GDP vs business confidence correlation

business-confidence.R

# Setup
library(conflicted)
library(tidyverse)
library(lubridate)
library(readxl)

conflict_prefer("filter", "dplyr")
conflict_prefer("lag", "dplyr")
conflict_prefer("lead", "dplyr")

# Read GDP data and calculate growth rates
# Seasonally adjusted quarterly real gdp
gdp_seas_adj <- read_csv("SNE446901_20190808_074504_68.csv", 
                         skip = 2, 
                         col_types = "ci", 
                         col_names = c("quarter", "gdp_seas_adj")) %>%
  filter(!is.na(gdp_seas_adj)) %>%
  separate(quarter, into = c("year", "quarter"), sep = "Q", convert = TRUE) %>%
  mutate(qtr_gdp_seas_adj_growth = 100 * (gdp_seas_adj / lag(gdp_seas_adj) - 1), 
         yoy_gdp_seas_adj_growth = 100 * (gdp_seas_adj / lag(gdp_seas_adj, 4) - 1))

# Un-adjsuted quarterly real gdp
gdp <- read_csv("SNE445001_20190808_094414_16.csv", 
                skip = 2, 
                col_types = "ci", 
                col_names = c("quarter", "gdp")) %>%
  filter(!is.na(gdp)) %>%
  separate(quarter, into = c("year", "quarter"), sep = "Q", convert = TRUE) %>%
  mutate(yoy_gdp_growth = 100 * (gdp / lag(gdp, 4) - 1))

# Read business confidence data
bus_conf <- read_excel("ANZ_Business_Outlook_Data.xlsx", 
                       sheet = "BusConf", 
                       skip = 5, 
                       col_types = c("date", rep("numeric", 6)), 
                       col_names = c("date_month", "conf_all", "conf_retail", "conf_mfg", "conf_ag", "conf_con", "conf_serv"), 
                       na = "...") %>%
  filter(!is.na(conf_all)) %>%
  mutate(date_month = as_date(date_month)) %>%
  mutate(quarter = quarter(date_month), 
         year = year(date_month))

# Calculate quarterly average business confidence
qtr_avg_bus_conf <- bus_conf %>%
  group_by(quarter, year) %>%
  summarise_at(vars(starts_with("conf")), mean, na.rm = TRUE) %>%
  arrange(year, quarter)

# Combine GDP and confidence data
combined_dat <- left_join(gdp_seas_adj, 
                          gdp, 
                          by = c("quarter", "year")) %>%
  left_join(qtr_avg_bus_conf, by = c("quarter", "year"))

# Calculate correlation of seasonally adjusted GDP growth with conf_all
gdp_qtr_seas_adj_gr_conf_cor <- function(l) {
  y <- cor(combined_dat$qtr_gdp_seas_adj_growth, 
           lag(combined_dat$conf_all, l), 
           use = "complete.obs")
  return(y)
}

gdp_yoy_seas_adj_gr_conf_cor <- function(l) {
  y <- cor(combined_dat$yoy_gdp_seas_adj_growth, 
           lag(combined_dat$conf_all, l), 
           use = "complete.obs")
  return(y)
}

gdp_yoy_gr_conf_cor <- function(l) {
  y <- cor(combined_dat$yoy_gdp_growth, 
           lag(combined_dat$conf_all, l), 
           use = "complete.obs")
  return(y)
}

gdp_conf_cor_vs_lag <- 
  tibble(bus_conf_lag = 0:8) %>%
  rowwise() %>%
  mutate(cor_qtr_seas_adj_gr = gdp_qtr_seas_adj_gr_conf_cor(bus_conf_lag),
         cor_yoy_seas_adj_gr = gdp_yoy_seas_adj_gr_conf_cor(bus_conf_lag), 
         cor_yoy_gr = gdp_yoy_gr_conf_cor(bus_conf_lag))

# Regression of year-on-year GDP growth vs confidence
summary(lm(yoy_gdp_growth ~ lag(conf_all, 4), data = combined_dat))