R - Looping linear regression results for time series - r

Question

I want to run linear regressions on the NZD vs a number of securities
I have some code to runs the regression but rather than apply it to each security i would prefer to run a loop through the list of securities to give me a file with the r^2 results from each linear regression
my dep variable is called: nzdusd
independent variables I would like to loop through are spx, adxy, vix
Code: as it currently stands with spx (like to use the same code to loop it through for variables adxy and vix as well)
library(tseries)
library(lmtest)
library(dplyr)
library(lubridate)
# 3 month regression, change variable here to get number of days
# e.g. 3 months sd = 60
# inputs
# 3 month regression
sd <- 60
# loading my market data from a saved location (variables nzdusd,spx, adxy, vix)
my_path <- file.path ("K:","X,"bbg_daily.Rdata")
load(file = my_path)
# Transform NZD into percentage change
pct.nzdusd <- nzdusd %>%
select(date, PX_LAST) %>%
mutate(lag = lag(PX_LAST),
pct_chg = (PX_LAST - lag) * 100 / lag) %>%
select(date, pct_chg)
# SPX(S&P 500)
myfun <- function(x) {
deparse(substitute(x))
}
# ^=^=^=^=^=^=^=^=^=^=^=^=^=^=
mysec_str <- myfun(spx)
mysec <- spx
z <- 5 # Series ID
# ^=^=^=^=^=^=^=^=^=^=^=^=^=^=
# Transform into percentage change
mypct <- mysec %>%
select(date, PX_LAST) %>%
mutate(lag = lag(PX_LAST),
pct_chg = (PX_LAST - lag) * 100 / lag) %>%
select(date, pct_chg)
assign(paste("pct.", mysec_str, sep = ""),mypct)
# join times series
ts <- paste("ts_", z, sep ="")
ts <- (inner_join(x = pct.nzdusd, y = mypct, by = "date"))
# get last row
last_row <- ts %>% slice(n())
end_dt <- last_row [1,1]
# start date declared above depending on regression
start_dt <- ts[((nrow (ts))-sd),1]
# getting subset of time series
ts_sub <- subset(ts,
date >= as.POSIXct(start_dt) &
date <= as.POSIXct(end_dt))
# regression
reg.ts = lm(pct_chg.x~pct_chg.y, ts_sub)
r2 <- summary(reg.ts)$r.squared
assign(paste(mysec_str, ".r2", sep = ""),r2)
stderr <- sqrt(deviance(reg.ts)/df.residual(reg.ts))
assign(paste(mysec_str, ".stderr", sep = ""),stderr)
#===================================================
r2 <- c(spx.r2, *adxy.r2, vix.r2*)
my_path2 <- file.path ("K:","x")
save (r2, file = my_path2 )
I've done code by simply copying and pasting and then replacing spx with the other variable names. But i know the code can be a lot slicker by using a loop. Particularily if I want to add a lot more independent variables

Answer 1

It's hard to known without reprex data, but to run multiple models, I've found pivoting longer, nesting by independent variables and then mutating through those variables works well. If your data just contains your dependent and independent variables, you can:
library(tidyverse)
ts_sub %>%
# Keep independent variable outside nested data
pivot_longer(- nzdusd, names_to = "dependent_vars", values_to = "values") %>%
nest_by(dependent_vars) %>%
mutate(model = list(lm(nzdusd ~ values, data = data)))
See: https://dplyr.tidyverse.org/reference/nest_by.html