[R] Forecasting MA model different to manually computation?
neumancohu at gmail.com
Wed May 22 17:41:13 CEST 2013
Thanks, but this does not help me, because first of all, I do not know
how to look at the source code (just entering fitted() or
getAnywhere(fitted()) does not help,
second, your solution x-m$residuals does not be a solution, because
then the question is, where do the residuals come from?
2013/5/22 Rui Barradas <ruipbarradas at sapo.pt>:
> Since R is open source, you can look at the source code of package forecast
> to know exactly how it is done. My guess would be
> x - m$residuals
> Time Series:
> Start = 1
> End = 3
> Frequency = 1
>  3.060660 4.387627 3.000000
> Hope this helps,
> Rui Barradas
> Em 22-05-2013 15:13, Neuman Co escreveu:
>> 3 down vote favorite
>> I am interested in forecasting a MA model.Therefore I have created a
>> very simple data set (three variables). I then adapted a MA(1) model
>> to it. The results are:
>> Series: x
>> ARIMA(0,0,1) with non-zero mean
>> ma1 intercept
>> -1.0000 3.5000
>> s.e. 0.8165 0.3163
>> sigma^2 estimated as 0.5: log likelihood=-3.91
>> AIC=13.82 AICc=-10.18 BIC=11.12
>> While the MA(1) model looks like this:
>> and a_t is White Noise.
>> Now, I look at the fitted values:
>> Time Series:
>> Start = 1
>> End = 3
>> Frequency = 1
>>  3.060660 4.387627 3.000000
>> I tried different ways, but I cant find out how the fitted values
>> (3.060660, 4.387627 and 3.000000) are calculated.
>> Any help would be very appreciated.
>> Neumann, Conrad
>> R-help at r-project.org mailing list
>> PLEASE do read the posting guide
>> and provide commented, minimal, self-contained, reproducible code.
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