Market side aggregation.

```
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)
```

- fit
A fitted market model object.

- model
A model object.

- parameters
A vector of model's parameters.

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

`aggregate_demand`

: Demand aggregation.`aggregate_supply`

: Supply aggregation.

demanded_quantities, supplied_quantities

```
# \donttest{
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
)
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
#> Warning: Using formula(x) is deprecated when x is a character vector of length > 1.
#> Consider formula(paste(x, collapse = " ")) instead.
# get estimated aggregate demand
aggregate_demand(fit)
#> # A tibble: 130 × 2
#> TREND D_HS
#> <int> <dbl>
#> 1 15 200.
#> 2 16 237.
#> 3 17 246.
#> 4 18 250.
#> 5 19 253.
#> 6 20 258.
#> 7 21 256.
#> 8 22 272.
#> 9 23 260.
#> 10 24 244.
#> # … with 120 more rows
# 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)
#> # A tibble: 2 × 2
#> date D_Q
#> <fct> <dbl>
#> 1 2 3870.
#> 2 3 3679.
# get estimated aggregate demand
aggregate_supply(fit)
#> # A tibble: 2 × 2
#> date S_Q
#> <fct> <dbl>
#> 1 2 3368.
#> 2 3 3423.
# }
```