我正在R中进行数据分析，以获取Fama-French CAPM模型的预评估Beta。为了计算排名前的Beta，我必须对市场收益（M）进行最少24个，最大60个月收益（R）的回归分析，并保存每个月份的Beta（回归分析的第一个系数）（ 60个月后，我有342个月）。为了实现这一点，我创建了以下循环：
mydata <- read.csv("Austria.csv", header = T, sep = ";")
Betas <- matrix(nrow = 342, ncol = 1)
for(k in 1:282){
M <- mydata[k:59+k,3]
R <- mydata[k:59+k,4] - mydata[k:59+k,2]
criteria <- length(!is.na(R))
if (criteria >= 24) {
result <- lm(R ~ M, na.action = na.omit)
} else {
result <- "NA"
}
if (any(result != "NA")) {
coefficients <- coef(summary(result))
} else {
coefficients <- "NA"
}
if (any(coefficients != "NA")) {
Betas[k,1] <- coefficients[2,1]
} else {
Betas[k,1] <- "NA"
}
}
但是通过此过程生成的beta与excel中的回归beta不匹配。谁能帮帮我吗？
我正在使用的数据由342行和4列组成。我的数据的一部分在这里：
structure(list(Date = structure(c(188L, 217L, 246L, 275L, 304L,
333L, 20L, 48L, 76L, 104L, 132L, 160L, 189L, 218L, 247L, 276L,
305L, 334L, 21L, 49L, 77L, 105L, 133L, 161L, 190L, 219L, 248L,
277L, 306L, 335L, 22L, 50L, 78L, 106L, 134L, 162L, 191L, 220L,
249L, 278L, 307L, 336L, 23L, 51L, 79L, 107L, 135L, 163L, 192L,
221L, 250L, 279L, 308L, 337L, 24L, 52L, 80L, 108L, 136L, 164L,
193L, 222L, 251L, 280L, 309L, 338L, 25L, 53L, 81L, 109L, 137L,
165L, 194L, 223L, 252L, 281L, 310L, 339L, 26L, 54L, 82L, 110L,
138L, 166L, 195L, 224L, 253L, 282L, 311L, 340L, 27L, 55L, 83L,
111L, 139L, 167L, 196L, 225L, 254L, 283L), .Label = c("21.01.00",
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"21.12.95", "21.12.96", "21.12.97", "21.12.98", "21.12.99"), class = "factor"),
RF = c(0.0068, 0.0066, 0.006, 0.0068, 0.0057, 0.006, 0.0052,
0.0048, 0.0044, 0.0053, 0.0047, 0.0042, 0.0049, 0.0046, 0.0046,
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