diseq (version 0.3.1)

initialize_market_model: Model initialization

Description

Model initialization

Usage

# S4 method for diseq_basic
initialize(
  .Object,
  key_columns,
  quantity_column,
  price_column,
  demand_specification,
  supply_specification,
  data,
  correlated_shocks = TRUE,
  verbose = 0
)
# S4 method for diseq_deterministic_adjustment
initialize(
  .Object,
  key_columns,
  time_column,
  quantity_column,
  price_column,
  demand_specification,
  supply_specification,
  data,
  correlated_shocks = TRUE,
  verbose = 0
)
# S4 method for diseq_directional
initialize(
  .Object,
  key_columns,
  time_column,
  quantity_column,
  price_column,
  demand_specification,
  supply_specification,
  data,
  correlated_shocks = TRUE,
  verbose = 0
)
# S4 method for diseq_stochastic_adjustment
initialize(
  .Object,
  key_columns,
  time_column,
  quantity_column,
  price_column,
  demand_specification,
  supply_specification,
  price_specification,
  data,
  correlated_shocks = TRUE,
  verbose = 0
)
# S4 method for equilibrium_model
initialize(
  .Object,
  key_columns,
  quantity_column,
  price_column,
  demand_specification,
  supply_specification,
  data,
  correlated_shocks = TRUE,
  verbose = 0
)

Arguments

.Object

The object to be Constructed.

key_columns

Key columns of the data set.

quantity_column

The quantity variable of the data set.

price_column

The price variable of the data set.

demand_specification

A formula representation of the right hand side of the demand equation.

supply_specification

A formula representation of the right hand side of the supply equation.

data

The data set.

correlated_shocks

Should the model be estimated using correlated shocks?

verbose

Verbosity level.

time_column

The time column of the data set.

price_specification

A formula representation of the price equation.

Value

The initialized model.

Functions

• initialize,diseq_basic-method: Basic disequilibrium model base constructor

• initialize,diseq_deterministic_adjustment-method: Disequilibrium model with deterministic price adjustment constructor

• initialize,diseq_directional-method: Directional disequilibrium model base constructor

• initialize,diseq_stochastic_adjustment-method: Disequilibrium model with stochastic price adjustment constructor

• initialize,equilibrium_model-method: Equilibrium model constructor

Details

The following two subsections describe the common initialization steps of all market model classes.

Variable construction

The constructor prepares the model's variables using the passed specifications. The specification strings are expected to follow the syntax of formula. The construction of the model's data uses the variables that are extracted by these specification. The demand variables are extracted by a formula that uses the quantity_column on the left hand side and the demand_specification on the right hand side of the formula. The supply variables are constructed by the thequantity_column and the supply_specification. In the case of the '>diseq_stochastic_adjustment model, the price dynamics' variables are extracted using the price_specification. The price_specification for the '>diseq_stochastic_adjustment should contain only terms other than that of excess demand. The excess demand term of the price equation is automatically added by the constructor.

Data preparation

1. If the passed data set contains rows with NA values, they are dropped. If the verbosity level allows warnings, a warning is emitted reporting how many rows were dropped.

2. After dropping the rows, factor levels may be invalidated. If needed, the constructor readjusts the factor variables by removing the unobserved levels. Factor indicators and interaction terms are automatically created.

3. The primary key column is constructed by pasting the values of the key_columns.

4. In the cases of the '>diseq_directional, '>diseq_deterministic_adjustment, and the '>diseq_stochastic_adjustment models, a column with lagged prices is constructed. Since lagged prices are unavailable for the observations of the first time point, these observations are dropped. If the verbosity level allows the emission of information messages, the constructor prints the number of dropped observations.

5. In the cases of the '>diseq_directional and the '>diseq_stochastic_adjustment models, a column with price differences is created.

Examples

# NOT RUN {
simulated_data <- simulate_data(
  "diseq_basic", 500, 3, # model type, observed entities, observed time points
  -0.9, 8.9, c(0.3, -0.2), c(-0.03, -0.01), # demand coefficients
  0.9, 4.2, c(0.03), c(-0.05, 0.02) # supply coefficients
)

# initialize the model
model <- new(
  "diseq_basic", # model type
  c("id", "date"), "Q", "P", # keys, quantity, and price variables
  "P + Xd1 + Xd2 + X1 + X2", "P + Xs1 + X1 + X2", # equation specifications
  simulated_data, # data
  correlated_shocks = FALSE # use independent shocks
)

show(model)
simulated_data <- simulate_data(
  # model type, observed entities and time points
  "diseq_deterministic_adjustment", 500, 3,
  # demand coefficients
  -0.9, 8.9, c(0.03, -0.02), c(-0.03, -0.01),
  # supply coefficients
  0.9, 4.2, c(0.03), c(0.05, 0.02),
  # price adjustment coefficient
  1.4
)

# initialize the model
model <- new(
  "diseq_deterministic_adjustment", # model type
  c("id", "date"), "date", "Q", "P", # keys, quantity, and price variables
  "P + Xd1 + Xd2 + X1 + X2", "P + Xs1 + X1 + X2", # equation specifications
  simulated_data, # data
  correlated_shocks = TRUE # allow shocks to be correlated
)

show(model)
# }
# NOT RUN {
simulated_data <- simulate_data(
  "diseq_directional", 500, 3, # model type, observed entities, observed time points
  -0.2, 4.3, c(0.03, 0.02), c(0.03, 0.01), # demand coefficients
  0.0, 4.0, c(0.03), c(0.05, 0.02) # supply coefficients
)

# in the directional model prices cannot be included in both demand and supply
model <- new(
  "diseq_directional", # model type
  c("id", "date"), "date", "Q", "P", # keys, time point, quantity, and price variables
  "P + Xd1 + Xd2 + X1 + X2", "Xs1 + X1 + X2", # equation specifications
  simulated_data, # data
  correlated_shocks = TRUE # allow shocks to be correlated
)

show(model)
# }
# NOT RUN {
simulated_data <- simulate_data(
  # model type, observed entities and time points
  "diseq_stochastic_adjustment", 500, 3,
  # demand coefficients
  -0.1, 9.8, c(0.3, -0.2), c(0.6, 0.1),
  # supply coefficients
  0.1, 5.1, c(0.9), c(-0.5, 0.2),
  # price adjustment coefficient
  1.4, 3.1, c(0.8)
)

# initialize the model
model <- new(
  "diseq_stochastic_adjustment", # model type
  c("id", "date"), "date", "Q", "P", # keys, quantity, and price variables
  "P + Xd1 + Xd2 + X1 + X2", "P + Xs1 + X1 + X2", "Xp1", # equation specifications
  simulated_data, # data
  correlated_shocks = TRUE # allow shocks to be correlated
)

show(model)
simulated_data <- simulate_data(
  "equilibrium_model", 500, 3, # model type, observed entities and time points
  -0.9, 14.9, c(0.3, -0.2), c(-0.03, -0.01), # demand coefficients
  0.9, 3.2, c(0.3), c(0.5, 0.02) # supply coefficients
)

# initialize the model
model <- new(
  "equilibrium_model", # model type
  c("id", "date"), "Q", "P", # keys, quantity, and price variables
  "P + Xd1 + Xd2 + X1 + X2", "P + Xs1 + X1 + X2", # equation specifications
  simulated_data, # data
  correlated_shocks = TRUE # allow shocks to be correlated
)

show(model)
# }