simpleVAR2: Simple VAR estimation
In amvillegas/iMoMo: Mortality Improvement Rate Modelling

Function to fit VAR mole of the form:

Δ^d y_t = C+Dt+∑_{i=1}^p A_i Δ^d y_{t-i} + ε_t

where C and D are K-dimensional vectors for parameters and A_1,...,A_p are K\times K matrices of autoregressive parampeters.

1	simpleVAR2(y, p = 1, d = 0, type = c("const", "trend", "both", "none"))

`y`	a matrix containing the multivariate timeseries
`p`	integer for the lag order (default is `p=1`)
`d`	degree of differencing (default is `d=0`)
`type`	string indicating whether a constand a trend are included. If `"const"` only C is included. If `"trend"` only D is included. If `"both"` both C and D are included.

A list of class "simpleVAR2" with components:

`K`	Dimension of the vector y. `K=1` if univariate
`A`	A 3-dimensional array with the vector autregressive matrices, A_1,...,A_p.
`C`	Vector of constant parameters.
`D`	Vector of trend parametesr.
`p`	Integer indicating the lag order.
`d`	Degree of differencing.
`n`	number of time periods used in the fitting.
`Sigma`	Variance-covariance matrix.
`resid`	Residuals of the model.
`fittingModel`	Output of the model call underlying the fitting.
`y`	Data used in the fitting
`dy`	Difference data used in the fitting
`datamat`	Array including all hte lags used in fitting the model.