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Could someone please explain the differences between the 3 fitting methods, method = c("CSS-ML", "ML", "CSS"), in Arima? If I run the code below I get an error message, but if I specify method="ML" in Arima it runs fine. So I was curious what the difference was between the 3 fitting methods.

Code with error:

library("fpp")

tsTrain <- window(hsales,end=1989.99)

pvar<-1:10
dvar<-1:2
qvar<-1:7

OrderGrid<-expand.grid(pvar,dvar,qvar)

n <- function(a,b,c) {Arima(tsTrain, order=c(a,b,c))}
ModFit <- do.call(Vectorize(n, SIMPLIFY=FALSE), unname(OrderGrid))

Fixed Code:

library("fpp")

tsTrain <- window(hsales,end=1989.99)

pvar<-1:10
dvar<-1:2
qvar<-1:7

OrderGrid<-expand.grid(pvar,dvar,qvar)

n <- function(a,b,c) {Arima(tsTrain, order=c(a,b,c),method="ML")}
ModFit <- do.call(Vectorize(n, SIMPLIFY=FALSE), unname(OrderGrid))
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  • $\begingroup$ Have you tried the help file first and then some time series textbook (e.g. Hamilton's) for further detail? $\endgroup$ – Richard Hardy Apr 28 '16 at 6:35
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According to the documentation, this is how each method fits the model:

  • CSS minimises the sum of squared residuals.

  • ML maximises the log-likelihood function of the ARIMA model.

  • CSS-ML mixes both methods: first, CSS is run, the starting parameters for the optimization algorithm are set to zeros or to the values given in the optional argument init; then, ML is applied passing the CSS parameter estimates as starting parameter values for the optimization algorithm.

In a model with lags of the dependent variable, a initial set of observations must be somehow defined in order to evaluate the sum of squares or the likelihood function. CSS and ML deal with this issue differently. CSS sets the initial observations to zeros, while ML uses the initial state vector returned by the Kalman filter. ML is supposed to be more accurate since, instead of setting these initial observations to zeros, it uses the sample data in order to get an estimate of these values.

For some details about the initialisation of the Kalman filter you may see this post or this post.

Notice that one thing is the starting parameter values and another is the set of initial observations. The former can be specified through the argument init, while the latter are specified internally by each method as described before. This distinction is important in order to not get confused when reading the documentation.


In your code, the following error is obtained for the ARIMA(10,1,2) model:

Error in optim(init[mask], armafn, method = optim.method, hessian = TRUE,  : 
  non-finite finite-difference value [5]

Roughly, this means that the optimization algorithm failed to reach a result. The error arises for method="ML" (actually for the ML step of the CSS-ML method). A further insight shows that the initial parameter values obtained from CSS are apparently not good enough. The following gives error:

arima(tsTrain, order=c(10,1,2), method="CSS-ML")

while using zeros (the default) as starting parameters the optimization algorithm converges to a solution:

arima(tsTrain, order=c(10,1,2), method="ML")

At first glance, this may seem strange, since the CSS step is supposed to provide starting parameters relatively close to those that maximise the likelihood function. A look at the autocorrelations of the data acf(tsTrain); pacf(tsTrain) suggests that the model ARIMA(10,1,2) is not plausible for the data, in fact this model is far from the model chosen by forecast::auto.arima, ARMA(1,0,0)(2,0,0). This may explain why estimates from CSS were not reliable as input for ML.

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