Description Usage Arguments Details Value Functions See Also Examples
Market side aggregation.
1 2 3 4 5 6 7 8 9  aggregate_demand(object, parameters)
## S4 method for signature 'market_model'
aggregate_demand(object, parameters)
aggregate_supply(object, parameters)
## S4 method for signature 'market_model'
aggregate_supply(object, parameters)

object 
A model object. 
parameters 
A vector of model's parameters. 
Calculates the sample's aggregate demand or supply at the passed set of
parameters. If the model is static, as is for example the case of
equilibrium_model
, then all observations are aggregated. If the
used data have a time dimension and aggregation per date is required, it can be
manually performed using the demanded_quantities
and
supplied_quantities
functions. If the model has a dynamic component,
such as the diseq_deterministic_adjustment
, then demanded
and supplied quantities are automatically aggregated for each time point.
The sum of the estimated demanded or supplied quantities evaluated at the given parameters.
aggregate_demand
: Demand aggregation.
aggregate_supply
: Supply aggregation.
demanded_quantities, supplied_quantities
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38  # initialize the basic model using the houses dataset
model < new(
"diseq_basic", # model type
c("ID", "TREND"), "HS", "RM", # keys, 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)
# get estimated aggregate demand
aggregate_demand(model, est@coef)
# simulate the deterministic adjustment model
model < simulate_model(
"diseq_deterministic_adjustment", list(
# observed entities, observed time points
nobs = 500, tobs = 3,
# demand coefficients
alpha_d = 0.6, beta_d0 = 9.8, beta_d = c(0.3, 0.2), eta_d = c(0.6, 0.1),
# supply coefficients
alpha_s = 0.2, beta_s0 = 4.1, beta_s = c(0.9), eta_s = c(0.5, 0.2),
# price equation coefficients
gamma = 0.9
), seed = 1356
)
# estimate the model object
est < estimate(model)
# get estimated aggregate demand
aggregate_demand(model, est@coef)
# get estimated aggregate demand
aggregate_supply(model, est@coef)

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