[V,VData,Fitted] = estimate(V,Inp,Range,...)
V [ VAR ] - Empty VAR object.
Inp [ struct ] - Input database.
Range [ numeric ] - Estimation range, including P pre-sample periods, where P is the order of the VAR.
V [ VAR ] - Estimated reduced-form VAR object.
VData [ struct ] - Output database with the endogenous variables and the estimated residuals.
Fitted [ numeric ] - Periods in which fitted values have been calculated.
'A=' [ numeric | empty ] - Restrictions on the individual values in the transition matrix, A.
'BVAR=' [ numeric ] - Prior dummy observations for estimating a BVAR; construct the dummy observations using the one of the BVAR functions.
'C=' [ numeric | empty ] - Restrictions on the individual values in the constant vector, C.
'J=' [ numeric | empty ] - Restrictions on the individual values in the coefficient matrix in front of exogenous inputs, J.
'diff=' [ true | false ] - Difference the series before estimating the VAR; integrate the series back afterwards.
'G=' [ numeric | empty ] - Restrictions on the individual values in the coefficient matrix in front of the co-integrating vector, G.
'cointeg=' [ numeric | empty ] - Co-integrating vectors (in rows) that will be imposed on the estimated VAR.
'comment=' [ char | Inf ] - Assign comment to the estimated VAR object; Inf means the existing comment will be preserved.
'constraints=' [ char | cellstr ] - General linear constraints on the VAR parameters.
'constant=' [ true | false ] - Include a constant vector in the VAR.
'covParam=' [ true | false ] - Calculate and store the covariance matrix of estimated parameters.
'eqtnByEqtn=' [ true | false ] - Estimate the VAR equation by equation.
'maxIter=' [ numeric | 1 ] - Maximum number of iterations when generalised least squares algorithm is involved.
'mean=' [ numeric | empty ] - Impose a particular asymptotic mean on the VAR process.
'order=' [ numeric | 1 ] - Order of the VAR.
'progress=' [ true | false ] - Display progress bar in the command window.
'schur=' [ true | false ] - Calculate triangular (Schur) representation of the estimated VAR straight away.
'stdize=' [ true | false ] - Adjust the prior dummy observations by the std dev of the observations.
'timeWeights=' [ tseries | empty ] - Time series of weights applied to individual periods in the estimation range.
'tolerance=' [ numeric | 1e-5 ] - Convergence tolerance when generalised least squares algorithm is involved.
'warning=' [ true | false ] - Display warnings produced by this function.
'fixedEff=' [ true | false ] - Include constant dummies for fixed effect in panel estimation; applies only if 'constant=' true.
'groupWeights=' [ numeric | empty ] - A 1-by-NGrp vector of weights applied to groups in panel estimation, where NGrp is the number of groups; the weights will be rescaled so as to sum up to 1.
Panel VAR objects are created by calling the function VAR with two input arguments: the list of variables, and the list of group names. To estimate a panel VAR, the input data, Inp, must be organised a super-database with sub-databases for each group, and time series for each variables within each group:
d.Group1_Name.Var1_Name
d.Group1_Name.Var2_Name
...
d.Group2_Name.Var1_Name
d.Group2_Name.Var2_Name
...