Rolling Window Regression by group in R (with dates) - r

Question

THIS IS MY DATA
I have a panel data in R, so I want to create a rolling window linear regression by group. For instance, I have a lot of dates from 1 to 618. Each number represents one date, but I have more than one observation for each date.
I want to create a rolling window for 20 dates. Finally, i want to output all coefficients for lm(y~x1+x2+x3+x4+x5+x6) in the period 1:20, and make a rolling window for doing another regression for 2:21, 3:22.. and so on for all my observations, so the last coefficients are for 598:618 period (I have 618 so i can´t do it manually).
My problem is that i select a window for 20 observations but i only get to select this 20 first observations, for example:
1
1
1
1
1
1
1 .... 1
and maybe the first 20 observations are only observations for the first date (1), because there are more than one observations by date. So I want to catch 20 observationes filtering by group, actually this will be more than 20 observations, but i want to rolling by date (date 1 to date 20, regardless of the observations.
After that, i need to estimate by Newey West method, so i need include in the final code something like that and output all coefficients and t-statistics.
neweywest <- coeftest(LMOBJECT, vcov. = NeweyWest, lag=12)
I hope it has been understood well.

Answer 1

You can create multiple linear models for a given interval of dates like this:
library(tidyverse)
# example data
set.seed(1337)
n_dates <- 10
data <- tibble(
date = runif(100, min = 1, max = n_dates) %>% floor(),
x1 = runif(100)**2,
x2 = runif(100) * 2,
x3 = runif(100) + 2,
y = x1 + 2 * x2 + runif(100)
) %>%
arrange(date)
data
#> # A tibble: 100 × 5
#> date x1 x2 x3 y
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0.754 0.700 2.21 2.79
#> 2 1 0.0230 1.97 2.70 4.89
#> 3 1 0.388 0.500 2.21 1.54
#> 4 1 0.225 0.135 2.87 0.849
#> 5 1 0.00000810 0.139 2.22 1.12
#> 6 1 0.255 0.893 2.21 2.25
#> 7 1 0.402 1.37 2.06 3.51
#> 8 1 0.00275 0.363 2.68 0.984
#> 9 2 0.238 1.68 2.53 3.98
#> 10 2 0.0309 1.47 2.05 3.69
#> # … with 90 more rows
# number of rows per day
data %>% count(date)
#> # A tibble: 9 × 2
#> date n
#> <dbl> <int>
#> 1 1 8
#> 2 2 10
#> 3 3 15
#> 4 4 8
#> 5 5 10
#> 6 6 10
#> 7 7 12
#> 8 8 7
#> 9 9 20
# size of rolling window in days
window_size <- 3
models <- tibble(
from = seq(n_dates),
to = from + window_size - 1
) %>%
mutate(
data = from %>% map2(to, ~ data %>% filter(date >= .x & date <= .y)),
model = data %>% map(possibly(~ lm(y ~ x1 + x2 + x3, data = .x), NA))
)
models
#> # A tibble: 10 × 4
#> from to data model
#> <int> <dbl> <list> <list>
#> 1 1 3 <tibble [33 × 5]> <lm>
#> 2 2 4 <tibble [33 × 5]> <lm>
#> 3 3 5 <tibble [33 × 5]> <lm>
#> 4 4 6 <tibble [28 × 5]> <lm>
#> 5 5 7 <tibble [32 × 5]> <lm>
#> 6 6 8 <tibble [29 × 5]> <lm>
#> 7 7 9 <tibble [39 × 5]> <lm>
#> 8 8 10 <tibble [27 × 5]> <lm>
#> 9 9 11 <tibble [20 × 5]> <lm>
#> 10 10 12 <tibble [0 × 5]> <lgl [1]>
models %>%
filter(!is.na(model)) %>%
transmute(
from, to,
coeff = model %>% map(coefficients),
r2 = model %>% map_dbl(~ .x %>% summary() %>% pluck("r.squared"))
) %>%
unnest_wider(coeff)
# A tibble: 9 x 7
# from to `(Intercept)` x1 x2 x3 r2
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 1 3 0.601 0.883 2.07 -0.0788 0.970
#2 2 4 0.766 0.965 2.01 -0.141 0.965
#3 3 5 0.879 0.954 1.94 -0.165 0.953
Another way of subseting groups is to use nest:
# get all observations from day 3 to 5
data %>% arrange(date) %>% nest(-date) %>% slice(3:5) %>% unnest()