SyntheticBanks¶

The SyntheticBanks class is an abstract base class that defines the interface for preprocessing and organizing banking system data. It provides the core functionality for managing bank-level data and relationships.

Core Functionality¶

The class handles:

Bank balance sheet data organization
Customer relationship mapping
Initial state calculations
Interest rate management
Market share calculations

Key Methods¶

initialise_deposits_and_loans: Sets up initial deposits and loans for all banks
set_bank_equity: Sets equity level for each bank
set_deposits_from_firms: Sets initial deposits from firms
set_deposits_from_households: Sets initial deposits from households
set_loans_to_firms: Sets initial loans to firms
set_loans_to_households: Sets initial loans to households
set_bank_deposits: Sets total deposits for each bank
set_market_share: Calculates and sets market share for each bank
set_liability: Calculates and sets total liabilities for each bank

Implementation¶

Module for preprocessing synthetic banking system data.

This module provides an abstract base class for preprocessing and organizing banking system data that will be used to initialize behavioral models in the simulation package. Key preprocessing includes:

Data Collection and Organization:
Bank balance sheet data preparation
Initial deposit and loan allocation
Interest rate parameter estimation
Relationship Data:
Bank-firm connections
Bank-household connections
Market share calculations
Financial Data Processing:
Interest rate parameter estimation
Balance sheet validation
Data consistency checks

Note

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

`SyntheticBanks` ¶

Abstract base class for preprocessing and organizing banking system data.

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

The preprocessed data is stored in a pandas DataFrame with the following columns

Equity: Initial bank equity
Corresponding Firms ID: Firm-bank relationship mapping
Corresponding Households ID: Household-bank relationship mapping
Deposits from Households: Initial household deposits
Mortgages to Households: Initial mortgage values
Consumption Loans to Households: Initial consumer loan values
Deposits from Firms: Initial firm deposits
Loans to Firms: Initial firm loans
Deposits: Total initial deposits
Short-Term Interest Rates on Firm Loans: Initial rate parameters
Long-Term Interest Rates on Firm Loans: Initial rate parameters
Interest Rates on Household Payday Loans: Initial rate parameters
Interest Rates on Household Consumption Loans: Initial rate parameters
Interest Rates on Mortgages: Initial rate parameters
Interest Rates on Firm Deposits: Initial rate parameters
Overdraft Rate on Firm Deposits: Initial rate parameters
Interest Rates on Household Deposits: Initial rate parameters
Overdraft Rate on Household Deposits: Initial rate parameters
Interest received from Loans: Initial interest calculations
Interest received from Deposits: Initial interest calculations
Profits: Initial profit calculations
Corporate Taxes Paid: Initial tax calculations
Liability: Initial liability calculations
Market Share: Initial market share calculations

Note

This is a data container class. The actual banking behavior (lending 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 preprocessing
`quarter`	`int`	Reference quarter for preprocessing
`number_of_banks`	`int`	Number of banks to preprocess data for
`bank_data`	`DataFrame`	Preprocessed bank-level data
`firm_passthrough`	`float`	Estimated rate adjustment parameter
`firm_ect`	`float`	Estimated error correction parameter
`firm_rate`	`float`	Initial firm loan rate
`hh_consumption_passthrough`	`float`	Estimated consumer rate parameter
`hh_consumption_ect`	`float`	Estimated consumer ECT parameter
`hh_consumption_rate`	`float`	Initial consumer loan rate
`hh_mortgage_passthrough`	`float`	Estimated mortgage rate parameter
`hh_mortgage_ect`	`float`	Estimated mortgage ECT parameter
`hh_mortgage_rate`	`float`	Initial mortgage rate

