Returns the estimated effect of a variable.

```
shortage_marginal(fit, variable, model, parameters)
shortage_probability_marginal(
fit,
variable,
aggregate = "mean",
model,
parameters
)
# S4 method for missing,ANY,disequilibrium_model,ANY
shortage_marginal(variable, model, parameters)
# S4 method for missing,ANY,ANY,disequilibrium_model,ANY
shortage_probability_marginal(variable, aggregate, model, parameters)
# S4 method for missing,ANY,disequilibrium_model,ANY
shortage_marginal(variable, model, parameters)
# S4 method for market_fit,ANY,missing,missing
shortage_marginal(fit, variable)
# S4 method for market_fit,ANY,ANY,missing,missing
shortage_probability_marginal(fit, variable, aggregate)
```

- fit
A fitted disequilibrium market model.

- variable
Variable name for which the effect is calculated.

- model
A disequilibrium model object.

- parameters
A vector of parameters.

- aggregate
Mode of aggregation. Valid options are "mean" (the default) and "at_the_mean".

The estimated effect of the passed variable.

`shortage_marginal`

: Marginal effect on market systemReturns the estimated marginal effect of a variable on the market system. For a system variable \(x\) with demand coefficient \(\beta_{d, x}\) and supply coefficient \(\beta_{s, x}\), the marginal effect on the market system is given by $$M_{x} = \frac{\beta_{d, x} - \beta_{s, x}}{\sqrt{\sigma_{d, x}^{2} + \sigma_{s, x}^{2} - 2 \rho_{ds} \sigma_{d, x} \sigma_{s, x}}}.$$

`shortage_probability_marginal`

: Marginal effect on shortage probabilitiesReturns the estimated marginal effect of a variable on the probability of observing a shortage state. The mean marginal effect on the shortage probability is given by $$M_{x} \mathrm{E}\phi(D - S)$$ and the marginal effect at the mean by $$M_{x} \phi(\mathrm{E}(D - S)),$$ where \(M_{x}\) is the marginal effect on the system, \(D\) is the demanded quantity, \(S\) the supplied quantity, and \(\phi\) is the standard normal density.

```
# \donttest{
# estimate a model using the houses dataset
fit <- diseq_deterministic_adjustment(
HS | RM | ID | TREND ~
RM + TREND + W + CSHS + L1RM + L2RM + MONTH |
RM + TREND + W + L1RM + MA6DSF + MA3DHF + MONTH,
fair_houses(), correlated_shocks = FALSE,
estimation_options = list(control = list(maxit = 1e+5)))
#> 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.
# mean marginal effect of variable "RM" on the shortage probabilities
#' shortage_probability_marginal(fit, "RM")
# marginal effect at the mean of variable "RM" on the shortage probabilities
shortage_probability_marginal(fit, "CSHS", aggregate = "at_the_mean")
#> D_CSHS
#> 0.000320671
# marginal effect of variable "RM" on the system
shortage_marginal(fit, "RM")
#> B_RM
#> -0.1702642
# }
```