Ekonometrika
Assignment 10
Kefas Ronaldo Immanuel Labiro
April 06, 2021
Preparation
library(tsibbledata)## Warning: package 'tsibbledata' was built under R version 4.0.4library(tsibble)## Warning: package 'tsibble' was built under R version 4.0.4library(DT)
library(ggplot2)## Warning: package 'ggplot2' was built under R version 4.0.4library(fpp2)## Warning: package 'fpp2' was built under R version 4.0.4## Warning: package 'forecast' was built under R version 4.0.4## Warning: package 'fma' was built under R version 4.0.4## Warning: package 'expsmooth' was built under R version 4.0.4library(fpp3)## Warning: package 'fpp3' was built under R version 4.0.4## Warning: package 'feasts' was built under R version 4.0.4## Warning: package 'fabletools' was built under R version 4.0.4## Warning: package 'fable' was built under R version 4.0.4library(autoplotly)## Warning: package 'autoplotly' was built under R version 4.0.4library(dplyr)
library(forecast)
library(feasts)
library(lubridate)
library(expsmooth)
library(fma)
library(fabletools)1 Use the help function to explore what the series gafa_stock, PBS, vic_elec and peltrepresent.
help("gafa_stock")## starting httpd help server ... donehelp("PBS")
help("vic_elec")
help("pelt")paste("gafa_stock is a time series of Historical stock prices from 2014-2018 for Google, Amazon, Facebook and Apple. ")## [1] "gafa_stock is a time series of Historical stock prices from 2014-2018 for Google, Amazon, Facebook and Apple. "datatable(gafa_stock,
caption = htmltools::tags$caption(
style = 'caption-side: bottom; text-align: center;',
htmltools::em('Table GAFA stock prices.')),
extensions = 'FixedColumns',
option = list(scrollX = TRUE, fixedColumns = TRUE)
)1.1 Use autoplot() to plot some of the series in these data sets.
gafa_stock %>% autoplot(Open)
PBS %>% autoplot(Scripts)
vic_elec %>% autoplot(Demand)
pelt %>% autoplot(Hare)
1.2 What is the time interval of each series?
interval(gafa_stock) ## Warning: tz(): Don't know how to compute timezone for object of class tbl_ts/
## tbl_df/tbl/data.frame; returning "UTC". This warning will become an error in the
## next major version of lubridate.## <Interval[0]>interval(PBS)## Warning: tz(): Don't know how to compute timezone for object of class tbl_ts/
## tbl_df/tbl/data.frame; returning "UTC". This warning will become an error in the
## next major version of lubridate.## <Interval[0]>interval(vic_elec)## Warning: tz(): Don't know how to compute timezone for object of class tbl_ts/
## tbl_df/tbl/data.frame; returning "UTC". This warning will become an error in the
## next major version of lubridate.## <Interval[0]>2 Use filter() to find what days corresponded to the peak closing price for each of the four stocks in gafa_stock.
gafa_stock %>%
group_by(Symbol) %>%
filter(Close == max(Close))## # A tsibble: 4 x 8 [!]
## # Key: Symbol [4]
## # Groups: Symbol [4]
## Symbol Date Open High Low Close Adj_Close Volume
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2018-10-03 230. 233. 230. 232. 230. 28654800
## 2 AMZN 2018-09-04 2026. 2050. 2013 2040. 2040. 5721100
## 3 FB 2018-07-25 216. 219. 214. 218. 218. 58954200
## 4 GOOG 2018-07-26 1251 1270. 1249. 1268. 1268. 24056003 Download the file tute1.csv, open it in Excel (or some other spreadsheet application), and review its contents. You should find four columns of information. Columns B through D each contain a quarterly series, labelled Sales, AdBudget and GDP. Sales contains the quarterly sales for a small company over the period 1981-2005. AdBudget is the advertising budget and GDP is the gross domestic product. All series have been adjusted for inflation.
