ISO\\", "iso_2digit_alpha_country", "_codes.csv")) %>% setNames(c("Code","Country"))
eu_df = read.csv(paste0("C:\\Users\\Misha\\Documents\\", "Data\\Misc\\EU_membership.csv"), stringsAsFactors = FALSE) %>% setNames(c("Country","Euro_area","EU")) %>% mutate(Country = gsub("\\s","_",Country)) %>% mutate(Country = sub("The_Netherlands","Netherlands", Country,fixed = TRUE)) %>% mutate(Country = sub("Slovakia","Slovak_Republic", Country,fixed = TRUE)) eu_dates_vec = seq.Date( from = as.Date(min(raw_data$bis_lbs$Date)), to = as.Date(max(raw_data$bis_lbs$Date)), by = "year") %>% format(.,"%Y") raw_data$EU_both = construct_countrypair_EU_index( eu_df = eu_df %>% select(Country, EU) %>% rename(Date = EU), dates_vec = eu_dates_vec, countries = countries_list$oecd_countries) %>% rename(EU_both = Status) %>% mutate(Date = as.character(Date)) raw_data$EU_one = construct_countrypair_EU_index( eu_df = eu_df %>% select(Country, EU) %>% rename(Date = EU), dates_vec = eu_dates_vec, countries = countries_list$oecd_countries, index_status = "one") %>% rename(EU_one = Status) %>% mutate(Date = as.character(Date)) raw_data$Euro_both = construct_countrypair_EU_index( eu_df = eu_df %>% select(Country, Euro_area) %>% rename(Date = Euro_area) %>% filter(!Date == "Not a member"), dates_vec = eu_dates_vec, countries = countries_list$oecd_countries) %>% rename(Euro_both = Status) %>% mutate(Date = as.character(Date)) raw_data$Euro_one = construct_countrypair_EU_index( eu_df = eu_df %>% select(Country, Euro_area) %>% rename(Date = Euro_area), dates_vec = eu_dates_vec, countries = countries_list$oecd_countries, index_status = "one") %>% rename(Euro_one = Status) %>% mutate(Date = as.character(Date)) rm(eu_df, eu_dates_vec)
raw_data$geodist = import.geodist.data() %>% filter(Country %in% countries_list$oecd_countries) %>% filter(Counter_Country %in% countries_list$oecd_countries) %>% mutate(CountryPair = ifelse(Country < Counter_Country, paste(Country, Counter_Country, sep = "-"), paste(Counter_Country,Country, sep = "-"))) %>% select(-Country, -Counter_Country) %>% distinct()
raw_data$crises_df = import.crises.dates.df(countries_vec = countries_list$oecd_countries)
df_list = list(raw_data$TotalCredit %>% filter(quarters(Date) == "Q4") %>% mutate(Date = format(Date, "%Y")) %>% deflate.data(.,vars_to_deflate = "Total_Credit") %>% select(-Total_Credit), raw_data$HousePrice %>% filter(quarters(Date) == "Q4") %>% mutate(Date = format(Date, "%Y")), raw_data$WDI_annual %>% rename(Date = Year) %>% select(-GDP_per_Capita) %>% deflate.data(.,vars_to_deflate = c("GDP"), cpi = raw_data$CPI) %>% select(-GDP)) country_df = df_list %>% reduce(right_join, by = c("Date", "Country")) %>% group_by(Country) %>% arrange(Date) %>% # filter(!is.na(Total_Credit_real)) %>% # filter(!is.na(HousePrice)) %>% mutate_at(.vars = c("Total_Credit_real","HousePrice"), .funs = list(ret = ~ . / dplyr::lag(., order_by = Date) - 1)) %>% # mutate_at(.vars = vars("Total_Credit_real","HousePrice"), # .funs = list(cycle = ~cffilter(., pl = 2 * 4, # pu = 8* 4 )[["cycle"]] / .)) %>% mutate(Fin_ret = rowMeans(data.frame(Total_Credit_real_ret, HousePrice_ret),na.rm = TRUE)) %>% # mutate(Fin_cycle = rowMeans(data.frame(Total_Credit_real_cycle, # HousePrice_cycle),na.rm = TRUE)) %>% ungroup() %>% filter(is.finite(Fin_ret)) %>% filter(Date >=1978) rm(df_list)
country_df = left_join(country_df, import.trilemma.ind(), by = c("Country","Date"))
country_df = left_join(country_df, import.fin.dev.ind(), by = c("Country","Date"))
bank_list = list() raw_data$bank_balance_real_norm = raw_data$bis_lbs %>% filter(quarters(Date) == "Q4") %>% mutate(Date = format(Date, "%Y")) %>% deflate.data(.,vars_to_deflate = "Balance") %>% select(Date, CountryPair,Balance_Pos, Balance_real) %>% normalize.bis.data(.,norm_df = country_df[,c("Date","Country", "GDP_real")], norm_val = "GDP_real") bank_list$bank_gdp = raw_data$bank_balance_real_norm %>% group_by(Date, CountryPair) %>% summarise(bank_gdp = mean(log(Balance_real), na.rm = TRUE)) %>% filter(!is.na(bank_gdp)) # bank_pop = bank_balance_real %>% # normalize.bis.data(.,norm_df = df[,c("Date","Country", "Pop")], # norm_val = "Pop") %>% # group_by(Date, CountryPair) %>% # summarise(bank_pop = mean(log(Balance_real), na.rm = TRUE)) %>% # filter(!is.na(bank_pop))
