diseq (version 0.4.6)

market_aggregation: Market side aggregation.

Description

Market side aggregation.

Usage

aggregate_demand(fit, model, parameters)
# S4 method for missing,market_model,ANY
aggregate_demand(model, parameters)
aggregate_supply(fit, model, parameters)
# S4 method for missing,market_model,ANY
aggregate_supply(model, parameters)
# S4 method for market_fit,missing,missing
aggregate_demand(fit)
# S4 method for market_fit,missing,missing
aggregate_supply(fit)

Arguments

fit

A fitted market model object.

model

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 using the estimated coefficients of a fitted model. Alternatively, the function calculates aggregates using a model and a set of parameters passed separately. If the model's data have multiple distinct subjects at each date, aggregation is calculated over subjects per unique date. If the model has time series data, namely a single subject per time point, aggregation is ululated over all time pints.

See Also

demanded_quantities, supplied_quantities

Examples

# NOT RUN {
fit <- diseq_basic(
  HS | RM | ID | TREND ~
  RM + TREND + W + CSHS + L1RM + L2RM + MONTH |
    RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH,
  fair_houses(),
  correlated_shocks = FALSE
)

# get estimated aggregate demand
aggregate_demand(fit)

# 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
fit <- estimate(model)

# get estimated aggregate demand
aggregate_demand(fit)

# get estimated aggregate demand
aggregate_supply(fit)
# }