3.1 You can read the data into R with the following script
tute1 <- read.csv("tute1.csv", header = TRUE)
datatable(tute1,
caption = htmltools::tags$caption(
style = 'caption-side: bottom; text-align: center;',
htmltools::em('tute1')),
extensions = 'FixedColumns',
option = list(scrollX = TRUE, fixedColumns = TRUE))3.2 Convert the data to time series
mytimeseries <- tute1 %>%
mutate(Quarter = yearmonth(Quarter)) %>%
as_tsibble(index = Quarter)3.3 Construct time series plots of each of the three series
mytimeseries %>%
pivot_longer(-Quarter) %>%
ggplot(aes(x = Quarter, y = value, colour = name)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y")
4 The USgas package contains data on the demand for natural gas in the US
4.1 Install the USgas package
#install.packages("USgas")
library(USgas)## Warning: package 'USgas' was built under R version 4.0.44.2 Create a tsibble from us_total with year as the index and state as the key
us_total <- us_total %>% as_tsibble(index = year, key = state)4.3 Plot the annual natural gas consumption by state for the New England area (comprising the states of Maine, Vermont, New Hampshire, Massachusetts, Connecticut and Rhode Island)
new_england<- us_total %>%
group_by(state) %>%
filter(state %in% c('Maine', 'Vermont', 'New Hampshire', 'Massachusetts', 'Connecticut' ,'Rhode Island')) %>%
ungroup()
autoplot(new_england, y)
5 Follow the instructions below:
5.1 Download tourism.xlsx and read it into R using readxl::read_excel()
tourism <-readxl::read_excel("tourism.xlsx")datatable(tourism,
caption = htmltools::tags$caption(
style = 'caption-side: bottom; text-align: center;',
htmltools::em('tourism')),
extensions = 'FixedColumns',
option = list(scrollX = TRUE, fixedColumns = TRUE))## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html5.2 Create a tsibble which is identical to the tourism tsibble from the tsibble package.
tourism_tsibble <- tourism %>%
mutate(Quarter = yearquarter(Quarter)) %>%
as_tsibble(key = c("Region", "State", "Purpose"),index = "Quarter")5.3 Find what combination of Region and Purpose had the maximum number of overnight trips on average.
tourism_tsibble %>% group_by(Region, Purpose) %>%
summarise(Trips = mean(Trips)) %>%
ungroup() %>%
filter(Trips == max(Trips))## # A tsibble: 1 x 4 [1Q]
## # Key: Region, Purpose [1]
## Region Purpose Quarter Trips
## <chr> <chr> <qtr> <dbl>
## 1 Melbourne Visiting 2017 Q4 985.5.4 Create a new tsibble which combines the Purposes and Regions, and just has total trips by State
tourism_tsibble %>%
group_by(State) %>%
summarise(Trips = sum(Trips)) %>%
ungroup()## # A tsibble: 640 x 3 [1Q]
## # Key: State [8]
## State Quarter Trips
## <chr> <qtr> <dbl>
## 1 ACT 1998 Q1 551.
## 2 ACT 1998 Q2 416.
## 3 ACT 1998 Q3 436.
## 4 ACT 1998 Q4 450.
## 5 ACT 1999 Q1 379.
## 6 ACT 1999 Q2 558.
## 7 ACT 1999 Q3 449.
## 8 ACT 1999 Q4 595.
## 9 ACT 2000 Q1 600.
## 10 ACT 2000 Q2 557.
## # ... with 630 more rows6 Create time plots of the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec.
6.1 Use ? (or help()) to find out about the data in each series.
aus_production %>% autoplot(Bricks)## Warning: Removed 20 row(s) containing missing values (geom_path).
pelt %>% autoplot(Lynx)
gafa_stock %>% autoplot(Close)
vic_elec %>% autoplot(Demand)
6.2 For the last plot, modify the axis labels and title
vic_elec %>% ggplot(aes(x= Date, y= Demand, group = Holiday)) +
geom_line(aes(col=Holiday)) +
facet_grid(Holiday ~ ., scales ="free" )
7 The aus_arrivals data set comprises quarterly international arrivals to Australia from Japan, New Zealand, UK and the US
datatable(aus_arrivals, caption = htmltools::tags$caption(
style = 'caption-side: bottom; text-align: center;',
htmltools::em('aus arrivals')),
extensions = 'FixedColumns',
option = list(scrollX = TRUE, fixedColumns = TRUE))7.1 Use autoplot(), gg_season() and gg_subseries() to compare the differences between the arrivals from these four countries
aus_arrivals %>% autoplot(Arrivals)
aus_arrivals %>% gg_season(Arrivals)
aus_arrivals %>% gg_subseries(Arrivals)
7.2 Can you identify any unusual observations?
- Arrivals dari Jepng menurun di quarter ke-2 jika dibandingkan dengan quarter lainnya
- Arrivals dari New Zealand tertinggi pada quarter ke-3 dan terendah pada quarter pertama
- Arrivals dari US dan UK naik pada quarter 1 dan 4, kemudian turun pada quarter 2 dan 3
8 Monthly Australian retail data is provided in aus_retail. Select one of the time series as follows (but choose your own seed value)
set.seed(12345678)
myseries <- aus_retail %>%
filter(`Series ID` == sample(aus_retail$`Series ID`,1))Explore your chosen retail time series using the following functions:
- autoplot(),
- gg_season(),
- gg_subseries(),
- gg_lag(),
- ACF() %>% autoplot()
Can you spot any seasonality, cyclicity and trend? What do you learn about the series?
autoplot(myseries, Turnover)
gg_season(myseries, Turnover)
gg_subseries(myseries, Turnover)
gg_lag(myseries, Turnover)
myseries %>% ACF(Turnover) %>% autoplot()
9 Use the following graphics functions: autoplot(), gg_season(), gg_subseries(), gg_lag(), ACF() and explore features from the following time series: “Total Private” Employed from us_employment, Bricks from aus_production, Hare from pelt, “H02” Cost from PBS, and us_gasoline.
