3  Quickstart

This chapter gets you from a fresh install to a solved CGE model in as few steps as possible, using the freely available GTAP 10 database and the classic GTAP model (version 6.2).

Before continuing, make sure you have completed all prerequisites in the Dependencies chapter:

3.1 Generate example scripts

ems_example() writes a set of ready-to-run R scripts to disk, with your data paths already injected at the top of each file. Point it at your HAR files and a project directory:

library(teems)

paths <- ems_example(
  model     = "GTAPv6",
  type      = "scripts",
  dat_input = "v6_data/gsddat.har",
  par_input = "v6_data/gsdpar.har",
  set_input = "v6_data/gsdset.har",
  write_dir = "~/my_project"
)

paths is a character vector of the written script file paths. Each script is self-contained and immediately runnable — no further editing is required to get a first run off the ground.

3.2 Step 1: verify the solver with a null run

Run null.R first. A null run applies no shocks, so every model variable should return zero. Note that the examples below use source() to run a specific script but for interactive runs opening the scripts generated by ems_example() within RStudio is recommended.

source(paths[basename(paths) == "null.R"])

# null.R performs these checks automatically:
check        # TRUE  — all variable values are 0
var_check    # TRUE  — output contains the expected number of variables
coeff_check  # TRUE  — output contains the expected number of coefficients

3.3 Step 2: run a uniform shock

full_uniform.R applies a 1% population shock (pop) uniformly across all regions. It is the simplest non-trivial run and a good sanity check before customizing anything.

source(paths[basename(paths) == "full_uniform.R"])

# full_uniform.R checks that the shock came through:
check  # TRUE — all pop values equal 1 (percentage change)

outputs is a tibble where each row corresponds to a solved variable or coefficient. The dat list column holds the result data for that row in data.table format.

# Inspect results for one variable
outputs$dat$pop

See the Composing results chapter for a full guide to working with outputs.

3.4 Next steps

The other scripts in paths cover the full range of shock and swap configurations available for GTAPv6. Recommended progression:

Script Try it when…
part_uniform.R You want to shock a single region or sector
part_uniform_part_swap.R You need to change the closure for part of the model
custom_full.R You have heterogeneous shock values from external data
full_uniform.R with a different variable You want to explore other exogenous variables

For a deeper look at each script pattern and how to customize them, see the Example files chapter. For data aggregation options (region and sector mappings), see Loading and specifying sets.