markets (version 1.1.5)

summary: Model and fit summaries

Description

Methods that summarize models and their estimates.

market_model: Prints basic information about the passed model object. In addition to the output of the show method, summary prints

• the number of observations,

• the number of observations in each equation for models with sample separation, and

• various categories of variables.

market_fit: Prints basic information about the passed model fit. In addition to the output of the model's summary method, the function prints basic estimation results. For a maximum likelihood estimation, the function prints

• the used optimization method,

• the maximum number of allowed iterations,

• the relative convergence tolerance (see optim),

• the convergence status,

• the initializing parameter values,

• the estimated coefficients, their standard errors, Z values, and P values, and

• \(-2 \log L\) evaluated at the maximum.

For a linear estimation of the equilibrium system, the function prints

• the used method,

• the summary of the first stage regression,

• the summary of the demand (second stage) regression, and

• the summary of the supply (second stage) regression.

Usage

# S4 method for market_model
summary(object)
# S4 method for market_fit
summary(object)

Value

No return value, called for for side effects (print summary).

Arguments

object

An object to be summarized.

Methods (by class)

• summary(market_model): Summarizes the model.

• summary(market_fit): Summarizes the model's fit.

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 = 5.1, 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 = 556
)

# print model summary
summary(model)

# estimate
fit <- estimate(model)

# print estimation summary
summary(fit)
# }