calibrate_national(dataset::Dataset, io::Dict, set::Dict)
calibrate_national(io::Dict, set::Dict, year::Integer)

Arguments

• d::Dict of DataFrames containing the model data.
• set::Dict of Arrays describing parameter indices (years, regions, goods, sectors, etc.)
• year::Int: year for which to perform calibration

Returns

• d::Dict of DataFrames containing the model data

_national_calibration_input(d::Dict, set::Dict, year::Int)
_national_calibration_input(d::Dict, set::Dict)

This function prepares the input for the calibration routine:

1. Select parameters relevant to the calibration routine.
2. For all parameters except taxes (ta0, tm0), set negative values to zero: $x = max\left\{0, x\right\}$
3. In the case of final demand, only set negative values to zero for fd = pce: $fd_{g,fd} = max\{0, fd_{g,fd}\}$
4. Fill all "missing" values with zeros to generate a complete dataset. This is relevant to how the penalty for missing keys is applied in the objective function.

Arguments

• d::Dict{Symbol,DataFrame}: all input parameters
• set::Dict of Arrays describing parameter indices (years, regions, goods, sectors, etc.)
• year::Int overwhich to calibrate

Returns

• d::Dict{Symbol, Dict}: input variables
• set::Dict of Arrays describing parameter indices (years, regions, goods, sectors, etc.), updated to include necessary set permutations.