# See source for more details on input, output from the author
library(ggplot2)
library(reshape2)
library(plyr)
options(width=80)
dtf <- product.data$entity
agg <- product.data$eu_aggregates
trade <- product.data$trade
\[\beta_1\]
In the following file, we explore a data table containing Sawnwood consumption data for 28 countries from 1961 to 2012. In 2012, the overall EU consumption, production and trade in million M3 per item was:
## Item Consumption Production Net_Trade Import Export NA
## 154 Total Sawnwood 85 99 14 34 48 0
## 155 Sawnwood Coniferous 76 89 13 30 43 0
## 156 Sawnwood Non Coniferous 9 10 0 4 5 0
## NA
## 154 0
## 155 0
## 156 0
#summary(agg)
ggplot(data=subset(agg, !Element%in%c("Import_Value", "Export_Value","Price"))) +
geom_line(aes(x=Year, y=Value/1e6, colour=Item),size=1) +
facet_wrap(~Element) +
ylab(paste("Million",product.data$metadata$unit)) + theme_bw()
## Warning: Removed 51 rows containing missing values (geom_path).
## Warning: Removed 51 rows containing missing values (geom_path).
Apparent consumption of Sawnwood by country
ggplot(data=dtf) +
geom_line(aes(x=Year, y=Consumption/1e+06, colour=Item), size=1) +
facet_wrap(~Country, scales = "free_y") +
xlab("Year") +
ylab(paste("Million",product.data$metadata$unit)) +
theme_bw() + theme(legend.position="bottom")
Average trade price of Sawnwood in the European Union
# Rename Price to Price USD (Use level because Element is a factor)
levels(agg$Element)[levels(agg$Element)=="Price"] <- "Price_real_USD"
ggplot(data=subset(agg, Element=="Price_real_USD")) +
geom_line(aes(x=Year, y=Value, color=Item)) +
ylab(paste("Price in (2010) constant USD /", product.data$metadata$unit))
Comparison of nominal price in USD and real price in USD and EUR.
p <- dcast(agg, Year + Item ~ Element, value.var="Value")
# Add US and EUR deflator in the plot for information
p <- merge(p, EUR[c("Year", "DeflEUR")])
p <- merge(p, US[c("Year", "DeflUS")])
p <- mutate(p,
Price_Nominal_USD = (Import_Value + Export_Value)/
(Import_Quantity + Export_Quantity) *1000,
Price_USD_2 = Price_EUR /ExchR)
p <- p[c("Year", "Item", "Price_Nominal_USD", "DeflUS",
"Price_real_USD", "DeflEUR", "ExchR", "Price_EUR")]
p <- melt(p, id=c("Year","Item"), variable.name="Element", value.name="Value")
ggplot(data=p) +
geom_line(aes(x=Year, y=Value, color=Item)) +
facet_wrap(~ Element, ncol=1, scales = "free_y")
## Warning: Removed 51 rows containing missing values (geom_path).
## Warning: Removed 51 rows containing missing values (geom_path).
Prices expressed in constant US dollars of 2010 per M3.
Import_Price = Import_Value / Import_Quantity / DeflUS*1000
Export_Price = Export_Value / Export_Quantity / DeflUS*1000
Trade prices for the 9 countries which have the highest trade volume in 2012 (volumes are in M3).
| Country | Net_Trade | Price_EUR.Export | Quantity.Export | Value.Export | Price_Trade.Export | Price_EUR.Import | Quantity.Import | Value.Import | Price_Trade.Import | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | United Kingdom | -4980633.00 | 253.72 | 140599.00 | 54236.00 | 368.99 | 253.72 | 5121232.00 | 1721105.00 | 321.47 |
| 2 | Italy | -4618000.00 | 218.91 | 273000.00 | 174564.00 | 611.65 | 218.91 | 4891000.00 | 1328737.00 | 259.87 |
| 3 | France | -2231685.00 | 250.13 | 837612.00 | 272329.00 | 311.00 | 250.13 | 3069297.00 | 1027228.00 | 320.14 |
| 4 | Netherlands | -2152900.00 | 257.00 | 421400.00 | 191452.00 | 434.59 | 257.00 | 2574300.00 | 832351.00 | 309.28 |
| 5 | Germany | 2384480.00 | 211.95 | 6696793.00 | 1787723.00 | 255.35 | 211.95 | 4312313.00 | 1315190.00 | 291.74 |
| 6 | Romania | 3178064.00 | 202.23 | 3225382.00 | 852608.00 | 252.86 | 202.23 | 47318.00 | 27526.00 | 556.45 |
| 7 | Austria | 3241112.00 | 207.90 | 5178107.00 | 1377928.00 | 254.55 | 207.90 | 1936995.00 | 589193.00 | 290.96 |
| 8 | Finland | 5992371.00 | 186.31 | 6448910.00 | 1596194.00 | 236.76 | 186.31 | 456539.00 | 114671.00 | 240.26 |
| 9 | Sweden | 11450088.00 | 210.15 | 11853000.00 | 3272317.00 | 264.08 | 210.15 | 402912.00 | 152689.00 | 362.50 |
ggplot(data=subset(trade, Country %in% countries)) +
geom_line(aes(x=Year, y=Price_Trade, color=Item, linetype = Trade))+
facet_wrap( ~ Country, ncol = 3) +
ylab(paste("Price in (2010) constant USD /", product.data$metadata$unit))
We will estimate the model \[ log(Consumption) = \beta_0 + \beta_1 log(GDP) + \beta_2 log(Price) + \beta_3 log(Consumption_{t-1}) \]
Lets look at the relationship between log(Consumption) and log(GDP) first.
plot(log(Consumption) ~ log(GDPconstantUSD),
data=dtf[grep("Total", dtf$Item),])
points(log(Consumption) ~ log(GDPconstantUSD),
data=dtf[grepl("Total", dtf$Item) & dtf$Year>2011,], col="red")
Sort countries by Net_Trade, then display a color for each country
dtf_last = dtf[grepl("Total", dtf$Item) & dtf$Year==max(dtf$Year),]
dtf_last = dtf_last[order(dtf_last$Net_Trade),]
dtf$Country = factor(dtf$Country, levels= dtf_last$Country, ordered=TRUE)
p = ggplot(dtf, aes(x=log(GDPconstantUSD), y=log(Consumption))) + facet_wrap(~Item)
p + geom_point(aes(alpha=Year, color=Country))
