Hi Niccolò,
I understand that you want to know about Information Criteria functions and Johansen Cointegration tests in MATLAB.
The ‘VARselect’ function in R, returns information criteria and final prediction error for sequential increasing the lag order up to a VAR(p)-proccess.
You can model the Vector autoregression process VAR(p) using a ‘varm’ object specifying the functional form and storing the parameter values of a p-order, stationary, multivariate vector autoregression model (VAR(p)) model.
Further, in order to get the information criteria, use the ‘aicbic’ function. ‘aicbic’ computes information criteria given loglikelihood values obtained by fitting competing models to data.
To perform Johansen Cointegration tests, The ‘jcitest’ function in MATLAB returns the rejection decisions h from conducting the Johansen test, which assesses each null hypothesis H(r) of cointegration rank less than or equal to r among the n-dimensional multivariate time series Y against the alternative trace test or maxeig test. The tests produce maximum likelihood estimates of the parameters in a vector error-correction (VEC) model of the cointegrated series.
To learn more about Information Criteria and Johansen Cointegration tests, please refer to the below example:
I hope this helps!