9.1 Can you spot any seasonality, cyclicity and trend?
TP <- us_employment %>%
filter(Title == "Total Private")autoplot(TP,Employed)
gg_season(TP,Employed)
gg_subseries(TP,Employed)
gg_lag(TP,Employed)
TP %>% ACF(Employed) %>% autoplot()
aus_production %>% autoplot(Bricks)## Warning: Removed 20 row(s) containing missing values (geom_path).
aus_production %>% gg_season(Bricks)## Warning: Removed 20 row(s) containing missing values (geom_path).
aus_production %>% gg_subseries(Bricks)## Warning: Removed 5 row(s) containing missing values (geom_path).
aus_production %>% gg_lag(Bricks)## Warning: Removed 20 rows containing missing values (gg_lag).
pelt %>% autoplot(Hare)
pelt %>% gg_subseries(Hare)
pelt %>% gg_lag(Hare)
H02 <- PBS %>% filter(ATC2 == "H02")
H02 %>% autoplot(Cost)
H02 %>% gg_season(Cost)
H02 %>% gg_subseries(Cost)
H02 %>% ACF(Cost)## # A tsibble: 92 x 6 [1M]
## # Key: Concession, Type, ATC1, ATC2 [4]
## Concession Type ATC1 ATC2 lag acf
## <chr> <chr> <chr> <chr> <lag> <dbl>
## 1 Concessional Co-payments H H02 1M 0.834
## 2 Concessional Co-payments H H02 2M 0.679
## 3 Concessional Co-payments H H02 3M 0.514
## 4 Concessional Co-payments H H02 4M 0.352
## 5 Concessional Co-payments H H02 5M 0.264
## 6 Concessional Co-payments H H02 6M 0.219
## 7 Concessional Co-payments H H02 7M 0.253
## 8 Concessional Co-payments H H02 8M 0.337
## 9 Concessional Co-payments H H02 9M 0.464
## 10 Concessional Co-payments H H02 10M 0.574
## # ... with 82 more rowsus_gasoline %>% autoplot(Barrels)
us_gasoline %>% gg_season(Barrels)
us_gasoline %>% gg_subseries(Barrels)
us_gasoline %>% gg_lag(Barrels)
10 The following time plots and ACF plots correspond to four different time series. Your task is to match each time plot in the first row with one of the ACF plots in the second row
- 1 dengan B
- 2 dengan A
- 3 dengan D
- 4 dengan C
11 The aus_livestock data contains the monthly total number of pigs slaughtered in Victoria, Australia, from Jul 1972 to Dec 2018. Use filter() to extract pig slaughters in Victoria between 1990 and 1995. Use autoplot() and ACF() for this data. How do they differ from white noise? If a longer period of data is used, what difference does it make to the ACF?
VicPig<- aus_livestock %>%
filter(State == "Victoria",
Animal == "Pigs",
between(year(Month),1990,1995))
VicPig %>% ACF(Count) %>% autoplot()
12 Use the following code to compute the daily changes in Google closing stock prices
dgoog <- gafa_stock %>%
filter(Symbol == "GOOG", year(Date) >= 2018) %>%
mutate(trading_day = row_number()) %>%
update_tsibble(index = trading_day, regular = TRUE) %>%
mutate(diff = difference(Close))12.1 Why was it necessary to re-index the tsibble?
12.2 Plot these differences and their ACF
google_stock <- gafa_stock %>%
filter(Symbol == "GOOG") %>%
mutate(trading_day = row_number()) %>%
update_tsibble(index = trading_day, regular = TRUE)
google_stock %>%
ACF(difference(Close)) %>%
autoplot()
12.3 Do the changes in the stock prices look like white noise?
5/100*30## [1] 1.5Data series bukanlah white noise jika adal lebih dari 1,5 yang tertinggal di luar batas. Kita dapat melihat dari plot di atas bahwa ada tiga lags yang di luar batas, yang berarti bahwa data series bukan white noise.