ParaLabelsOpt function

Create the variable labels used in the estimation

ParaLabelsOpt(ModelType, WishStationarityQ, MLEinputs, BS_outputs = FALSE)

Arguments

• ModelType: a string-vector containing the label of the model to be estimated
• WishStationarityQ: User must set "1" is she wishes to impose the largest eigenvalue under the Q to be strictly smaller than 1. Otherwise set "0"
• MLEinputs: Set of inputs that are necessary to the log-likelihood function
• BS_outputs: Generates simplified output list in the bootstrap setting. Default is set to FALSE.

Returns

list containing starting values and constraints: for each input argument K (of f), we need four inputs that look like:

1. a starting value: K0

2. 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

3. a lower bound lb (lb <- NULL -> no lower bound)

4. an upper bound ub (ub <- NULL -> no upper bound)

5. 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.