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假设我要回归预测模型：Return_t = x + Volume_t-1 + Volatility_t-1 + e。我有一个 5 年的每周面板数据，其中 28 家公司已经在 excel 中准备好了，看起来像这样：

ID  Date        Return      Volume       Volatility
1   2012-01-10  0.039441572 0.6979594    0.2606079
1   2012-01-17 -0.021107681 0.6447289    0.3741519
1   2012-01-24  0.004798082 1.0072677    0.3097104
1   2012-01-31  0.001559987 1.0066153    0.2761096
1   2012-02-07 -0.009058289 0.7218983    0.2592109
1   2012-02-14  0.046404936 1.2879986    0.4304542
2   2012-01-10  0.02073912 -0.141970906  0.2573633
2   2012-01-17 -0.00369127  0.007792180  0.3360240
2   2012-01-24 -0.05881038  0.001347634  0.2163933
2   2012-01-31 -0.05664598  0.640085029  0.3545598
2   2012-02-07  0.03654193  0.360513703  0.3594383
2   2012-02-14  0.03092432  0.105669775  0.3043643

我想将自变量设置为 t-1，哪个包允许我在 R 中做到这一点？我将运行具有固定效应的面板数据回归。

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2 回答 2

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按“ID”分组后，我们可以使用lagfromdplyr

library(dplyr)
df1 %>% 
  group_by(ID) %>%
  mutate(Volume_1 = lag(Volume), Volatility_1 = lag(Volatility))

或者另一个选项shift来自data.table

library(data.table)
nm1 <- c("Volume", "Volatility")
setDT(df1)[, paste0(nm1, "_1") := lapply(.SD, shift), by = ID, .SDcols = nm1]
于 2017-06-10T10:15:55.570 回答
0

您也可以使用mutate_at然后加入：

df %>% 
  mutate_at(4:5, lag) %>% 
  left_join(df, ., by = c('ID','Date','Return'))

输出：

   ID       Date       Return     Volume.x Volatility.x     Volume.y Volatility.y
1   1 2012-01-10  0.039441572  0.697959400    0.2606079           NA           NA
2   1 2012-01-17 -0.021107681  0.644728900    0.3741519  0.697959400    0.2606079
3   1 2012-01-24  0.004798082  1.007267700    0.3097104  0.644728900    0.3741519
4   1 2012-01-31  0.001559987  1.006615300    0.2761096  1.007267700    0.3097104
5   1 2012-02-07 -0.009058289  0.721898300    0.2592109  1.006615300    0.2761096
6   1 2012-02-14  0.046404936  1.287998600    0.4304542  0.721898300    0.2592109
7   2 2012-01-10  0.020739120 -0.141970906    0.2573633  1.287998600    0.4304542
8   2 2012-01-17 -0.003691270  0.007792180    0.3360240 -0.141970906    0.2573633
9   2 2012-01-24 -0.058810380  0.001347634    0.2163933  0.007792180    0.3360240
10  2 2012-01-31 -0.056645980  0.640085029    0.3545598  0.001347634    0.2163933
11  2 2012-02-07  0.036541930  0.360513703    0.3594383  0.640085029    0.3545598
12  2 2012-02-14  0.030924320  0.105669775    0.3043643  0.360513703    0.3594383
于 2017-06-10T10:22:28.040 回答