Likelihood scores.

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.

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

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

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

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

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

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

# S4 method for 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

# \donttest{
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)))
#>            D_P     D_CONST        D_Xd1          D_X1        S_P     S_CONST
#> 1 -0.122260422  0.34811752 -0.417340236  0.3605332179 -0.5466158  1.55640326
#> 2  0.743843382  1.23910947 -1.148249906  1.4036189852  0.1513030  0.25204357
#> 3  0.037446089  0.04769418  0.047007752  0.0006108855  0.0625768  0.07970256
#> 4 -0.002693132  0.01690514 -0.008471139 -0.0192476350  0.1587665 -0.99659773
#> 5 -0.088372742  0.14821086 -0.129232886  0.0357887188 -0.1360946  0.22824566
#> 6 -0.201806861 -0.29613341  0.271890840 -0.1908639813 -0.3212258 -0.47137000
#>         S_Xs1       S_Xs2         S_X1  D_VARIANCE  S_VARIANCE        RHO
#> 1 -1.35247639 -0.20897526  1.611912774 -0.39852402 -0.97800236 0.03240626
#> 2  0.05638665  0.04213804  0.285505964 -0.42071759 -0.25214455 0.05403332
#> 3 -0.07472583 -0.12786917  0.001020861 -0.04798689  0.47379627 0.04437969
#> 4 -1.33182296  1.31959174  1.134693567  0.02317068 -0.02313445 0.08570107
#> 5 -0.15303714 -0.03443069  0.055114852 -0.08776477  0.40012622 0.13650416
#> 6 -0.58471108  0.50310081 -0.303807511  0.25698735  0.24866134 0.05187506
# }