advancehs / MTED-TabloToR

A package that can interpret GEMPACK-style TABLO models in R and solve them

Package tabloToR

A package that can interpret GEMPACK-style TABLO models in R and solve them

To install, you can try the following:

install.packages('devtools')
devtools::install_git('https://github.com/mivanic/tabloToR.git')

To perform a simulation, you can try the following:

model = tabloToR::GEModel$new()

# You need to have the model, such as gtap.tab 
model$loadTablo('gtap.tab')

# You need to get the data files .har
data = list(
  # The file with GTAP sets may alternatively be called sets.har by some data aggregation programs
  gtapsets = HARr::read_har('gsdgset.har'),
  # The file with GTAP parameters may alternatively be called default.prm by some data aggregation programs
  gtapparm = HARr::read_har('gsdgpar.har'),
  # The file with GTAPdata may alternatively be called basedata.har by some data aggregation programs
  gtapdata = HARr::read_har('gsdgdat.har')
)

# Initialize the model object

model$loadData(data)

# Set up the closure

for(var in c("afall",
      "afcom",
      "afeall",
      "afecom",
      "afereg",
      "afesec",
      "afreg",
      "afsec",
      "ams",
      "aoall",
      "aoreg",
      "aosec",
      "atall",
      "atd",
      "atf",
      "atm",
      "ats",
      "au",
      "avaall",
      "avareg",
      "avasec",
      "cgdslack",
      "dpgov",
      "dppriv",
      "dpsave",
      "endwslack",
      "incomeslack",
      "pfactwld",
      "pop",
      "profitslack",
      "psaveslack",
      "tf",
      "tfd",
      "tfm",
      "tgd",
      "tgm",
      "tm",
      "tms",
      "to",
      "tpd",
      "tpm",
      "tp",
      "tradslack",
      "tx",
      "txs")){
  model$variableValues[[var]][]=0
}

model$variableValues$qo[model$data$endw_comm,] = 0

# Specify sets for shocks 

ag= c("grains", "v_f", "osd", "c_b", "pfb", "ocr", "ctl", "oap", "rmk", "wol")
model$variableValues$aoall[ag,c("northam")] = 1

agFood = c("grains", "v_f", "osd", "c_b", "pfb", "ocr", "ctl", "oap", "rmk", "wol", "food")

model$variableValues$tms[agFood,,c("northam")] = (model$data$viws[agFood,,c("northam")] / model$data$vims[agFood,,c("northam")] -1)*100

# Run the model
model$solveModel(iter = 3,steps = c(1,3))

# View the results (variable ev--welfare)
model$data$ev