使用“R”的时间序列建模中缺少日期值

Question

我试图通过重现 this post 来直观地了解时间序列在金融市场中的使用。由于无法访问博客中使用的数据集，因此我使用了 GOOG 代码以及 quantmod 和 tseries 库：

library(quantmod)
library(tseries)

getSymbols("GOOG")
str(GOOG) # We start with an xts

序列不稳定，需要差分：

GOOG_stationary = 100 * diff(log(GOOG$GOOG.Adjusted)) # Made stationary

现在，当我尝试运行博客中要求的时间序列模型时，我收到如下错误消息：

GOOG_stationary = 100 * diff(log(GOOG$GOOG.Adjusted)) # Made stationary
summary(arma(GOOG_stationary, order = c(2,2)))
Error in summary(arma(GOOG_stationary, order = c(2, 2))) : 
  error in evaluating the argument 'object' in selecting a method for function 'summary': 
Error in arma(GOOG_stationary, order = c(2, 2)) : NAs in x

似乎日期中有 NA 个值，但我不知道这些是周末还是其他间隔。实际价格中没有 NA 值：sum(is.na(GOOG$GOOG.Adjusted)) [1] 0，或日期中：sum(is.na(index(GOOG))) [1] 0.

可能是周末和节假日的问题。如果是这样，如何处理？

Answer 1

只需排除 NAs。在这种情况下只是第一个。

GOOG_stationary = 100 * diff(log(GOOG$GOOG.Adjusted))[-1]

summary(arma(GOOG_stationary, order = c(2,2)))

Call:
arma(x = GOOG_stationary, order = c(2, 2))

Model:
ARMA(2,2)

Residuals:
      Min        1Q    Median        3Q       Max 
-12.41416  -0.86057  -0.02153   0.91053  18.17041 

Coefficient(s):
           Estimate  Std. Error  t value Pr(>|t|)  
ar1        -0.19963          NA       NA       NA  
ar2         0.04969     0.65183    0.076   0.9392  
ma1         0.18210          NA       NA       NA  
ma2        -0.06049     0.66539   -0.091   0.9276  
intercept   0.05303     0.02783    1.905   0.0567 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Fit:
sigma^2 estimated as 3.62,  Conditional Sum-of-Squares = 8685.37,  AIC = 9916.97

使用“R”的时间序列建模中缺少日期值

Missing Date values in time series modeling using `R`

r

time-series

finance