[R] Estimating MA parameters through arima or through package "dlm"
Mark Leeds
markleeds2 at gmail.com
Mon Jan 4 17:31:22 CET 2016
Hi: I don't have time to look at the details of what you're doing but the
"equivalence"
between state space and arima ( as paul gilbert pointed out a few weeks ago
) is not a true equivalence.
if you are in an area of the parameter space that the state space
formulation
can't reach, then you won't get the same parameter estimates. so, what
you're doing
might be okay or might not be, depending on whether the state space
formulation
can reach that area of the parameter space. there's another state space
formulation that is truly equivalent which is called the SSOE formulation
or innovations representation but
I don't know if you want to get into that. google "SSOE state space" if
you're interested.
Mark
On Mon, Jan 4, 2016 at 9:25 AM, Stefano Sofia <
stefano.sofia at regione.marche.it> wrote:
> Dear list users,
> I want to use apply a MA(2) process (x=beta1*epsilon_(t-1) +
> beta2*epsilon_(t-1) + epsilon_(t)) to a given time series (x), and I want
> to estimate the two parameters beta1, beta2 and the variance of the random
> variable epsilon_(t).
>
> If I use
> MA2_1 <- Arima(x, order=c(0,0,2))
> I get the following result
>
> [1] "MA2_1"
> Series: x
> ARIMA(0,0,2) with non-zero mean
>
> Coefficients:
> ma1 ma2 intercept
> -0.0279 0.0783 5.3737
> s.e. 0.0667 0.0622 0.0245
>
> sigma^2 estimated as 0.1284: log likelihood=-92.63
> AIC=193.25 AICc=193.43 BIC=207.11
> [1] 0 2 0 0 1 0 0
>
> From this straightforward analysis V[epsilon]=0.1284, beta1=-0.0279 and
> beta2=0.0783.
>
> I also tried to use a DLM representation of ARIMA models and estimate the
> unknown parameters by maximum likelihood through the dlm package (in
> particular applying the example at section 3.2.6, page 115, of "Dynamic
> Linear Models with R" by Petris, Petrone and Campagnoli:
>
> arma_parameters <- function(x)
> {
> buildGap <- function(u)
> {
> gap <- dlmModARMA(ma = u[2 : 3], sigma2 = u[1])
> return(gap)
> }
> init <- c(0.005, 0.004, 0.003)
> outMLE <- dlmMLE(x, init, buildGap)
> dlmGap <- buildGap(outMLE$par)
> }
>
> and this gives:
> [1] "outMLE"
> $par
> [1] 1.00816794 0.02349296 0.02364788
>
> $value
> [1] 3089.196
>
> $counts
> function gradient
> 10 10
>
> $convergence
> [1] 0
>
> $message
> [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
>
> [1] "dlmGap"
> $FF
> [,1] [,2] [,3]
> [1,] 1 0 0
>
> $V
> [,1]
> [1,] 0
>
> $GG
> [,1] [,2] [,3]
> [1,] 0 1 0
> [2,] 0 0 1
> [3,] 0 0 0
>
> $W
> [,1] [,2] [,3]
> [1,] 1.00816794 0.0236848488 0.0238410337
> [2,] 0.02368485 0.0005564272 0.0005600964
> [3,] 0.02384103 0.0005600964 0.0005637899
>
> $m0
> [1] 0 0 0
>
> $C0
> [,1] [,2] [,3]
> [1,] 1e+07 0e+00 0e+00
> [2,] 0e+00 1e+07 0e+00
> [3,] 0e+00 0e+00 1e+07
>
> In this case
> V[epsilon]=W[1,1]=1.00816794
> beta1=W[2,1]/W[1,1]=0.02349296
> beta2=W[3,1]/W[1,1]=0.02364788
>
> I presume that these two approaches should give comparable results, but
> this does not happen.
> Is the model that I used correct? And does it make sense to perform this
> kind of comparison?
>
> This is the log of a rainfall time series (which has already been
> deseasonalised):
> [1] 6.014937 4.978801 5.654592 5.616771 5.612398 5.837147 5.121580 5.832176
> [9] 5.205654 5.355642 5.405376 6.257859 5.516247 5.500850 4.708629 5.482304
> [17] 5.689684 5.727824 4.779123 5.289277 5.217107 5.976351 4.630838
> 5.683240
> [25] 5.345678 5.906179 5.605434 5.497578 5.898801 5.660875 5.111988
> 5.571013
> [33] 5.949340 5.374352 4.841033 5.995706 5.661223 5.458734 4.454347
> 5.795754
> [41] 5.995706 5.596939 5.399971 5.908898 5.282696 5.438514 5.528635
> 6.022721
> [49] 5.524257 5.519459 4.957235 5.547518 5.080783 5.411200 5.056883
> 5.798183
> [57] 5.086361 5.536547 5.220356 5.141664 5.847017 5.052417 5.734635
> 5.340419
> [65] 5.724238 5.634432 5.685958 5.307773 5.817706 5.134032 4.987708
> 5.110179
> [73] 5.423628 5.347108 4.859037 5.556828 5.487283 5.661223 5.732370
> 5.469325
> [81] 5.726848 5.419207 5.172187 5.608006 5.130490 5.586874 5.171052
> 5.683240
> [89] 4.674696 5.286245 5.342813 5.370638 5.432411 5.748118 6.355239
> 5.557986
> [97] 5.399067 5.222516 5.279644 5.425390 5.540871 5.917818 5.132853
> 5.689007
> [105] 5.900993 5.007296 5.102911 5.778271 5.318120 5.927726 5.066385
> 5.716699
> [113] 5.511815 4.714921 5.383577 5.319100 5.269403 5.354698 5.145749
> 5.204556
> [121] 5.878296 5.070161 5.441552 5.213304 5.450180 5.695750 4.893352
> 5.425390
> [129] 5.682559 5.487283 4.213608 5.751620 5.432411 5.379436 5.700444
> 5.580484
> [137] 5.357529 5.319100 4.532599 5.603225 5.208393 5.254888 5.017280
> 5.349961
> [145] 4.374498 5.187944 5.585374 5.716370 3.561046 5.119789 5.163070
> 5.422745
> [153] 5.863915 5.651436 4.762174 5.655642 4.797442 5.735927 4.911183
> 5.240688
> [161] 5.148076 5.477300 4.572647 5.493473 5.437644 4.854371 4.908233
> 4.755313
> [169] 5.582744 5.527841 5.613128 5.211124 5.275049 5.462984 5.016617
> 5.981919
> [177] 5.566817 5.094364 5.314191 5.712742 5.299317 5.452325 4.691348
> 5.851628
> [185] 5.410753 5.488938 5.660179 5.900993 5.380819 5.256453 4.781641
> 5.531807
> [193] 5.497578 5.274537 4.325456 5.271973 5.077047 5.258536 5.280662
> 5.247024
> [201] 5.995208 4.700480 4.991113 5.457029 5.194622 5.487283 5.197391
> 5.747161
> [209] 5.842094 5.372497 5.306781 5.641907 5.565286 5.259057 5.241218
> 4.759607
> [217] 4.550714 5.230574 4.470495 5.664348 4.846547 5.771130 4.823502
> 5.598422
> [225] 5.627621 5.547518 5.596939 5.468482 5.536940 5.606170 5.281680
> 5.656691
> [233] 5.283204 5.752255 5.192401 4.550714
>
>
> Thank you for your attention and your help
> Stefano
>
>
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