estimate  R Documentation 
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
.
estimate(object, ...) ## S4 method for signature 'market_model' estimate( object, gradient = "calculated", hessian = "calculated", standard_errors = "homoscedastic", ... ) ## S4 method for signature 'equilibrium_model' estimate(object, method = "BFGS", ...)
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: 
hessian 
One of three potential options: 
standard_errors 
One of three potential options:

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.
# initialize the model using the houses dataset model < new( "diseq_deterministic_adjustment", # model type subject = ID, time = TREND, quantity = HS, price = RM, demand = RM + TREND + W + CSHS + L1RM + L2RM + MONTH, supply = RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH, fair_houses(), # data correlated_shocks = FALSE # let shocks be independent ) # estimate the model object (BFGS is used by default) fit < estimate(model) # estimate the model by specifying the optimization details passed to the optimizer. fit < estimate(model, control = list(maxit = 1e+6)) # summarize results summary(fit)
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