在R中对每组进行5年时间窗的滚动回归

ff29svar 于 2023-01-15 发布在其他

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我有变量为industry_sales、year和industry的数据。
我希望在5年的滚动窗口中对每个行业进行时间序列回归，因变量为industry_sales，自变量为year。例如，如果数据范围为2000-2010年，则每个回归和行业组的滚动窗口将为2000-2004、2001-2005，依此类推。
我尝试了一次，但代码失败：

library(tidyverse)
library(zoo)

# Group the data by industry
data_grouped <- data %>% 
  group_by(industry) 
glimpse(data_grouped)

# Define a function to run a regression
regression_func <- function(x) {
  lm(industry_sales ~ year, data = x)
}

# Perform rolling linear regression on each group, with a window of 5 years
results_list <- data_grouped %>% 
  do({
    rollapply(data_grouped$year, width = 5, FUN = regression_func, by = 1)
})

dput（）输出的数据如下所示，任何帮助将不胜感激：

structure(list(year = c(2010, 2011, 2012, 2013, 2014, 2015, 2016, 
2017, 2018, 2019, 2020, 2021, 2022, 2010, 2011, 2012, 2013, 2014, 
2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2010, 2011, 2012, 
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2010, 
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 
2022, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 
2020, 2021, 2022, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 
2018, 2019, 2020, 2021, 2022, 2010, 2011, 2012, 2013, 2014, 2015, 
2016, 2017, 2018, 2019, 2020, 2021, 2022, 2010, 2011, 2012, 2013, 
2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022), industry_sales = c(853.218, 
1001.856, 987.741, 990.679, 1061.804, 985.904, 1073.43, 1305.779, 
1314.722, 1188.122, 1327.245, 1711.143, 577.358, 25731.969, 26484.263, 
23085.259, 24842.509, 30563.672, 34947.361, 40066.425, 38293.972, 
40484.096, 37160.342, 29109.755, 35227.521, 16522.915, 1072310.255, 
1314543.982, 1288932.701, 1245467.184, 1169743.543, 882325.562, 
804108.99, 966832.252, 1099655.803, 999685.99, 725094.688, 797072.967, 
59906.738, 131274.107, 167635.266, 170003.001, 161892.01, 149722.289, 
108169.702, 92950.89, 120837.727, 132130.143, 121670.84, 118206.596, 
147732.623, 542.17, 201842.321, 209721.793, 215722.823, 223158.977, 
221191.213, 223076.061, 229199.539, 242995.929, 256145.995, 257282.085, 
234455.443, 214560.191, 13964.787, 88190.421, 90191.422, 80848.451, 
93999.434, 92325.688, 99138.466, 98682.173, 95106.591, 97622.097, 
94254.621, 81145.338, 87421.329, 12116.901, 247825.263, 262355, 
235423.37, 236697.987, 245287.766, 222221.931, 219657.82, 222990.939, 
224672.689, 162633.722, 139853.429, 148534.588, 4463.853, 28177.129, 
29298.618, 29251.077, 31696.968, 31636.033, 33064.57, 35745.408, 
32034.079, 31978.646, 31661.88, 28572.857, 34190.976, 3400.497
), industry = c("Agriculture, Forestry and Fishing", "Agriculture, Forestry and Fishing", 
"Agriculture, Forestry and Fishing", "Agriculture, Forestry and Fishing", 
"Agriculture, Forestry and Fishing", "Agriculture, Forestry and Fishing", 
"Agriculture, Forestry and Fishing", "Agriculture, Forestry and Fishing", 
"Agriculture, Forestry and Fishing", "Agriculture, Forestry and Fishing", 
"Agriculture, Forestry and Fishing", "Agriculture, Forestry and Fishing", 
"Agriculture, Forestry and Fishing", "Construction", "Construction", 
"Construction", "Construction", "Construction", "Construction", 
"Construction", "Construction", "Construction", "Construction", 
"Construction", "Construction", "Construction", "Manufacturing", 
"Manufacturing", "Manufacturing", "Manufacturing", "Manufacturing", 
"Manufacturing", "Manufacturing", "Manufacturing", "Manufacturing", 
"Manufacturing", "Manufacturing", "Manufacturing", "Manufacturing", 
"Mining", "Mining", "Mining", "Mining", "Mining", "Mining", "Mining", 
"Mining", "Mining", "Mining", "Mining", "Mining", "Mining", "Retail Trade", 
"Retail Trade", "Retail Trade", "Retail Trade", "Retail Trade", 
"Retail Trade", "Retail Trade", "Retail Trade", "Retail Trade", 
"Retail Trade", "Retail Trade", "Retail Trade", "Retail Trade", 
"Services", "Services", "Services", "Services", "Services", "Services", 
"Services", "Services", "Services", "Services", "Services", "Services", 
"Services", "Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Transportation, Communications, Electric, Gas and Sanitary service", 
"Wholesale Trade", "Wholesale Trade", "Wholesale Trade", "Wholesale Trade", 
"Wholesale Trade", "Wholesale Trade", "Wholesale Trade", "Wholesale Trade", 
"Wholesale Trade", "Wholesale Trade", "Wholesale Trade", "Wholesale Trade", 
"Wholesale Trade")), row.names = c(NA, -104L), class = c("tbl_df", 
"tbl", "data.frame"))

