使用`R`的时间序列建模中缺少日期值

Question

I'm trying to get an intuitive idea of the use of time series in financial markets by attempting to reproduce this post . Since the dataset used in the blog is not accessible, I have used instead the GOOG ticker and the quantmod and tseries libraries:

library(quantmod)
library(tseries)

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

The series is not stationary calling for differencing:

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

Now when I try to run a time series model as called for in the blog, I get an error message as follows:

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

It seems as though there are NA values in the dates, but I don't know if these are weekends, or other gaps. There are no NA values in the actual prices: sum(is.na(GOOG$GOOG.Adjusted)) [1] 0 , or in the dates: sum(is.na(index(GOOG))) [1] 0 .

It is likely to be a problem with weekends and holidays . If this is the case, how can it be handled?

Answer 1

Just exclude the NAs . In this case just the first.

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