r - 基于 R 中的列值（或日期）的滚动回归
问题描述
我有下表：
# A tibble: 40 x 5
# Groups: stock [1]
stock date mkt_cap week returns
<chr> <date> <dbl> <int> <dbl>
1 A 2019-03-04 10522834. NA NA
2 A 2019-03-05 11659707. NA 9.70
3 A 2019-03-06 11464531. NA -2.25
4 A 2019-03-07 12217241. NA 5.80
5 A 2019-03-08 11619351. 1 -5.57
6 A 2019-03-09 11578687. NA -0.899
7 A 2019-03-10 11658368. NA 0.141
8 A 2019-03-11 12722921. NA 8.20
9 A 2019-03-12 15429934. NA 18.8
10 A 2019-03-13 16801600. NA 7.98
11 A 2019-03-14 17898334. NA 5.79
12 A 2019-03-15 18492686. 2 2.74
13 A 2019-03-16 20686683. NA 10.7
14 A 2019-03-17 22299970. NA 6.98
15 A 2019-03-18 22924182. NA 2.24
16 A 2019-03-19 24174351. NA 4.79
17 A 2019-03-20 24661467. NA 1.48
18 A 2019-03-21 23351810. NA -5.97
19 A 2019-03-22 27826601. 3 17.0
20 A 2019-03-23 30670482. NA 9.22
21 A 2019-03-24 32802772. NA 6.21
22 A 2019-03-25 31778387. NA -3.68
23 A 2019-03-26 33237006. NA 3.99
24 A 2019-03-27 34971479. NA 4.59
25 A 2019-03-28 36774005. NA 4.53
26 A 2019-03-29 37594815. 4 1.71
27 A 2019-03-30 38321816. NA 1.42
28 A 2019-03-31 35167070. NA -9.08
29 A 2019-04-01 35625396. NA 0.808
30 A 2019-04-02 35764747. NA -0.0940
31 A 2019-04-03 28316242. NA -23.8
32 A 2019-04-04 26124803. NA -8.53
33 A 2019-04-05 30390295. 5 14.6
34 A 2019-04-06 28256485. NA -7.76
35 A 2019-04-07 29807837. NA 4.87
36 A 2019-04-08 30970364. NA 3.36
37 A 2019-04-09 30470093. NA -2.10
38 A 2019-04-10 30860276. NA 0.806
39 A 2019-04-11 27946472. NA -10.4
40 A 2019-04-12 27662766. 6 -1.48
在这张表上，我想运行一个滚动回归，其中滚动回归包含过去一个月的数据。我想在几周内运行这些滚动回归。也就是说，在 week==1、week ==2 等，我们使用过去一个月的数据。回归应该是lm(return~mkt_cap)。我使用 slide_period() 函数尝试了很多事情，但是，这对我来说并不奏效。例如，我尝试运行
tbl.data %>% group_by(stock, week) %>% slide_period(date, date, "month", ~.x, .before = 1). 我的数据存在一些空白，因此我更喜欢考虑日期的解决方案。
有人可以帮我吗？亲切的问候。
解决方案
我会使用 tidyverserowwise方法。
我不清楚应该如何按周创建模型并回到上个月。在下面的方法中，我max_date每周计算一次，然后返回 30 天。
# setup
library(tidyverse)
library(lubridate)
dat <- tribble(~stock, ~date, ~mkt_cap, ~week, ~returns,
"A", "2019-03-04", 10522834., NA, NA,
"A", "2019-03-05", 11659707., NA, 9.70,
"A", "2019-03-06", 11464531., NA, -2.25,
"A", "2019-03-07", 12217241., NA, 5.80,
"A", "2019-03-08", 11619351., 1, -5.57,
"A", "2019-03-09", 11578687., NA, -0.899,
"A", "2019-03-10", 11658368., NA, 0.141,
"A", "2019-03-11", 12722921., NA, 8.20,
"A", "2019-03-12", 15429934., NA, 18.8,
"A", "2019-03-13", 16801600., NA, 7.98,
"A", "2019-03-14", 17898334., NA, 5.79,
"A", "2019-03-15", 18492686., 2, 2.74,
"A", "2019-03-16", 20686683., NA, 10.7,
"A", "2019-03-17", 22299970., NA, 6.98,
"A", "2019-03-18", 22924182., NA, 2.24,
"A", "2019-03-19", 24174351., NA, 4.79,
"A", "2019-03-20", 24661467., NA, 1.48,
"A", "2019-03-21", 23351810., NA, -5.97,
"A", "2019-03-22", 27826601., 3, 17.0,
"A", "2019-03-23", 30670482., NA, 9.22,
"A", "2019-03-24", 32802772., NA, 6.21,
"A", "2019-03-25", 31778387., NA, -3.68,
"A", "2019-03-26", 33237006., NA, 3.99,
"A", "2019-03-27", 34971479., NA, 4.59,
"A", "2019-03-28", 36774005., NA, 4.53,
"A", "2019-03-29", 37594815., 4, 1.71,
"A", "2019-03-30", 38321816., NA, 1.42,
"A", "2019-03-31", 35167070., NA, -9.08,
"A", "2019-04-01", 35625396., NA, 0.808,
"A", "2019-04-02", 35764747., NA, -0.0940,
"A", "2019-04-03", 28316242., NA, -23.8,
"A", "2019-04-04", 26124803., NA, -8.53,
"A", "2019-04-05", 30390295., 5, 14.6,
"A", "2019-04-06", 28256485., NA, -7.76,
"A", "2019-04-07", 29807837., NA, 4.87,