`country_name = country_name` `instance-attribute` ¶

`year = year` `instance-attribute` ¶

`quarter = quarter` `instance-attribute` ¶

`number_of_banks = number_of_banks` `instance-attribute` ¶

`bank_data = bank_data` `instance-attribute` ¶

`firm_passthrough = firm_passthrough` `instance-attribute` ¶

`firm_ect = firm_ect` `instance-attribute` ¶

`firm_rate = firm_rate` `instance-attribute` ¶

`hh_consumption_passthrough = hh_consumption_passthrough` `instance-attribute` ¶

`hh_consumption_ect = hh_consumption_ect` `instance-attribute` ¶

`hh_consumption_rate = hh_consumption_rate` `instance-attribute` ¶

`hh_mortgage_passthrough = hh_mortgage_passthrough` `instance-attribute` ¶

`hh_mortgage_ect = hh_mortgage_ect` `instance-attribute` ¶

`hh_mortgage_rate = hh_mortgage_rate` `instance-attribute` ¶

`init(country_name: str, year: int, number_of_banks: int, bank_data: pd.DataFrame, quarter: int, firm_passthrough: float, firm_ect: float, firm_rate: float, hh_consumption_passthrough: float, hh_consumption_ect: float, hh_consumption_rate: float, hh_mortgage_passthrough: float, hh_mortgage_ect: float, hh_mortgage_rate: float) -> None` `abstractmethod` ¶

Initialize a synthetic banking system.

Parameters:

Name	Type	Description	Default
`country_name`	`str`	Country identifier	required
`year`	`int`	Reference year for data	required
`number_of_banks`	`int`	Number of banks to create	required
`bank_data`	`DataFrame`	Initial bank-level data	required
`quarter`	`int`	Reference quarter (1-4)	required
`firm_passthrough`	`float`	Rate adjustment factor for firm loans	required
`firm_ect`	`float`	Error correction term for firm rates	required
`firm_rate`	`float`	Base rate for firm loans	required
`hh_consumption_passthrough`	`float`	Rate adjustment for consumer loans	required
`hh_consumption_ect`	`float`	Error correction for consumer rates	required
`hh_consumption_rate`	`float`	Base rate for consumer loans	required
`hh_mortgage_passthrough`	`float`	Rate adjustment for mortgages	required
`hh_mortgage_ect`	`float`	Error correction for mortgage rates	required
`hh_mortgage_rate`	`float`	Base mortgage rate	required

`initialise_deposits_and_loans(synthetic_population: SyntheticPopulation, firm_deposits: np.ndarray, firm_debt: np.ndarray) -> None` ¶

Initialize the deposits and loans for all banks in the system.

This method sets up the initial state of bank balance sheets by: 1. Setting household deposits based on their wealth allocation 2. Setting household loans (mortgages and other debt) 3. Setting firm deposits and loans 4. Calculating total bank deposits

Parameters:

Name	Type	Description	Default
`synthetic_population`	`SyntheticPopulation`	Population data including household wealth	required
`firm_deposits`	`ndarray`	Array of firm deposit amounts	required
`firm_debt`	`ndarray`	Array of firm debt amounts	required

`set_deposits_from_firms(firm_deposits: np.ndarray) -> None` `abstractmethod` ¶

Set the initial deposits from firms for each bank.

Parameters:

Name	Type	Description	Default
`firm_deposits`	`ndarray`	Array of deposit amounts by firm	required

`set_deposits_from_households(household_deposits: np.ndarray) -> None` `abstractmethod` ¶

Set the initial deposits from households for each bank.

Parameters:

Name	Type	Description	Default
`household_deposits`	`ndarray`	Array of deposit amounts by household	required

`set_loans_to_firms(firm_debt: np.ndarray) -> None` `abstractmethod` ¶

Set the initial loans to firms for each bank.

Parameters:

Name	Type	Description	Default
`firm_debt`	`ndarray`	Array of debt amounts by firm	required

`set_loans_to_households(household_mortgage_debt: np.ndarray, household_other_debt: np.ndarray) -> None` `abstractmethod` ¶

Set the initial loans to households for each bank.

Parameters:

Name	Type	Description	Default
`household_mortgage_debt`	`ndarray`	Array of mortgage debt by household	required
`household_other_debt`	`ndarray`	Array of non-mortgage debt by household	required

`set_bank_equity(bank_equity: float) -> None` `abstractmethod` ¶

Set the equity level for each bank.

Parameters:

Name	Type	Description	Default
`bank_equity`	`float`	Equity amount to set for each bank	required

`set_bank_deposits(firm_deposits: np.ndarray, household_deposits: np.ndarray, firm_debt: np.ndarray, household_debt: np.ndarray) -> None` `abstractmethod` ¶

Set the total deposits for each bank.