来源：https://stackoverflow.com/questions/75077944/conduct-rolling-regression-in-windows-of-5-years-for-each-group-in-r

2条答案

按热度按时间

a2mppw5e1#

存在以下几个问题：

问题中的代码将data$year的子向量window传递给回归函数，但回归函数定义为接受数据框参数，而不是年份向量。
目前还不清楚最终的输出是什么。2可能是一个以行业、年份和各种统计数据为列的数据框架，或者如果只有一个统计数据，可能是一个以年份为行、行业为列的二维结果。3请参阅以下获取这些结果的方法。
**1）**我们假设对于每个回归，我们需要行业、窗口的最后一年、截距、斜率和R^2的一行。修改c（...）以更改所需的统计量。例如，如果需要大量统计量，则为c(year = tail(yr, 1), unlist(broom::glance(fm)))。

这将按industry分组，然后使用group_modify为每个行业创建行。

library(dplyr, exclude = c("filter", "lag"))
library(zoo)

out <- data %>%
  group_by(industry) %>%
  group_modify(~ {
    .x$year %>%
       rollapplyr(5, function(yr) {
         fm <- lm(industry_sales ~ year, .x, subset = year %in% yr)
         c(year = tail(yr, 1), intercept = coef(fm)[[1]], slope = coef(fm)[[2]], 
           r.squared = summary(fm)$r.squared)
       }) %>%
       as.data.frame
  }) %>% 
  ungroup

给出：

> out
# A tibble: 72 × 5
   industry                           year intercept  slope r.squared
   <chr>                             <dbl>     <dbl>  <dbl>     <dbl>
 1 Agriculture, Forestry and Fishing  2014   -80707.  40.6     0.704 
 2 Agriculture, Forestry and Fishing  2015    -7481.   4.22    0.0433
 3 Agriculture, Forestry and Fishing  2016   -32534.  16.7     0.362 
 4 Agriculture, Forestry and Fishing  2017  -128244.  64.2     0.605 
 5 Agriculture, Forestry and Fishing  2018  -165315.  82.6     0.741 
 6 Agriculture, Forestry and Fishing  2019  -129070.  64.6     0.503 
 7 Agriculture, Forestry and Fishing  2020   -77455.  39.0     0.317 
 8 Agriculture, Forestry and Fishing  2021  -164845.  82.3     0.428 
 9 Agriculture, Forestry and Fishing  2022   193469. -95.2     0.134 
10 Construction                       2014 -1587815. 802.      0.208 
# … with 62 more rows
# ℹ Use `print(n = ...)` to see more rows

**2）**如果你只需要一个统计量--比如斜率--那么用一个二维动物园序列，每个行业一列，每个年份一行，可能会更方便。注意，斜率是平移不变的，所以我们可以只使用1：5作为每种情况下的预测因子，我们将得到与使用年份相同的斜率。

library(zoo)
z <- read.zoo(data, split = "industry")
out2 <- rollapplyr(z, 5, function(x) coef(lm(x ~ seq(5)))[[2]])

给出：

> out2[1:3, 1:3]
     Agriculture, Forestry and Fishing Construction Manufacturing
2014                           40.5995     802.1652      12578.98
2015                            4.2159    2440.4609     -98362.60
2016                           16.6603    4406.7184    -133278.90

如果您希望将其作为 Dataframe ，请使用fortify.zoo(out2)或长格式fortify.zoo(out2, melt = TRUE)。

赞(0）回复(0）举报 2023-01-15

pxy2qtax2#

这里有一个使用slider::slide2()而不是zoo::rollapply()的解决方案，它将生成一个命名的嵌套列表，其中包含嵌套在industries中的5年期结果。

library(tidyverse)
library(slider)

rolling_lm <- function(x, y) {
  res <- slide2(x, y, \(x, y) lm(y ~ x), .after = 5, .complete = TRUE)
  names(res) <- slide_chr(x, \(x) paste(min(x), "-", max(x)), .after = 5, .complete = TRUE)
  res[!is.null(res)]
}

results_list <- data %>%
  split(.$industry) %>%
  map(\(grp) rolling_lm(grp$year, grp$industry_sales))

# preview first 3 results
results_list[1:3]

$`Agriculture, Forestry and Fishing`
$`Agriculture, Forestry and Fishing`$`2010 - 2015`

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
  -47676.99        24.18  

$`Agriculture, Forestry and Fishing`$`2011 - 2016`

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
   -23345.5         12.1  

$`Agriculture, Forestry and Fishing`$`2012 - 2017`

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
 -100379.38        50.36

注意，这里假设每年有一行没有缺失或重复的年份，如果不是这样，slide2_index()或slide2_period()可能更有用;参见introductory vignette。

赞(0）回复(0）举报 2023-01-15

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在R中对每组进行5年时间窗的滚动回归

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