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Angie Liu
SP21 AEDE6120
Commits
28af92ae
Commit
28af92ae
authored
4 years ago
by
Angie Liu
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# Lab 5
# install packages
install.packages
(
'tseries'
)
# for time series tests
install.packages
(
'seastests'
)
# for seasonality test
install.packages
(
'car'
)
# for calculating VIF
# load packages
library
(
tidyverse
)
library
(
ggplot2
)
library
(
lmtest
)
library
(
sandwich
)
library
(
stargazer
)
library
(
tseries
)
library
(
seastests
)
library
(
car
)
# clear
rm
(
list
=
ls
())
# import data file
data0
<-
read_csv
(
"https://code.osu.edu/liu.6200/sp21-aede6120/-/raw/master/LabData/depos0.csv"
)
# Time series models ----
# Y ~ X
# dif_Y ~ dif_X
# Y_t - Y_(t-1)
# %_Y ~ %_X
# Y_t/Y_(t-1) - 1
# Variable transformation ----
# transform the dependent variable
data0
<-
data0
%>%
mutate
(
Deposits_a1
=
lag
(
Deposits
))
# lag
data0
<-
data0
%>%
mutate
(
Deposits_d1
=
Deposits
-
Deposits_a1
)
# difference
data0
<-
data0
%>%
mutate
(
Deposits_p1
=
Deposits
/
Deposits_a1
-
1
)
# % change
# transform independent variables
# set up my_lag
lag_names
<-
paste0
(
"a"
,
1
:
4
)
my_lag
<-
setNames
(
paste0
(
"lag(.,"
,
1
:
4
,
")"
),
lag_names
)
# create lags
var_names
<-
c
(
'rdi'
,
'hpi'
)
data0
<-
data0
%>%
mutate_at
(
var_names
,
funs_
(
my_lag
))
# create differences
data0
<-
data0
%>%
mutate
(
rdi_d1
=
rdi
-
rdi_a1
)
data0
<-
data0
%>%
mutate
(
hpi_d1
=
hpi
-
hpi_a1
)
# create lags of the differences
var_names_diff
<-
c
(
'rdi_d1'
,
'hpi_d1'
)
data0
<-
data0
%>%
mutate_at
(
var_names_diff
,
funs_
(
my_lag
))
# create % changes
data0
<-
data0
%>%
mutate
(
rdi_p1
=
rdi
/
rdi_a1
-
1
)
data0
<-
data0
%>%
mutate
(
hpi_p1
=
hpi
/
hpi_a1
-
1
)
# create lags of the % changes
var_names_p
<-
c
(
'rdi_p1'
,
'hpi_p1'
)
data0
<-
data0
%>%
mutate_at
(
var_names_p
,
funs_
(
my_lag
))
# Create seasonal dummy variables ----
# S1.extract months
data0
$
date
<-
data0
$
date
%>%
as.Date
(
'%m/%d/%Y'
)
data0
$
m
<-
data0
$
date
%>%
months
# S2.create dummy vars
dumv
<-
model.matrix
(
~
m
,
data0
)
dumv
<-
dumv
%>%
as.data.frame
%>%
select
(
-
'(Intercept)'
)
# S3.put dummy vars into data0
data0
<-
cbind
(
data0
,
dumv
)
# Test for seasonality ----
actual.na
<-
data0
%>%
filter
(
scenario
==
'actuals'
)
# put 'actuals' into a new dataset
wo
(
actual.na
$
Deposits
,
freq
=
4
)
%>%
summary
# Detecting trends in time-series data ----
actual.na
%>%
select
(
Deposits
)
%>%
ts.plot
actual.na
%>%
select
(
Deposits_d1
)
%>%
ts.plot
actual.na
%>%
select
(
Deposits_p1
)
%>%
ts.plot
# Augmented Dickey-Fuller test for unit root ----
actual
<-
actual.na
%>%
na.omit
# delete missing values
# ADF test
adf.test
(
actual
$
Deposits
,
k
=
4
)
adf.test
(
actual
$
Deposits_p1
,
k
=
4
)
# Model selection review ----
# create the starting model
reg0
<-
lm
(
Deposits_p1
~
mMarch
+
mJune
+
mSeptember
,
data
=
actual
)
summary
(
reg0
)
# run model selection
reg1
<-
step
(
reg0
,
direction
=
"forward"
,
scope
=
(
~
mMarch
+
mJune
+
mSeptember
+
rdi_d1
+
rdi_d1_a1
+
rdi_d1_a2
+
rdi_d1_a3
+
rdi_d1_a4
+
hpi_p1
+
hpi_p1_a1
+
hpi_p1_a2
+
hpi_p1_a3
+
hpi_p1_a4
))
summary
(
reg1
)
# see VIFs of the variables
vif
(
reg1
)
# DW test for autocorrelation
dwtest
(
reg1
)
# Breusch-Godfrey test for autocorrelation
bgtest
(
reg1
)
# histogram of residuals for detecting violation of normality assumption
hist
(
residuals
(
reg1
))
# plot residuals vs fitted values for detecting heteroscedasticity
plot
(
reg1
,
1
)
plot
(
reg1
)
# Present results with robust s.e. ----
# calculate standard s.e. and robust s.e.
ols_se
<-
reg1
%>%
vcov
%>%
diag
%>%
sqrt
hc_se
<-
reg1
%>%
vcovHC
(
type
=
"HC2"
)
%>%
diag
%>%
sqrt
# present results
stargazer
(
reg1
,
reg1
,
se
=
list
(
ols_se
,
hc_se
),
type
=
'text'
)
# Predict deposits based on the selected model ----
# merge scenarios with actuals.na
fbase
<-
bind_rows
(
actual.na
,
data0
[
data0
$
scenario
==
"fbase"
,
])
fadvr
<-
bind_rows
(
actual.na
,
data0
[
data0
$
scenario
==
"fadvr"
,
])
# get out-of-sample predictions
fbase
$
p_fbase_p1
<-
predict
(
reg1
,
fbase
)
fadvr
$
p_fadvr_p1
<-
predict
(
reg1
,
fadvr
)
# delete un-wanted vars
fbase
<-
fbase
%>%
select
(
p_fbase_p1
,
date
,
scenario
,
Deposits
)
fadvr
<-
fadvr
%>%
select
(
p_fadvr_p1
,
date
,
scenario
,
Deposits
)
# export result
write_csv
(
fbase
,
'fbase.csv'
)
write_csv
(
fadvr
,
'fadvr.csv'
)
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