16.11.2023 Time Series#

Time Series#

library(tseries)

Import Data

data <- read.table("data/Per_Capita_GDP_Data_1870-1987.asc", header=T)
head(data)

Bind the Column and Data set together = cbind()

startyear <- 1870
year <- seq(from=startyear, to= 1987, by=1)
data_GDP <- cbind(year, data) 
head(data_GDP) # new year column

Define Time Series

data_ts <- ts(data_GDP, startyear)
class(data_ts)

[1] "mts"    "ts"     "matrix"

MTS = multiple Time Series

apply diff

us_diff <- diff(log(data_ts[,"USA"]))

  1. Take the Column USA

  2. log the GDP

  3. now take the Difference \(\Delta y = y_{t} - y_{t-1}\)

=> Growth Rate of Log GDP in USA

Plot

plot(data_ts[,c("USA")])

plot(us_diff)

Panel Data#

library(plm)
library(stargazer)

data(wagepan, package='wooldridge') # directly from package witoout loading
head(wagepan)

Get Panel Data from thsi with pdata.frame()

wagepan.p <- pdata.frame(wagepan, index=c("nr","year") ) #added HA: individual index and time index
pdim(wagepan.p)

  • n = number of individuals / regions = 545

  • T = Number of observations of the indiviudals = 8

  • N = n*T

Regressions with plm()

1) Simple linear Regression with all data pooled

wagemodel1 <- plm(lwage ~ married + union + exper, data=wagepan.p, model="pooling")

2) now same as above but with controls

wagemodel2 <- plm(lwage ~ married + black + union + educ + exper, data=wagepan.p, model="pooling")

3) estimate fixed effects model (based on „within-transformation“ of data) <–> pooling model

NOTE: use for my Acemoglu Paper!!

wagemodel3 <- plm(lwage ~ married + union + educ + exper, data=wagepan.p, model="within")

Summary

summary(wagemodel3)

4) same, but with year dummy for shock absorption

Used with factor(year) for easy year incuding of the variables

wagemodel4 <- plm(lwage ~ married + black + union + educ + exper + factor(year), data=wagepan.p, model="pooling")

Combine all and get stargazer to analyze it

#| results='asis' 
mywagemodels <- list(wagemodel1,wagemodel2,wagemodel3,wagemodel4)
stargazer(
  mywagemodels, 
  type="html"
  #out="data/mywagemodels.html"
  )

ggplot#

library(tidyverse)

Replicating the Data from AJR 2001

load("data/dataset_AJR2001.Rdata")
head(data)

Simple:

ggplot(data=data, aes(x = logem4, y=logpgp95)) +
    geom_point() + 
    geom_smooth(method="lm")

Advanced:

figure <- ggplot(data, aes(x = logem4, y=logpgp95)) + #Settler mortality and GDP per Cap
  #geom_point() + #if we want to use points
  geom_text(aes(label=data[,1]),hjust=0, vjust=0) + #if we want the country names as points
  geom_smooth(color="black",method="lm", se=FALSE) + 
  theme_classic()+ 
  coord_cartesian(xlim=c(2,8), ylim=c(4,10.5)) + 
  labs(
    title="Figure 1 from AJR (2001) with ggplot()",
    x="Log of European Settler Mortality", 
    y="Log GDP per capita, PPP, 1995"
    )
figure