Computes and plots the own-price point elasticity of demand across prices. Elasticity is computed numerically via central differences on the unconditional demand curve.
Usage
plot_elasticity(object, ...)
# S3 method for class 'beezdemand_hurdle'
plot_elasticity(
object,
prices = NULL,
n_points = 200,
show_unit = TRUE,
x_trans = c("log10", "log", "linear", "pseudo_log"),
free_trans = 0.01,
x_lab = "Price",
y_lab = "Elasticity",
style = c("modern", "apa"),
...
)
# S3 method for class 'beezdemand_tmb'
plot_elasticity(
object,
prices = NULL,
n_points = 200,
show_unit = TRUE,
x_trans = c("log10", "log", "linear", "pseudo_log"),
free_trans = 0.01,
x_lab = "Price",
y_lab = "Elasticity",
style = c("modern", "apa"),
...
)Arguments
- object
A fitted model object.
- ...
Additional arguments passed to methods.
- prices
Numeric vector of prices. If
NULL, uses a smooth grid spanning the observed price range.- n_points
Integer; number of grid points (default 200).
- show_unit
Logical; show unit elasticity reference line at -1 (default
TRUE).- x_trans
Character; x-axis transformation (default
"log10").- free_trans
Numeric; replacement for price = 0 on log scales.
- x_lab, y_lab
Axis labels.
- style
Character;
"modern"or"apa".
Details
Point elasticity is computed as: $$\eta(P) = \frac{dQ}{dP} \cdot \frac{P}{Q(P)}$$
This uses the unconditional demand Q(P) for hurdle models (which includes the probability of zero consumption), providing the economically relevant total elasticity.
Examples
# \donttest{
data(apt)
fit <- fit_demand_hurdle(apt, y_var = "y", x_var = "x", id_var = "id")
#> Sample size may be too small for reliable estimation.
#> Subjects: 10, Parameters: 12, Recommended minimum: 60 subjects.
#> Consider using more subjects or the simpler 2-RE model.
#> Fitting HurdleDemand3RE model...
#> Part II: zhao_exponential
#> Subjects: 10, Observations: 160
#> Fixed parameters: 12, Random effects per subject: 3
#> Optimizing...
#> Converged in 81 iterations
#> Computing standard errors...
#> Done. Log-likelihood: 32.81
plot_elasticity(fit)
# }