Calculates and sets total deposits by combining: - Firm deposits - Household deposits - Firm debt - Household debt

Parameters:

Name	Type	Description	Default
`firm_deposits`	`ndarray`	Array of firm deposit amounts	required
`household_deposits`	`ndarray`	Array of household deposit amounts	required
`firm_debt`	`ndarray`	Array of firm debt amounts	required
`household_debt`	`ndarray`	Array of household debt amounts	required

`initialise_rates_profits_liabilities(policy_rate: float, tau_bank: float, risk_premium: float, consumption_loans_markup: float, mortgage_markup: float, household_overdraft_markup: float)` ¶

Initialize bank rates, profits, and liabilities.

This method sets up the complete financial structure of banks by: 1. Setting interest rates for all products 2. Calculating interest income from loans and deposits 3. Computing profits and taxes 4. Setting liabilities and market shares

Parameters:

Name	Type	Description	Default
`policy_rate`	`float`	Central bank policy rate	required
`tau_bank`	`float`	Bank tax rate	required
`risk_premium`	`float`	Risk premium for loans	required
`consumption_loans_markup`	`float`	Markup for consumer loans	required
`mortgage_markup`	`float`	Markup for mortgages	required
`household_overdraft_markup`	`float`	Markup for household overdrafts	required

`set_initial_interest_rates(central_bank_policy_rate: float, bank_markup_interest_rate_short_term_firm_loans: float, bank_markup_interest_rate_long_term_firm_loans: float, bank_markup_interest_rate_household_payday_loans: float, bank_markup_interest_rate_household_consumption_loans: float, bank_markup_interest_rate_mortgages: float, bank_markup_interest_rate_overdraft_firm: float, bank_markup_interest_rate_overdraft_household: float) -> None` ¶

Set initial interest rates for all bank products.

This method initializes rates for: - Firm loans (short and long term) - Household loans (payday, consumption, mortgages) - Deposits (firm and household) - Overdrafts

Parameters:

Name	Type	Description	Default
`central_bank_policy_rate`	`float`	Base rate from central bank	required
`bank_markup_interest_rate_short_term_firm_loans`	`float`	Markup for short-term firm loans	required
`bank_markup_interest_rate_long_term_firm_loans`	`float`	Markup for long-term firm loans	required
`bank_markup_interest_rate_household_payday_loans`	`float`	Markup for payday loans	required
`bank_markup_interest_rate_household_consumption_loans`	`float`	Markup for consumer loans	required
`bank_markup_interest_rate_mortgages`	`float`	Markup for mortgages	required
`bank_markup_interest_rate_overdraft_firm`	`float`	Markup for firm overdrafts	required
`bank_markup_interest_rate_overdraft_household`	`float`	Markup for household overdrafts	required

`set_interest_received_from_loans() -> None` ¶

Calculate and set the interest income received from all loans.

This method computes the total interest income from: - Firm loans (short and long term) - Household loans (mortgages, consumption, payday) - Overdraft facilities

The calculated interest income is stored in the bank_data DataFrame.

`set_interest_received_from_deposits(central_bank_policy_rate: float) -> None` ¶

Calculate and set the interest income received from deposits.

This method computes interest income from: - Household deposits - Firm deposits - Interbank deposits

Parameters:

Name	Type	Description	Default
`central_bank_policy_rate`	`float`	Base rate from central bank used for deposit rate calculations	required

`set_profits() -> None` ¶

Calculate and set bank profits.

Computes profits by: 1. Adding all interest income (from loans and deposits) 2. Subtracting interest expenses 3. Subtracting operational costs 4. Adjusting for any extraordinary items

`set_corporate_taxes_paid(tau_bank: float) -> None` ¶

Calculate and set corporate taxes paid by banks.

Parameters:

Name	Type	Description	Default
`tau_bank`	`float`	Corporate tax rate applicable to banks	required

`set_market_share() -> None` ¶

Calculate and set market share for each bank.

