As I need your urgent help so let me modify my question. I imported the
following data set to R and run the statements i mentioned in my previous
reply
Year Month Period a b c
1 2008 Jan 2008-Jan 105,536,785 9,322,074 9,212,111
2 2008 Feb 2008-Feb 137,239,037 10,986,047 11,718,202
3 2008 Mar 2008-Mar 130,237,985 10,653,977 11,296,096
4 2008 Apr 2008-Apr 133,634,288 10,582,305 11,729,520
5 2008 May 2008-May 161,312,530 13,486,695 13,966,435
6 2008 Jun 2008-Jun 153,091,141 12,635,693 13,360,372
7 2008 Jul 2008-Jul 176,063,906 13,882,619 15,202,934
8 2008 Aug 2008-Aug 193,584,660 14,756,116 16,083,263
9 2008 Sep 2008-Sep 180,894,120 13,874,154 14,524,268
10 2008 Oct 2008-Oct 196,691,055 14,998,119 15,802,627
11 2008 Nov 2008-Nov 184,977,893 13,748,124 14,328,875
and the AR result is
Call:
arima(x = a, order = c(2, 0, 0))
Coefficients:
ar1 ar2 intercept
0.4683 0.4020 5.8654
s.e. 0.2889 0.3132 2.8366
sigma^2 estimated as 4.115: log likelihood = -24.04, aic = 56.08
The minimum mount of a is more than 100 million and the intercept is 5.86
based on the result above.
If I placed all values into the formula then Xt=5.8654+0.4683*(184,977,893
)+0.4020*(196,691,055 )= 165,694,957.27. Do you think that makes sense? Did
i interpret the result incorrectly?
Also, i submit the following statement for the prediction of next period
> predict<-predict(fit, n.ahead=1)
> predict
it came out the value of 9.397515 below and I have no idea about how to
interpret this value. Please help.
$pred
Time Series:
Start = 12
End = 12
Frequency = 1
[1] 9.397515
$se
Time Series:
Start = 12
End = 12
Frequency = 1
[1] 2.028483
Stephen Oman wrote:
>
> I am a beginner in using R and I need help in the interpretation of AR
> result by R. I used 12 observations for my AR(2) model and it turned out
> the intercept showed 5.23 while first and second AR coefficients showed
> 0.40 and 0.46. It is because my raw data are in million so it seems the
> intercept is too small and it doesn't make sense. Did i make any mistake
> in my code? My code is as follows:
>
> r<-read.table("data.txt", dec=",", header=T)
> attach(r)
> fit<-arima(a, c(2,0,0))
>
> Thank you for your help first.
>
>
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