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Hi, I am using the mAr package to calibrate an Multivariate model (size 3, order 12). I am trying to do the two following things: 1. I would like to calibrate the model using not a single time series, but several of them: each time series should be seen as one "independent" realisation of the mAr process; for instance this happens when you have a time series with lacking data

Hello, I am currently testing a Vector AR of dim 3 over not a lot of data (135 * 3 observations) . To test the adequation of my vecot ar, I use the Schwarz Bayesian Criterion and the classic modified Portmanteau test on the residuals (it can be found for instance in http://www.iue.it/PUB/ECO2004-8.pdf , page 15) -> the null hypothesis is "the residuals process are a vectorila white

Hi, I am trying to use optim to solve a heavy calibration problem. I supply the parameters in vector form. But before entering my target The call is simply: optim(par = parameters, fn = SumLSQ, method = "Nelder-Mead") the function SumLSQ is simply: SumLSQ<-function(parameters, data = timeseries){ print("sumLSQ") nbseries =