Market share is computed based on: - Total assets - Total deposits - Number of customers (firms and households)

`set_liability() -> None` ¶

Calculate and set total liabilities for each bank.

Liabilities include: - Customer deposits (firm and household) - Interbank borrowing - Other funding sources

DefaultSyntheticBanks¶

DefaultSyntheticBanks is a concrete implementation of SyntheticBanks that provides preprocessing of banking system data using standard data sources (OECD, Eurostat, Compustat). This class handles the initialization and organization of data that will be used to initialize behavioral models in the simulation package.

Data Source Integration¶

The class integrates data from multiple sources:

OECD economic indicators
Eurostat banking statistics
Compustat bank-level data
National accounts data

Initial State Processing¶

The class processes:

Bank balance sheet data
Historical rate parameters
Customer relationship mappings
Initial state calculations

Parameter Estimation¶

Key parameters estimated include:

Interest rate parameters
Balance sheet ratios
Market share calculations
Risk premiums

Factory Methods¶

The class provides two factory methods:

from_readers: Creates a DefaultSyntheticBanks instance using standard data sources
from_readers_compustat: Creates a DefaultSyntheticBanks instance using detailed Compustat data

Implementation¶

Module for preprocessing synthetic banking system data.

This module provides a concrete implementation for preprocessing banking system data that will be used to initialize behavioral models. Key preprocessing includes:

Data Collection and Processing:
Bank balance sheet data preparation
Historical rate parameter estimation
Initial state calculations
Bank Data Organization:
Standard bank data preprocessing
Compustat-based data preprocessing
Data validation and consistency checks
Parameter Estimation:
Interest rate parameters
Balance sheet ratios
Market share calculations

Note

This module is NOT used for simulating bank behavior. It preprocesses data that will be used to initialize behavioral models in the simulation package. The actual banking decisions and operations are implemented elsewhere.

`DefaultSyntheticBanks` ¶

Default implementation for preprocessing banking system data.

This class preprocesses and organizes banking system data by collecting historical data and estimating parameters. These will be used to initialize behavioral models, but this class does NOT implement any behavioral logic.

The class provides two preprocessing paths: 1. Standard preprocessing using OECD/Eurostat data 2. Compustat-based preprocessing for detailed bank profiles

The preprocessing includes: - Bank balance sheet data organization - Interest rate parameter estimation - Relationship mapping (bank-firm, bank-household) - Initial state calculations

Note

This is a data container class. The actual banking behavior (lending, rate setting, etc.) is implemented in the simulation package, which uses these preprocessed parameters.

Attributes:

Name	Type	Description
`country_name`	`str`	Country identifier for data collection
`year`	`int`	Reference year for preprocessing
`number_of_banks`	`int`	Number of banks to preprocess data for
`bank_data`	`DataFrame`	Preprocessed bank-level data
`quarter`	`int`	Reference quarter for preprocessing
`firm_passthrough`	`float`	Estimated rate adjustment parameter
`firm_ect`	`float`	Estimated error correction parameter
`firm_rate`	`float`	Initial firm loan rate
`hh_consumption_passthrough`	`float`	Estimated consumer rate parameter
`hh_consumption_ect`	`float`	Estimated consumer ECT parameter
`hh_consumption_rate`	`float`	Initial consumer loan rate
`hh_mortgage_passthrough`	`float`	Estimated mortgage rate parameter
`hh_mortgage_ect`	`float`	Estimated mortgage ECT parameter
`hh_mortgage_rate`	`float`	Initial mortgage rate
`proxy_country`	`Optional[Country]`	Country to use as proxy when missing data

`init(country_name: str, year: int, number_of_banks: int, bank_data: pd.DataFrame, quarter: int, firm_passthrough: float, firm_ect: float, firm_rate: float, hh_consumption_passthrough: float, hh_consumption_ect: float, hh_consumption_rate: float, hh_mortgage_passthrough: float, hh_mortgage_ect: float, hh_mortgage_rate: float, proxy_country: Optional[Country] = None)` ¶

Initialize the banking system data container.

