滚动多个回归面板数据

如何解决滚动多个回归面板数据

对于正在观察其中18个月的公司，我试图在过去36个月中对时间t进行滚动回归，但是我无法使函数起作用。我只想要var1的系数。 X，y，z是控制变量。

这是我尝试运行的数据和代码示例。

structure(list(Year = c(2018,2014,2008,2004,2005,2002,2010,2013,1998),Month = c(6,12,4,6,8,11,3,3),ISIN = c("NO0004895103","NO0010571680","NO0010010473","NO0003079709","NO0003117202","NO0003073801","NO0010379266","NO0004913609","NO0003072407","NO0003679102"),SIZE = c(3637822300,1.155e+10,1925631048.5,519688494.5,790931587.08,127597142.4,2892152759.3,554108244.7,73484767,375946560),BM = c(7.47698486077218e-07,7.92202995594714e-07,1.9900582030005e-07,3.59385930497676e-07,6.54209144403066e-07,6.72227955087816e-07,1.43273711611857e-06,5.15824596832591e-07,1.23252873661613e-06,2.79726876987137e-07
    ),OP = c(0.145781283498513,0.0433749257278669,0.130477272126991,0.211294037715838,0.127523990874984,-0.0185266025249259,-0.0822835066759631,0.128844560922287,-0.0155446815227611,0.416024745410667),INVEST = c(1.58608106515088,0.00307252384303782,0.33925195328069,-0.0984424241606425,0.333479950150282,0.13302885162465,-0.035773912311751,0.408569401011161,-0.206778240645154,-0.177187857233583),MonthlyReturn = c(-0.019509251810137,0.095308641975309,0.14864864864865,-0.034364261168385,-0.064512147964095,-0.14080459770115,0.13580244980708,-0.1890214797136,-0.045226130653266,0.39682527166336),RiskFreeRate = c(0.00064,0.00117,0.00532,0.00163,0.00166,0.00594,0.00206,0.00499,0.00153,0.00332),ShareTurnover = c(31649L,907793L,5318465L,1831390L,8956640L,302000L,7333090L,516000L,75553L,2836550L),MarketExcessReturn = c(1.0813,2.7638,11.5701,6.4672,-3.3108,-1.8598,10.5445,-9.0969,-0.0793,10.8305),ExcessReturn = c(-2.0149251810137,9.4138641975309,14.332864864865,-3.5994261168385,-6.6172147964095,-14.674459770115,13.374244980708,-19.40114797136,-4.6756130653266,39.350527166336
    ),TradeDate = structure(c(17711,16434,13999,12599,12902,11929,14973,14211,15791,10316),class = "Date"),GR_SIZE = structure(c(2L,3L,2L,1L,1L),.Label = c("1","2","3"),class = "factor"),GR_OP = structure(c(2L,3L),GR_BM = structure(c(2L,1L
    ),GR_INVEST = structure(c(3L,SIZE_BM = structure(c(5L,8L,4L,5L,9L,.Label = c("11","12","13","21","22","23","31","32","33"),SIZE_OP = structure(c(5L,7L,SIZE_INVEST = structure(c(6L,6L,Date = structure(c(17683,16405,13970,12570,12874,11900,14944,14184,15765,10286
    ),EPU_Paper = c(197.436482473082,181.040599101032,58.1799902251583,62.5412044042803,57.4799138334861,52.420100605017,61.1549363311955,92.1818760618723,76.2564063202547,118.390708950295
    )),row.names = c(NA,-10L),class = "data.frame")

#merge dataframes
df_Final <- merge(df_Final,EPU_Paper,by = c("Year","Month"))

# Create empty dataframe
date <- seq(as.Date("1991-01-31"),as.Date("2019-12-30"),by = "month")

Beta <- data.frame(ISIN = character(),Beta = numeric(),date =
                          as.Date(character()),stringsAsFactors = FALSE)

## Model
Returns_length <- 36 # No. of observations in rolling regression 
Returns_req <- 18 # observations of stocks requred in period 


for (i in 1:(length(date)-Returns_length)) {
  beta.tab <- df_Final %>% filter(TradeDate >= date[i] & TradeDate < date[i+Returns_length]) %>%
    group_by(ISIN) %>% filter(n() >= Returns_req) %>%
    do(ols.model = lm(data = df_Final,formula = ExcessReturn ~ var1 + x + y + z)) %>%
    mutate(Beta = coef(ols.model)[2]) %>% select("ISIN","Beta") %>%
    mutate(TradeDate = date[Returns_length+i])
  Beta <- rbind(Beta,beta.tab)}

解决方法

问题中似乎有多个未定义的输入df_Final和EU_paper，但假设数据按时间顺序在组内，并且我们想将lm应用于w从当前行到w-1行，然后在末尾使用注释中生成的某些数据之前，我们定义一个coef.x函数来计算所需系数，并使用一个滚动函数roll（此处宽度为2），然后按组g应用它。

library(zoo)

w <- 2
nr <- nrow(DF)
coef.x <- function(ix) coef(lm(y ~ x + x1,DF,subset = ix))[["x"]]
roll <- function(ix) rollapplyr(ix,w,coef.x,fill = NA)
transform(DF,coef.x = ave(1:nr,g,FUN = roll))

给予：

  y              x            x1 g        coef.x
1 1 -0.56047564655  0.4609162060 1            NA
2 2 -0.23017748948 -1.2650612346 1  3.0275676040
3 3  1.55870831415 -0.6868528519 1  0.5590071753
4 4  0.07050839142 -0.4456619701 2            NA
5 5  0.12928773516  1.2240817974 2 17.0127792594
6 6  1.71506498688  0.3598138271 2  0.6306055904

这也可以使用dplyr来编写，其中w，roll和coef.x来自上面：

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

DF %>%
  group_by(g) %>%
  mutate(coef.x = roll(cur_group_rows())) %>%
  ungroup

注意

# test data
set.seed(123)
DF <- data.frame(y = 1:6,x = rnorm(6),x1 = rnorm(6),g = c(1,1,2,2))