Description Usage Arguments Value Functions Examples
All models are estimated using full information maximum likelihood. The
equilibrium_model
can also be estimated using twostage
least squares. The maximum likelihood estimation is based on
mle2
. If no starting values are provided, the function uses
linear regression estimates as initializing values. The default optimization method is
BFGS. For other alternatives see mle2
. The implementation of
the twostage least square estimation of the equilibrium_model
is based on systemfit
.
1 2 3 4 5 6 7 8 9 10 11 12 13 
object 
A model object. 
... 
Named parameter used in the model's estimation. These are passed further
down to the estimation call. For the 
gradient 
One of two potential options: 'numerical' and 'calculated'. By default, all the models are estimated using the analytic expressions of their likelihoods' gradients. 
hessian 
One of three potential options: 'skip', 'numerical', and 'calculated'. The default is to use the 'calculated' Hessian for the model that expressions are available and the 'numerical' Hessian in other cases. Calculated Hessian expressions are available for the basic and directional models. 
standard_errors 
One of three potential options: 'homoscedastic', 'heteroscedastic', or a vector with variables names for which standard error clusters are to be created. The default value is 'homoscedastic'. If the option 'heteroscedastic' is passed, the variancecovariance matrix is calculated using heteroscedasticity adjusted (HuberWhite) standard errors. If the vector is supplied, the variancecovariance matrix is calculated by grouping the score matrix based on the passed variables. 
method 
A string specifying the estimation method. When the passed value is
among 
The object that holds the estimation result.
estimate,market_modelmethod
: Full information maximum likelihood estimation.
estimate,equilibrium_modelmethod
: Equilibrium model estimation.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # initialize the model using the houses dataset
model < new(
"diseq_deterministic_adjustment", # model type
c("ID", "TREND"), "TREND", "HS", "RM", # keys, time, quantity, and price variables
"RM + TREND + W + CSHS + L1RM + L2RM + MONTH", # demand specification
"RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH", # supply specification
fair_houses(), # data
correlated_shocks = FALSE # allow shocks to be correlated
)
# estimate the model object (BFGS is used by default)
est < estimate(model)
# estimate the model by specifying the optimization details passed to the optimizer.
est < estimate(model, control = list(maxit = 1e+5), method = "BFGS")
# summarize results
bbmle::summary(est)

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