SyntheticCentralBank¶

The SyntheticCentralBank module provides data structures and utilities for preprocessing and organizing central bank data that will be used to initialize behavioral models in the simulation package.

SyntheticCentralBank¶

The SyntheticCentralBank class is an abstract base class that provides a framework for collecting and organizing central bank data. It is not used for simulating central bank behavior - it only handles data preprocessing.

Key Features¶

Historical policy rate collection
Inflation and growth data aggregation
Parameter estimation from historical data
Data validation and organization
Country-specific data handling

Attributes¶

country_name (str): Country identifier for data collection
year (int): Reference year for data preprocessing
central_bank_data (pd.DataFrame): Preprocessed central bank data containing:
Policy rates
Additional metrics (implementation-specific)

DefaultSyntheticCentralBank¶

The DefaultSyntheticCentralBank class is a concrete implementation that preprocesses and organizes central bank data by estimating Taylor rule parameters from historical data.

Key Features¶

Taylor rule parameter estimation
Interest rate smoothing calculation
Response coefficients estimation
Natural rate computation
Zero lower bound enforcement

Attributes¶

The preprocessed data DataFrame contains:

policy_rate (float): Historical/initial policy rate
targeted_inflation_rate (float): Reference inflation target
rho (float): Estimated interest rate smoothing parameter
r_star (float): Estimated natural real interest rate
xi_pi (float): Estimated inflation response coefficient
xi_gamma (float): Estimated growth response coefficient

Factory Methods¶

from_readers¶

@classmethod
def from_readers(
    cls,
    country_name: str,
    year: int,
    quarter: int,
    readers: DataReaders,
    exogenous_data: ExogenousCountryData,
    central_bank_configuration: CentralBankDataConfiguration
) -> DefaultSyntheticCentralBank

Creates a preprocessed central bank data container using historical data.

Parameters:

country_name (str): Country to preprocess data for
year (int): Reference year for preprocessing
quarter (int): Reference quarter (1-4)
readers (DataReaders): Data source readers
exogenous_data (ExogenousCountryData): External economic data
central_bank_configuration (CentralBankDataConfiguration): Configuration settings

Returns:

DefaultSyntheticCentralBank: Container with preprocessed parameters

Parameter Estimation¶

The class estimates Taylor rule parameters using the form:

r_t = ρr_{t-1} + (1-ρ)[r* + π* + ξ_π(π_t - π*) + ξ_γγ_t]

where:

r_t: historical policy rate
ρ: smoothing parameter
r*: natural rate
π*: inflation target
π_t: historical inflation
γ_t: historical growth

Usage Example¶

from macro_data import DataReaders, ExogenousCountryData
from macro_data.configuration.dataconfiguration import CentralBankDataConfiguration
from macro_data.processing.synthetic_central_bank import DefaultSyntheticCentralBank

# Initialize data readers and configuration
readers = DataReaders.from_raw_data(...)
exogenous_data = ExogenousCountryData(...)
config = CentralBankDataConfiguration(inflation_target=0.02)

# Create central bank data for France in 2023 Q1
france_central_bank = DefaultSyntheticCentralBank.from_readers(
    country_name="FRA",
    year=2023,
    quarter=1,
    readers=readers,
    exogenous_data=exogenous_data,
    central_bank_configuration=config
)

# Access estimated parameters
policy_rate = france_central_bank.central_bank_data["policy_rate"]
inflation_response = france_central_bank.central_bank_data["xi_pi"]

Module for preprocessing synthetic central bank data.

This module provides an abstract base class for preprocessing and storing synthetic central bank data that will be used to initialize behavioral models in the simulation package. Key preprocessing includes:

Data Collection:
Historical policy rates
Inflation data
Growth indicators
Data Organization:
Policy rate preprocessing
Parameter estimation
Data validation
Country-Specific Data:
Currency area identification
Policy rate histories
Economic indicators

Note

This module is NOT used for simulating central bank behavior. It only handles the preprocessing and organization of data that will later be used to initialize behavioral models in the simulation package.

`SyntheticCentralBank` ¶

Abstract base class for preprocessing and storing central bank data.

This class provides a framework for collecting and organizing central bank data that will be used to initialize behavioral models. It is NOT used for simulating central bank behavior - it only handles data preprocessing.

The central bank data is stored in a pandas DataFrame containing

Policy Rate: Historical/initial policy rates
Additional metrics may be added by concrete implementations

Note

This is a data container class. The actual central bank behavior (policy decisions, rate setting, etc.) is implemented in the simulation package, which uses this preprocessed data for initialization.

Attributes:

Name	Type	Description
`country_name`	`str`	Country identifier for data collection
`year`	`int`	Reference year for data preprocessing
`central_bank_data`	`DataFrame`	Preprocessed central bank data

`country_name = country_name` `instance-attribute` ¶

`year = year` `instance-attribute` ¶

`central_bank_data = central_bank_data` `instance-attribute` ¶

`init(country_name: str, year: int, central_bank_data: pd.DataFrame)` `abstractmethod` ¶

Initialize the central bank data container.

Parameters:

Name	Type	Description	Default
`country_name`	`str`	Country identifier for data collection	required
`year`	`int`	Reference year for data preprocessing	required
`central_bank_data`	`DataFrame`	Initial central bank data to preprocess	required

SyntheticCentralBank¶