scores: Likelihood scores.

In diseq: Estimation Methods for Markets in Equilibrium and Disequilibrium

Likelihood scores.

Description

It calculates the gradient of the likelihood at the given parameter point for each observation in the sample. It, therefore, returns an n x k matrix, where n denotes the number of observations in the sample and k the number of estimated parameters. The ordering of the parameters is the same as the one that is used in the summary of the results. The method can be called either using directly a fitted model object, or by separately providing a model object and a parameter vector.

Usage

scores(object, parameters, fit = missing())

## S4 method for signature 'diseq_basic,ANY,ANY'
scores(object, parameters)

## S4 method for signature 'diseq_deterministic_adjustment,ANY,ANY'
scores(object, parameters)

## S4 method for signature 'diseq_directional,ANY,ANY'
scores(object, parameters)

## S4 method for signature 'diseq_stochastic_adjustment,ANY,ANY'
scores(object, parameters)

## S4 method for signature 'equilibrium_model,ANY,ANY'
scores(object, parameters)

## S4 method for signature 'missing,missing,market_fit'
scores(fit)

Arguments

object	A model object.
parameters	A vector with model parameters.
fit	A fitted model object.

Value

The score matrix.

Examples

model <- simulate_model(
  "diseq_basic", list(
    # observed entities, observed time points
    nobs = 500, tobs = 3,
    # demand coefficients
    alpha_d = -0.9, beta_d0 = 8.9, beta_d = c(0.6), eta_d = c(-0.2),
    # supply coefficients
    alpha_s = 0.9, beta_s0 = 7.9, beta_s = c(0.03, 1.2), eta_s = c(0.1)
  ),
  seed = 7523
)

# estimate the model object (BFGS is used by default)
fit <- estimate(model)

# Calculate the score matrix
head(scores(model, coef(fit)))

diseq documentation built on June 2, 2022, 1:10 a.m.