shortage_analysis: Analysis of shortages

In diseq: Estimation Methods for Markets in Equilibrium and Disequilibrium

Analysis of shortages

Description

Analysis of shortages

Usage

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 signature 'missing,market_model,ANY'
shortages(model, parameters)

## S4 method for signature 'missing,market_model,ANY'
normalized_shortages(model, parameters)

## S4 method for signature 'missing,market_model,ANY'
relative_shortages(model, parameters)

## S4 method for signature 'missing,market_model,ANY'
shortage_probabilities(model, parameters)

## S4 method for signature 'missing,market_model,ANY'
shortage_indicators(model, parameters)

## S4 method for signature 'missing,market_model,ANY'
shortage_standard_deviation(model, parameters)

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

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

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

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

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

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

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

Arguments

fit	A fitted model object.
model	A market model object.
parameters	A vector of parameters at which the shortages are evaluated.

Details

The following methods offer functionality for analyzing estimated shortages of the market 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.

Value

A vector with the (estimated) shortages.

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

# 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)))

# get estimated normalized shortages
head(normalized_shortages(fit))

# get estimated relative shortages
head(relative_shortages(fit))

# get the estimated shortage probabilities
head(shortage_probabilities(fit))

# get the estimated shortage indicators
head(shortage_indicators(fit))

# get the estimated shortages
head(shortages(fit))

# get the estimated shortage variance
shortage_standard_deviation(fit)

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