AR(2) coefficient interpretation
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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