Commit 71140004 authored by Mateo CAICEDO's avatar Mateo CAICEDO

Proof reading.

parent 9316a238
......@@ -49,8 +49,8 @@ To those series we add 2 series :
The sources we use here are :
* the Area-Wide Model (AWM), originally constructed by @Faga01
* International Finance Statistics (IMF)
* Bank of International Settlements
* International Financial Statistics (IMF)
* Bank for International Settlements
* European Central Bank
We take data directly from <a href="https://db.nomics.world/" target="_blank">DBnomics</a>. The DBnomics API can be accessed through R with the <a href="https://github.com/dbnomics/rdbnomics" target="_blank">rdbnomics</a> package. All the following code is written in R, thanks to the [@RCT16] and the [@RStu16].
......@@ -289,10 +289,10 @@ loans_hh <-
# Bank lending rates
To build long series of lending rate, we use historical data from the International Finance Statistics of the IMF. Historical series will be used before 2000, because such series is available from ECB since 2000Q1. So we consider only countries among the first 12 Euro area members. Among them, four have very partial series. Eventually, we consider data only for : Belgium, Finland, France, Germany, Ireland, Italy, Netherlands and Spain. As in the methodology of AWM, we weigth the sum of the lending rates by the gross domestic product based on purchasing-power-parity (PPP) of each country in 1995, from the World Economic Outlook of the IMF.
To build long series of lending rates, we use historical data from the IMF International Financial Statistics. Historical series will be used before 2000, because such series are available from ECB since 2000Q1. Thus, we consider only countries among the first 12 Euro area members. Among them, four have very partial series. Eventually, we only consider data for : Belgium, Finland, France, Germany, Ireland, Italy, Netherlands and Spain. As in the AWM methodology, we weigth the sum of the lending rates by the gross domestic product based on purchasing-power-parity (PPP) of each country in 1995, from the IMF World Economic Outlook.
```{r}
# Download lending rates of the 8 countries from IFS
# Download the 8 countries' lending rates from IFS
country_code <- c("BE","FR","DE","IT","NL","FI","IE","ES")
url_country <- paste0(country_code, collapse = "+")
filter <- paste0("Q.",url_country,".FILR_PA")
......@@ -308,7 +308,7 @@ lendingrate_bycountry <- df %>%
time=as.Date(as.yearqtr(gsub("Q","",time)))) %>%
filter(year(time)>=1985)
# Download PPP GDP of the 8 countries from WEO
# Download the 8 countries' PPP GDP from WEO
country_iso <- c("BEL","FRA", "DEU", "ITA", "NLD", "FIN", "IRL", "ESP")
url_country_iso <- paste0(country_iso, collapse = "+")
url_dbnomics <- "https://api.db.nomics.world/api/v1/sdmx"
......@@ -378,7 +378,7 @@ lendingrate <- chain(basis = lendingrate_recent,
date_chain = "2000-01-01")
```
More precisely, the recent bank lending rates come from the ECB and are described as
More precisely, the recent bank lending rates come from the ECB and are described as:
```{r}
varname
```
......@@ -446,7 +446,7 @@ varname
# Entrepreunarial networth
The entrepreunarial networth is approximated through the Dow Jones index for the Euro area, in a similar spirit of what is chosen by [@Chri14a] in the US database.
The entrepreunarial networth is approximated through the Dow Jones index for the Euro area, in a similar way of what is chosen by [@Chri14a] in the US database.
```{r, fig.align="center"}
# Dow Jones euro Euro area (changing composition) - Equity/index - Dow Jones Euro Stoxx Price Index - Historical close, average of observations through period - Euro
......@@ -470,7 +470,7 @@ ggplot(networth,aes(time,values)) +
ggtitle("Entrepreunarial networth (index)")
```
More precisely, the index come from the ECB and is desdcribed as
More precisely, the index come from the ECB and is described as
```{r}
varname
```
......@@ -501,15 +501,15 @@ ggplot(houseprice,aes(time,values)) +
```
More precisely, house prices come from the BIS and are desdcribed as
More precisely, house prices come from the BIS and are described as
```{r}
varname
```
# Final finance database for the Euro area
# Final financial database for the Euro area
We build the final finance database with the 6 series described before.
We build the final financial database with the 6 series described before.
```{r}
EA_Finance_data <- bind_rows(loans_nfc,
loans_hh,
......@@ -561,7 +561,7 @@ ggplot(plot_df,aes(time,values))+
axis.text=element_text(size=7))
```
You can download the 6 finance series directly as csv <a href="http://shiny.nomics.world/data/EA_Finance_rawdata.csv" target="_blank">here</a>
You can download the 6 financial series directly as csv <a href="http://shiny.nomics.world/data/EA_Finance_rawdata.csv" target="_blank">here</a>
```{r}
EA_Finance_rawdata <-
EA_Finance_data %>%
......@@ -573,7 +573,7 @@ EA_Finance_rawdata %>%
# Final CMR database for the Euro area
We want to build eventually a database similar to the [@Chri14a] database, but for the Euro area, on the basis of the [@Smet03] database. The database will begin in 1980Q1, as the financial series are not available before. You can download all the raw series <a href="http://shiny.nomics.world/data/EA_CMR_rawdata.csv" target="_blank">here</a>.
We eventually want to build a database similar to the [@Chri14a] database, but for the Euro area, on the basis of the [@Smet03] database. The database will begin in 1980Q1, as the financial series are not available before. You can download all the raw series <a href="http://shiny.nomics.world/data/EA_CMR_rawdata.csv" target="_blank">here</a>.
```{r}
#File EA_SW_rawdata.csv is created via EA_SW_data.Rmd, which can be found on obsmacro under sw03-data
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment