Market model simulation — market_simulation • markets

Market data and model simulation functionality based on the data generating process induced by the market model specifications.

`simulate_data`

Returns a data frame with simulated data from a generating process that matches the passed model string. By default, the simulated observations of the controls are drawn from a normal distribution.

`simulate_model`

Simulates a data frame based on the generating process of the passed model and uses it to initialize a model object. Data are simulated using the simulate_data function.

Usage

simulate_data(
  model_type_string,
  nobs = NA_integer_,
  tobs = NA_integer_,
  alpha_d = NA_real_,
  beta_d0 = NA_real_,
  beta_d = NA_real_,
  eta_d = NA_real_,
  alpha_s = NA_real_,
  beta_s0 = NA_real_,
  beta_s = NA_real_,
  eta_s = NA_real_,
  gamma = NA_real_,
  beta_p0 = NA_real_,
  beta_p = NA_real_,
  sigma_d = 1,
  sigma_s = 1,
  sigma_p = 1,
  rho_ds = 0,
  rho_dp = 0,
  rho_sp = 0,
  seed = NA_integer_,
  price_generator = function(n) stats::rnorm(n = n),
  control_generator = function(n) stats::rnorm(n = n),
  verbose = 0
)

# S4 method for ANY
simulate_data(
  model_type_string,
  nobs = NA_integer_,
  tobs = NA_integer_,
  alpha_d = NA_real_,
  beta_d0 = NA_real_,
  beta_d = NA_real_,
  eta_d = NA_real_,
  alpha_s = NA_real_,
  beta_s0 = NA_real_,
  beta_s = NA_real_,
  eta_s = NA_real_,
  gamma = NA_real_,
  beta_p0 = NA_real_,
  beta_p = NA_real_,
  sigma_d = 1,
  sigma_s = 1,
  sigma_p = 1,
  rho_ds = 0,
  rho_dp = 0,
  rho_sp = 0,
  seed = NA_integer_,
  price_generator = function(n) stats::rnorm(n = n),
  control_generator = function(n) stats::rnorm(n = n),
  verbose = 0
)

simulate_model(
  model_type_string,
  simulation_parameters,
  seed = NA,
  verbose = 0,
  correlated_shocks = TRUE
)

# S4 method for ANY
simulate_model(
  model_type_string,
  simulation_parameters,
  seed = NA,
  verbose = 0,
  correlated_shocks = TRUE
)

Arguments

model_type_string: Model type. It should be among equilibrium_model, diseq_basic, diseq_directional, diseq_deterministic_adjustment, and diseq_stochastic_adjustment.
nobs: Number of simulated entities.
tobs: Number of simulated dates.
alpha_d: Price coefficient of demand.
beta_d0: Constant coefficient of demand.
beta_d: Coefficients of exclusive demand controls.
eta_d: Demand coefficients of common controls.
alpha_s: Price coefficient of supply.
beta_s0: Constant coefficient of supply.
beta_s: Coefficients of exclusive supply controls.
eta_s: Supply coefficients of common controls.
gamma: Price equation's stability factor.
beta_p0: Price equation's constant coefficient.
beta_p: Price equation's control coefficients.
sigma_d: Demand shock's standard deviation.
sigma_s: Supply shock's standard deviation.
sigma_p: Price equation shock's standard deviation.
rho_ds: Demand and supply shocks' correlation coefficient.
rho_dp: Demand and price shocks' correlation coefficient.
rho_sp: Supply and price shocks' correlation coefficient.
seed: Pseudo random number generator seed.
price_generator: Pseudo random number generator callback for prices. The default generator is \(N(0, 1)\).
control_generator: Pseudo random number generator callback for non-price controls. The default generator is \(N(0, 1)\).
verbose: Verbosity level.
simulation_parameters: List of parameters used in model simulation. See the simulate_data function for details.
correlated_shocks: Should the model be estimated using correlated shocks?

Value

`simulate_data`

The simulated data.

`simulate_model`

The simulated model

Functions

simulate_data(): Simulate model data.
simulate_model(): Simulate model.

Examples

# \donttest{
model <- simulate_model(
  "diseq_stochastic_adjustment", list(
    # observed entities, observed time points
    nobs = 500, tobs = 3,
    # demand coefficients
    alpha_d = -0.1, beta_d0 = 9.8, beta_d = c(0.3, -0.2), eta_d = c(0.6, -0.1),
    # supply coefficients
    alpha_s = 0.1, beta_s0 = 6.1, beta_s = c(0.9), eta_s = c(-0.5, 0.2),
    # price equation coefficients
    gamma = 1.2, beta_p0 = 3.1, beta_p = c(0.8)
  ),
  seed = 31
)
# }