Analysis of shortages

shortages(fit, model, parameters)

normalized_shortages(fit, model, parameters)

relative_shortages(fit, model, parameters)

shortage_probabilities(fit, model, parameters)

shortage_indicators(fit, model, parameters)

shortage_standard_deviation(fit, model, parameters)

# S4 method for missing,disequilibrium_model,ANY
shortages(model, parameters)

# S4 method for missing,disequilibrium_model,ANY
normalized_shortages(model, parameters)

# S4 method for missing,disequilibrium_model,ANY
relative_shortages(model, parameters)

# S4 method for missing,disequilibrium_model,ANY
shortage_probabilities(model, parameters)

# S4 method for missing,disequilibrium_model,ANY
shortage_indicators(model, parameters)

# S4 method for missing,disequilibrium_model,ANY
shortage_standard_deviation(model, parameters)

# S4 method for missing,diseq_stochastic_adjustment,ANY
shortage_standard_deviation(model, parameters)

# S4 method for market_fit,missing,missing
shortages(fit)

# S4 method for market_fit,missing,missing
normalized_shortages(fit)

# S4 method for market_fit,missing,missing
relative_shortages(fit)

# S4 method for market_fit,missing,missing
shortage_probabilities(fit)

# S4 method for market_fit,missing,missing
shortage_indicators(fit)

# S4 method for market_fit,missing,missing
shortage_standard_deviation(fit)

Arguments

fit

A fitted model object.

model

A disequilibrium model object.

parameters

A vector of parameters at which the shortages are evaluated.

Value

A vector with the (estimated) shortages.

Details

The following methods offer functionality for analyzing estimated shortages in the disequilibrium models. The methods can be called either using directly a fitted model object, or by separately providing a model object and a parameter vector.

shortages

Returns the predicted shortages at a given point.

normalized_shortages

Returns the shortages normalized by the variance of the difference of the shocks at a given point.

relative_shortages

Returns the shortages normalized by the supplied quantity at a given point.

shortage_probabilities

Returns the shortage probabilities, i.e. the probabilities of an observation coming from an excess demand state, at the given point.

shortage_indicators

Returns a vector of indicators (Boolean values) for each observation. An element of the vector is TRUE for observations at which the estimated shortages are non-negative, i.e. the market at in an excess demand state. The remaining elements are FALSE. The evaluation of the shortages is performed using the passed parameter vector.

shortage_standard_deviation

Returns the variance of excess demand.

Functions

  • shortages: Shortages.

  • normalized_shortages: Normalized shortages.

  • relative_shortages: Relative shortages.

  • shortage_probabilities: Shortage probabilities.

  • shortage_indicators: Shortage indicators.

  • shortage_standard_deviation: Shortage variance.

Examples

# \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.

# get estimated normalized shortages
head(normalized_shortages(fit))
#>   normalized_shortages
#> 1           -0.2725824
#> 2            0.2392505
#> 3            0.1104494
#> 4           -1.7675573
#> 5            0.7434607
#> 6           -0.2920426

# get estimated relative shortages
head(relative_shortages(fit))
#>   relative_shortages
#> 1        -0.05966599
#> 2         0.05272308
#> 3         0.02515503
#> 4        -0.40363575
#> 5         0.18520902
#> 6        -0.07403025

# get the estimated shortage probabilities
head(shortage_probabilities(fit))
#>   shortage_probabilities
#> 1             0.39258710
#> 2             0.59454434
#> 3             0.54397352
#> 4             0.03856747
#> 5             0.77139859
#> 6             0.38512702

# get the estimated shortage indicators
head(shortage_indicators(fit))
#>   shortage_indicators
#> 1               FALSE
#> 2                TRUE
#> 3                TRUE
#> 4               FALSE
#> 5                TRUE
#> 6               FALSE

# get the estimated shortages
head(shortages(fit))
#>    shortages
#> 1  -8.275923
#> 2   7.263927
#> 3   3.353374
#> 4 -53.665114
#> 5  22.572339
#> 6  -8.866757

# get the estimated shortage variance
shortage_standard_deviation(fit)
#> shortage_standard_deviation 
#>                    30.36117 
# }