"A", "2019-04-08", 30970364., NA, 3.36,
"A", "2019-04-09", 30470093., NA, -2.10,
"A", "2019-04-10", 30860276., NA, 0.806,
"A", "2019-04-11", 27946472., NA, -10.4,
"A", "2019-04-12", 27662766., 6, -1.48) %>%
mutate(date = as.Date(date)) %>%
fill(week, .direction = "up")
# summarised data.frame by week with min and max date
dat2 <- dat %>%
group_by(week) %>%
summarise(max_date = max(date),
min_date = max_date %m-% months(1))
#> `summarise()` ungrouping output (override with `.groups` argument)
# create the models
dat3 <- dat2 %>%
rowwise() %>%
mutate(mod = list(lm(returns ~ mkt_cap,
data = filter(dat,
date <= .env$max_date,
date >= .env$min_date))))
# get the relevant informationen per week
dat3 %>%
mutate(res = list(broom::tidy(mod)),
broom::glance(mod)) %>%
select(week,
res,
adj.r.squared,
mod_p.value = p.value,
nobs) %>%
unnest(res) %>%
filter(term != "(Intercept)")
#> # A tibble: 6 x 9
#> week term estimate std.error statistic p.value adj.r.squared mod_p.value
#> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 mkt_~ 1.01e-5 1.34e-5 0.756 0.529 -0.167 0.529
#> 2 2 mkt_~ 9.26e-7 7.45e-7 1.24 0.245 0.0520 0.245
#> 3 3 mkt_~ 2.56e-7 2.97e-7 0.864 0.400 -0.0152 0.400
#> 4 4 mkt_~ 2.00e-8 1.42e-7 0.141 0.889 -0.0426 0.889
#> 5 5 mkt_~ -1.18e-7 1.61e-7 -0.736 0.467 -0.0150 0.467
#> 6 6 mkt_~ -3.23e-7 2.37e-7 -1.37 0.182 0.0271 0.182
#> # ... with 1 more variable: nobs <int>
由reprex 包（v0.3.0）于 2021-04-27 创建
更新
当处理不止一只股票时，这种方法可以很容易地扩展：
# lets append the same data and change stock to "B":
dat <- dat %>%
bind_rows({mutate(., stock = "B")})
# summarised data.frame by week and group with min and max date
dat2 <- dat %>%
group_by(stock, week) %>%
summarise(max_date = max(date),
min_date = max_date %m-% months(1))
#> `summarise()` has grouped output by 'stock'. You can override using the `.groups` argument.
# create the models, and this time also filer for .env$stock
dat3 <- dat2 %>%
rowwise() %>%
mutate(mod = list(lm(returns ~ mkt_cap,
data = filter(dat,
stock == .env$stock,
date <= .env$max_date,
date >= .env$min_date))))
# get the relevant informationen per week (this stays the same!)
dat3 %>%
mutate(res = list(broom::tidy(mod)),
broom::glance(mod)) %>%
select(week,
res,
adj.r.squared,
mod_p.value = p.value,
nobs) %>%
unnest(res) %>%
filter(term != "(Intercept)")
#> Adding missing grouping variables: `stock`
#> # A tibble: 12 x 10
#> # Groups: stock [2]
#> stock week term estimate std.error statistic p.value adj.r.squared
#> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 A 1 mkt_cap 0.0000101 0.0000134 0.756 0.529 -0.167
#> 2 A 2 mkt_cap 0.000000926 0.000000745 1.24 0.245 0.0520
#> 3 A 3 mkt_cap 0.000000256 0.000000297 0.864 0.400 -0.0152
#> 4 A 4 mkt_cap 0.0000000200 0.000000142 0.141 0.889 -0.0426
#> 5 A 5 mkt_cap -0.000000118 0.000000161 -0.736 0.467 -0.0150
#> 6 A 6 mkt_cap -0.000000323 0.000000237 -1.37 0.182 0.0271
#> 7 B 1 mkt_cap 0.0000101 0.0000134 0.756 0.529 -0.167
#> 8 B 2 mkt_cap 0.000000926 0.000000745 1.24 0.245 0.0520
#> 9 B 3 mkt_cap 0.000000256 0.000000297 0.864 0.400 -0.0152
#> 10 B 4 mkt_cap 0.0000000200 0.000000142 0.141 0.889 -0.0426
#> 11 B 5 mkt_cap -0.000000118 0.000000161 -0.736 0.467 -0.0150
#> 12 B 6 mkt_cap -0.000000323 0.000000237 -1.37 0.182 0.0271
#> # … with 2 more variables: mod_p.value <dbl>, nobs <int>
由reprex 包（v0.3.0）于 2021-04-27 创建
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