基于滚动子集将数据分类为十分位数

Question

我正在尝试使用 R 复制 Fama French 1993 论文。我需要进行以下排序：

1. 每个月，
2. 仅计算 NYSE 股票的 ME 十分位断点
3. 将所有股票按 2 中创建的十分位数排序。

数据生成：

    set.seed(1234)
    n = 120
    stocks <- c("A", "B", "C", "D", "E")
    exchange <- c("NYSE", "NASDAQ", "AMEX")
    df <- as.data.frame(cbind(Month = 1:12,
                      exchangeCode = exchange[round(runif(n, 1, 3))],
                      Stock = stocks[round(runif(n, 1, 5))],
                      ME=floor(100*abs(rnorm(n)))))

期望的输出：

ME_NYSE_vals <- as.numeric(paste(df[df$Month==1 & df$exchangeCode=="NYSE","ME"]))

ME_ALL_vals <- as.numeric(paste(df[df$Month==1,"ME"]))

cut(x = ME_ALL_vals,
breaks = c(-Inf,quantile(ME_NYSE_vals,probs=seq(.1,.9,.1)),+Inf),
labels = 1:10
)

应根据 ME_NSYE_vals 计算休息时间。削减应适用于每个月的所有 ME_ALL_vals。

Answer 1

如果打算保留整个数据框，但只为 NYSE 值生成十分位数，则下面的代码可以执行此操作。关键是只为与 NYSE 值相关的条目生成十分位数，但要保持完整的数据集实现某种形式的部分排序。

# Libs
Vectorize(require)(package = c("dplyr", "magrittr"),
                   character.only = TRUE)
# Transformations
df %<>%
    mutate(nTileNYSE = ifelse(exchangeCode == "NYSE", ntile(ME, 10), NA))
    arrange(nTileNYSE)

代码已应用于数据：

set.seed(1)
df <- as.data.frame(cbind(exchangeCode = c("NYSE", "NASDAQ"), 
                          Stock = c("A", "B", "C", "A"), 
                          Month = 1:12,
                          ME=rnorm(1200)))

---

第二种方法

根据 cmets 中的讨论，我建议采用以下方法：

# Libs --------------------------------------------------------------------

Vectorize(require)(package = c( "tidyr", "dplyr", "magrittr", "xts", "Hmisc"),
                   char = TRUE)

# Data generation ---------------------------------------------------------

set.seed(1234)
n = 120
stocks <- c("A", "B", "C", "D", "E")
exchange <- c("NYSE", "NASDAQ", "AMEX")
df <- as.data.frame(cbind(Month = 1:12,
                          exchangeCode = exchange[round(runif(n, 1, 3))],
                          Stock = stocks[round(runif(n, 1, 5))],
                          ME = floor(100*abs(rnorm(n)))))

# Transformations ---------------------------------------------------------

# For some reason this was needed
df$ME <- as.numeric(as.character(df$ME))

# Generate cuts
dfNtiles <- df %>% 
  arrange(exchangeCode, Month, ME) %>% 
  group_by(exchangeCode, Month) %>% 
  mutate(cutsBsdOnNYSE = cut(x = ME, 
                             breaks = cut2(x = df$ME[df$exchangeCode == "NYSE"],
                                           g = 10, onlycuts = TRUE))) %>% 
  ungroup() %>% 
  group_by(cutsBsdOnNYSE) %>% 
  mutate(grpBsdOnNYSE = n())

很简单

• 生成反映数据子集的切割括号。

• 将这些括号应用于整个向量(ME)

• 对获得的组进行编号以便创建组标识符

  归结为：