Parameters:

Name	Type	Description	Default
`country_name`	`str`	Country identifier for data collection	required
`year`	`int`	Reference year for preprocessing	required
`number_of_banks`	`int`	Number of banks to preprocess data for	required
`bank_data`	`DataFrame`	Initial data to preprocess	required
`quarter`	`int`	Reference quarter for preprocessing	required
`firm_passthrough`	`float`	Estimated rate adjustment parameter	required
`firm_ect`	`float`	Estimated error correction parameter	required
`firm_rate`	`float`	Initial firm loan rate	required
`hh_consumption_passthrough`	`float`	Estimated consumer rate parameter	required
`hh_consumption_ect`	`float`	Estimated consumer ECT parameter	required
`hh_consumption_rate`	`float`	Initial consumer loan rate	required
`hh_mortgage_passthrough`	`float`	Estimated mortgage rate parameter	required
`hh_mortgage_ect`	`float`	Estimated mortgage ECT parameter	required
`hh_mortgage_rate`	`float`	Initial mortgage rate	required
`proxy_country`	`Optional[Country]`	Country to use as proxy when missing data	`None`

`from_readers(single_bank: bool, country_name: Country, year: int, readers: DataReaders, scale: int, banks_data_configuration: BanksDataConfiguration, quarter: int, inflation_data: pd.DataFrame, exchange_rate_from_eur: float = 1.0, proxy_eu_country: Optional[Country] = None) -> DefaultSyntheticBanks` `classmethod` ¶

Create a preprocessed banking system data container using standard data sources.

This method preprocesses data using OECD/Eurostat sources to prepare: 1. Bank balance sheet data (scaled appropriately) 2. Initial rate parameters 3. Bank relationship mappings

For standard preprocessing: - Number of banks is derived from actual bank branches (scaled) - Bank equity is distributed based on historical data - Rate parameters are estimated from historical rates

Parameters:

Name	Type	Description	Default
`single_bank`	`bool`	Whether to preprocess data for a single bank	required
`country_name`	`Country`	Country to preprocess data for	required
`year`	`int`	Reference year for preprocessing	required
`readers`	`DataReaders`	Data source readers	required
`scale`	`int`	Scaling factor for bank numbers	required
`banks_data_configuration`	`BanksDataConfiguration`	Preprocessing configuration	required
`quarter`	`int`	Reference quarter for preprocessing	required
`inflation_data`	`DataFrame`	Historical inflation data	required
`exchange_rate_from_eur`	`float`	Exchange rate for conversion. Defaults to 1.0.	`1.0`
`proxy_eu_country`	`Optional[Country]`	EU country for proxy data. Defaults to None.	`None`

Returns:

Name	Type	Description
`DefaultSyntheticBanks`	`DefaultSyntheticBanks`	Container with preprocessed banking system data

`from_readers_compustat(country_name: Country, year: int, readers: DataReaders, single_bank: bool, scale: int, quarter: int, inflation_data: pd.DataFrame, exchange_rate_from_eur: float = 1.0, proxy_eu_country: Optional[Country] = None) -> DefaultSyntheticBanks` `classmethod` ¶

Create a preprocessed banking system data container using Compustat data.

This method preprocesses detailed Compustat data to prepare: 1. Historical balance sheet information 2. Actual deposit and loan distributions 3. Historical equity levels and ratios

The preprocessing steps: 1. Fetch and filter relevant Compustat bank data 2. Sample and scale data appropriately 3. Align with country-level totals 4. Estimate initial parameters

Parameters:

Name	Type	Description	Default
`country_name`	`Country`	Country to preprocess data for	required
`year`	`int`	Reference year for preprocessing	required
`readers`	`DataReaders`	Data source readers	required
`single_bank`	`bool`	Whether to preprocess data for a single bank	required
`scale`	`int`	Scaling factor for bank numbers	required
`quarter`	`int`	Reference quarter for preprocessing	required
`inflation_data`	`DataFrame`	Historical inflation data	required
`exchange_rate_from_eur`	`float`	Exchange rate for conversion. Defaults to 1.0.	`1.0`
`proxy_eu_country`	`Optional[Country]`	EU country for proxy data. Defaults to None.	`None`

Returns:

Name	Type	Description
`DefaultSyntheticBanks`	`DefaultSyntheticBanks`	Container with preprocessed banking system data

`initialise_rates(country_name, inflation_data, proxy_eu_country, quarter, readers, year)` `classmethod` ¶

Preprocess and estimate initial interest rate parameters.

