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Commit 28af92ae authored by Angie Liu's avatar 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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