trade_list = list() export_df = lapply(list.files(paste0("C:\\Users\\Misha\\Documents\\Data", "\\IMF\\Export-Import\\Export"), full.names = TRUE), import_imf_df, countries_vec = countries_list$oecd_countries) %>% bind_rows() %>% mutate(Exports = as.numeric(Exports)) %>% group_by(Date, CountryPair) %>% summarise(Exports = sum(Exports, na.rm = TRUE)) import_df = lapply(list.files(paste0("C:\\Users\\Misha\\Documents\\Data", "\\IMF\\Export-Import\\Import"), full.names = TRUE), import_imf_df, countries_vec = countries_list$oecd_countries) %>% bind_rows() %>% mutate(Imports = as.numeric(Imports)) %>% group_by(Date, CountryPair) %>% summarise(Imports = sum(Imports, na.rm = TRUE)) trade_df = full_join(export_df,import_df, by = c("Date", "CountryPair")) %>% gather(.,key = Balance_Pos, value = Trade, -Date, - CountryPair) %>% deflate.data(.,vars_to_deflate = "Trade") %>% select(-Trade) trade_list$trade_gdp = trade_df %>% ungroup() %>% normalize.imf.data(.,wdi_df = country_df[,c("Date","Country", "GDP_real")], norm_val = "GDP_real") %>% group_by(Date, CountryPair) %>% summarise(trade_gdp = mean(log(Trade_real), na.rm = TRUE)) %>% filter(!is.na(trade_gdp)) %>% filter(is.finite(trade_gdp)) rm(export_df, import_df, trade_df)
ind_list = list() # ind_list$Harmon_both = raw_data$Harmon_both_quarter %>% # mutate(Date = format(Date, "%Y")) %>% # group_by(CountryPair, Date, Directive) %>% # summarise(Transposed = max(Transposed)) %>% # group_by(Date,CountryPair) %>% # summarise(Harmon_both_Index = log( # sum(Transposed + 1,na.rm = TRUE))) # # ind_list$Harmon_one = raw_data$Harmon_one_quarter %>% # mutate(Date = format(Date, "%Y")) %>% # group_by(CountryPair, Date, Directive) %>% # summarise(Transposed = max(Transposed)) %>% # group_by(Date,CountryPair) %>% # summarise(Harmon_one_Index = log( # sum(Transposed + 1,na.rm = TRUE))) ind_list$EU_both = raw_data$EU_both ind_list$EU_one = raw_data$EU_one ind_list$Euro_both = raw_data$Euro_both ind_list$Euro_one = raw_data$Euro_one
country_pair_df = unlist(list(bank_list, trade_list, ind_list), recursive = FALSE) %>% reduce(left_join, by = c("Date","CountryPair"))
country_pair_df = country_pair_df %>% separate(col = CountryPair,into = c("Country_A","Country_B"), sep = "-", remove = FALSE) %>% group_by(Country_A) %>% mutate(Country_A_crises = classify_crises_dates( Target_Country = Country_A[1], dates_vec = Date, crises_df = raw_data$crises_df[,1:3])) %>% group_by(Country_B) %>% mutate(Country_B_crises = classify_crises_dates( Target_Country = Country_B[1], dates_vec = Date, crises_df = raw_data$crises_df[,1:3])) %>% ungroup() %>% rowwise() %>% mutate(Crises_tot = sum(Country_A_crises, Country_B_crises)) %>% mutate(Crises_one = as.numeric(Crises_tot ==1)) %>% mutate(Crises_both = as.numeric(Crises_tot ==2)) %>% mutate(Crises = min(Crises_tot,1)) %>% ungroup() %>% select(-Country_A,-Country_B)
country_pair_df = country_pair_df %>% left_join(.,raw_data$geodist %>% select(CountryPair, comlang_ethno) %>% rename(Common_Lang = comlang_ethno) %>% distinct(), by = "CountryPair")
controls = c("FX_stab","MI_ind","FO_ind","FD","GDP_real","Pop") country_pair_df = append.countrypair.dataframe( country_pair_df, country_df %>% select(Date,Country,controls) %>% mutate(GDP_real = log(GDP_real))) %>% collapse_pair_controls(controls,collapse_funcs = "sum") rm(controls)
fin_reg_df = list(country_pair_df, get.neg.abs.diff(country_df[, c("Date","Country", "Fin_ret")]) %>% rename(Fin_synch = Fin_ret)) %>% reduce(left_join, by = c("CountryPair","Date")) %>% group_by(CountryPair) %>% mutate(Time_Trend = seq_along(Date)) %>% ungroup() %>% mutate(Fin_synch = 100 * Fin_synch)
country_pairs_table = table(fin_reg_df$CountryPair) fin_reg_df = fin_reg_df %>% filter(CountryPair %in% names(country_pairs_table)[ country_pairs_table > 10]) # fin_reg_df = fin_reg_df %>% # filter(bank_gdp >= quantile(bank_gdp,0.025) & # bank_gdp <= quantile(bank_gdp,0.975))
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