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
estimation summary provided by systemfit in
addition to the model's summary output.
An object to be summarized.
summary,market_model-method: Summarizes the model.
summary,market_fit-method: Summarizes the model's fit.
# \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)
#> Stochastic Adjustment Model for Markets in Disequilibrium
#> Demand RHS : D_P + D_Xd1 + D_Xd2 + D_X1 + D_X2
#> Supply RHS : S_P + S_Xs1 + S_X1 + S_X2
#> Price Dynamics RHS: (D_Q - S_Q) + Xp1
#> Short Side Rule : Q = min(D_Q, S_Q)
#> Shocks : Correlated
#> Nobs : 1000
#> Sample Separation : Not Separated
#> Quantity Var : Q
#> Price Var : P
#> Key Var(s) : id, date
#> Time Var : date
# }