Create the variable labels used in the estimation
ParaLabelsOpt(ModelType, WishStationarityQ, MLEinputs, BS_outputs = FALSE)list containing starting values and constraints: for each input argument K (of f), we need four inputs that look like:
a starting value: K0
a variable label ('K0') followed by a ':' followed by a type of constraint. The constraint can be:
'bounded': bounded matrix;
'Jordan' or 'Jordan MultiCountry': a matrix of Jordan type;
'psd': psd matrix;
'stationary': largest eigenvalue of the risk-neutral feedback matrix is strictly smaller than 1;
'diag' or 'BlockDiag': a diagonal or block diagonal matrix.
'JLLstructure': to impose the zero-restrictions on the variance-voriance matrix along the lines of the JLL models
a lower bound lb (lb <- NULL -> no lower bound)
an upper bound ub (ub <- NULL -> no upper bound)
Specification of the optimization settings:
'iter off': hide the printouts of the numerical optimization routines;
'fminunc only': only uses fminunc for the optimization;
''fminsearch only': only uses fminsearch for the optimization.
a string-vector containing the label of the model to be estimated
User must set "1" is she wishes to impose the largest eigenvalue under the Q to be strictly smaller than 1. Otherwise set "0"
Set of inputs that are necessary to the log-likelihood function
Generates simplified output list in the bootstrap setting. Default is set to FALSE.