时间序列分析与r软件.doc

一、
:
da<-("d:/",header=T)
dim(da)
y=da[,1]
m=da[,2]
basicStats(m)
plot(y,m,type='l')
title(main='financial e of China:1978-2010')
:

> da<-("d:/",header=T)
> dim(da)
[1] 33 2
> y=da[,1]
> m=da[,2]
> basicStats(m)
m
nobs +01
NAs +00
Minimum +03
Maximum +04
1. Quartile +03
3. Quartile +04
Mean +04
Median +03
Sum +05
SE Mean +03
LCL Mean +03
UCL Mean +04
Variance +08
Stdev +04
Skewness +00
Kurtosis +00
> plot(y,m,type='l')
> title(main='financial e of China:1978-2010')
二、
:
da<-("d:/",header=T)
dim(da)
y=da[,1]
logm=log(da[,2])
basicStats(logm)
plot(y,logm,type='l')
title(main='financial e of China:1978-2010')
:

> da<-("d:/",header=T)
> dim(da)
[1] 33 2
> y=da[,1]
> logm=log(da[,2])
> basicStats(logm)
logm
nobs
NAs
Minimum
Maximum
1. Quartile
3. Quartile
Mean
Median
Sum
SE Mean
LCL Mean
UCL Mean
Variance
Stdev
Skewness
Kurtosis -
> plot(y,logm,type='l')
> title(main='financial e of China:1978-2010')
三、
:
da<-("d:/",header=T)
dim(da)
y=da[,1]
dy1=diff(log(da[,2]),lag=1)
basicStats(dy1)
plot(dy1,type='l')
title(main='financial e of China:1978-2010')
:

> da<-("d:/",header=T)
> dim(da)
[1] 33 2
> y=da[,1]
> dy1=diff(log(da[,2]),lag=1)
> basicStats(dy1)
dy1
nobs
NAs
Minimum
Maximum
1. Quartile
3. Quartile
Mean
Median
Sum
SE Mean
LCL Mean
UCL Mean
Variance
Stdev
Skewness -
Kurtosis -
四、