run_cp2015: Run a simulation using the Caliendo and Parro (2015)...

In paulofelipe/cp2015: Caliendo and Parro (2015) Quantitative Trade Model

Run a simulation using the Caliendo and Parro (2015) quantitative trade model

Description

Caliendo and Parro (2015) develop a Ricardian quantitative trade model that considers multiple countries and multiple sectors. The model allows interactions across sectors based on Input-Output linkages.

The model is originally used to study the trade and welfare effects of NAFTA given the observed tariff changes. However, from data provided by the user, this model can be used for simulations to assess the effects of different trade policies (changes in tariffs and/or iceberg trade costs).

Usage

run_cp2015(
  data,
  zero_aggregate_deficit = FALSE,
  ufactor = 0.5,
  tol = 1e-07,
  maxiter = 10000,
  verbose = TRUE,
  triter = 100,
  nthreads = 1
)

Arguments

data	a List with the model data. Run help(cp2015_nafta) to see the required data.
zero_aggregate_deficit	a boolean indicating whether the simulation should impose zero aggregate deficits.
ufactor	an update factor between (0, 1]. This value is used to update the value of variables at each iteration.
tol	a tolerance number for convergence. The default is 1e-7.
maxiter	the number of maximum iterations.
verbose	a boolean indicating whether convergence information should be printed.
triter	an integer indicating that information should be printed for each multiple of that number.
nthreads	an integer indicating the number of threads to use.

Value

A list with 13 elements:

• c_nj_hat (changes in cost an input bundle) - a data.frame with 3 columns:

  • region

  • sector

  • c_nj_hat (relative change)

• P_nj_hat (changes in the region-sector price index) - a data.frame with 3 columns:

  • region

  • sector

  • P_nj_hat (relative change)

• pi_nij (bilateral trade share) - a data.frame with 5 columns:

  • importer

  • exporter

  • sector

  • pi_bln (trade share in the baseline scenario)

  • pi_cfl (trade share in the counterfactual scenario)

• X_nj (total expenditure) - a data.frame with 4 columns:

  • region

  • sector

  • X_bln (expenditure in the baseline scenario)

  • X_cfl (expenditure in the counterfactual scenario)

• I_n (regional income) - a data.frame with 3 columns:

  • region

  • I_bln (regional income in the baseline scenario)

  • I_cfl (regional income in the counterfactual scenario)

• P_n_hat (consumer price index) - a data.frame with 2 columns:

  • region

  • P_n_hat (relative change)

• w_n_hat ("wages") - a data.frame with 2 columns:

  • region

  • w_hat (relative change)

• trade (trade data) - a data.frame with 9 columns:

  • importer

  • exporter

  • sector

  • tau_bln (tariffs in the baseline scenario)

  • tau_cfl (tariffs in the counterfactual scenario)

  • d_bln (relative changes of the iceberg trade costs in the baseline scenario)

  • d_cfl (relative changes of the iceberg trade costs in the counterfactual scenario)

  • trade_bln (trade value, net of tariffs, in the baseline scenario)

  • trade_cfl (trade value, net of tariffs, in the counterfactual scenario)

• tot (terms of trade) - a data.frame with 4 columns:

  • partner

  • region

  • sector

  • tot (contribution of terms of trade to welfare in %)

• vot (volume of trade) - a data.frame with 4 columns:

  • region

  • partner

  • sector

  • vot (contribution of volume of trade to welfare in %)

• tech (technical efficiency) - a data.frame with 4 columns:

  • region

  • partner

  • sector

  • tech (contribution of technical efficiency to welfare in %)

• welfare (total welfare by region) - a data.frame with 6 columns:

  • region

  • tot (contribution of terms of trade to welfare in %)

  • vot (contribution of volume of trade to welfare in %)

  • tech (contribution of technical efficiency to welfare in %)

  • welfare (total welfare in %)

  • realwage (relative change in the real wage).

• convergence_info (info about the solution) - a data.frame with 3 variables:

  • scenario

  • criteria_value

  • message

References

Lorenzo Caliendo, Fernando Parro, Estimates of the Trade and Welfare Effects of NAFTA, The Review of Economic Studies, Volume 82, Issue 1, January 2015, Pages 1–44, https://doi.org/10.1093/restud/rdu035

Examples

## Not run: 
  data("cp2015_nafta")
  
  results_without_deficits <- run_cp2015(
    data = cp2015_nafta,
    zero_aggregate_deficit = TRUE,
    verbose = TRUE
  )
  
  results_with_deficits <- run_cp2015(
    data = cp2015_nafta,
    zero_aggregate_deficit = FALSE,
    verbose = TRUE
  )

## End(Not run)

paulofelipe/cp2015 documentation built on Nov. 24, 2024, 11:46 p.m.