diseq (version 0.3.1)

market_aggregation: Market side aggregation.

Description

Market side aggregation.

Usage

aggregate_demand(object, parameters)
# S4 method for market_model
aggregate_demand(object, parameters)
aggregate_supply(object, parameters)
# S4 method for market_model
aggregate_supply(object, parameters)

Arguments

object

A model object.

parameters

A vector of model's parameters.

Value

The sum of the estimated demanded or supplied quantities evaluated at the given parameters.

Functions

• aggregate_demand: Demand aggregation.

• aggregate_supply: Supply aggregation.

Details

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.

See Also

demanded_quantities, supplied_quantities

Examples

# NOT RUN {
# 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)
# }