This method: 1. Collects historical rate data 2. Estimates rate adjustment parameters 3. Calculates initial rates for different products

The preprocessing includes parameter estimation for: - Firm loan rates - Consumer loan rates - Mortgage rates - Default parameters when data is insufficient

Parameters:

Name	Type	Description	Default
`country_name`	`Country`	Country to process data for	required
`inflation_data`	`DataFrame`	Historical inflation data	required
`proxy_eu_country`	`Optional[Country]`	EU country for proxy data	required
`quarter`	`int`	Reference quarter	required
`readers`	`DataReaders`	Data source readers	required
`year`	`int`	Reference year	required

Returns:

Name	Type	Description
`tuple`		Nine estimated parameters: - firm_ect: Estimated error correction for firm rates - firm_passthrough: Estimated adjustment for firm rates - firm_rate: Initial firm rate - hh_consumption_ect: Estimated consumer rate ECT - hh_consumption_passthrough: Estimated consumer rate adjustment - hh_consumption_rate: Initial consumer rate - hh_mortgage_ect: Estimated mortgage ECT - hh_mortgage_passthrough: Estimated mortgage adjustment - hh_mortgage_rate: Initial mortgage rate

`set_bank_equity(bank_equity: float) -> None` ¶

Set the equity level for each bank.

Parameters:

Name	Type	Description	Default
`bank_equity`	`float`	Equity amount to set for each bank	required

`set_deposits_from_firms(firm_deposits: np.ndarray) -> None` ¶

Set the initial deposits from firms for each bank.

Parameters:

Name	Type	Description	Default
`firm_deposits`	`ndarray`	Array of deposit amounts by firm	required

`set_deposits_from_households(household_deposits: np.ndarray) -> None` ¶

Set the initial deposits from households for each bank.

Parameters:

Name	Type	Description	Default
`household_deposits`	`ndarray`	Array of deposit amounts by household	required

`set_loans_to_firms(firm_debt: np.ndarray) -> None` ¶

Set the initial loans to firms for each bank.

Parameters:

Name	Type	Description	Default
`firm_debt`	`ndarray`	Array of debt amounts by firm	required

`set_loans_to_households(household_mortgage_debt: np.ndarray, household_other_debt: np.ndarray) -> None` ¶

Set the initial loans to households for each bank.

Parameters:

Name	Type	Description	Default
`household_mortgage_debt`	`ndarray`	Array of mortgage debt by household	required
`household_other_debt`	`ndarray`	Array of non-mortgage debt by household	required

`set_bank_deposits(firm_deposits: np.ndarray, firm_debt: np.ndarray, household_deposits: np.ndarray, household_debt: np.ndarray) -> None` ¶

Set the total deposits for each bank.

Parameters:

Name	Type	Description	Default
`firm_deposits`	`ndarray`	Array of firm deposit amounts	required
`firm_debt`	`ndarray`	Array of firm debt amounts	required
`household_deposits`	`ndarray`	Array of household deposit amounts	required
`household_debt`	`ndarray`	Array of household debt amounts	required

SyntheticBanks¶

Core Functionality¶

Key Methods¶

Implementation¶

SyntheticBanks ¶

country_name = country_name instance-attribute ¶

year = year instance-attribute ¶

quarter = quarter instance-attribute ¶

number_of_banks = number_of_banks instance-attribute ¶

bank_data = bank_data instance-attribute ¶

firm_passthrough = firm_passthrough instance-attribute ¶

firm_ect = firm_ect instance-attribute ¶

firm_rate = firm_rate instance-attribute ¶

hh_consumption_passthrough = hh_consumption_passthrough instance-attribute ¶

hh_consumption_ect = hh_consumption_ect instance-attribute ¶

hh_consumption_rate = hh_consumption_rate instance-attribute ¶

hh_mortgage_passthrough = hh_mortgage_passthrough instance-attribute ¶

hh_mortgage_ect = hh_mortgage_ect instance-attribute ¶

hh_mortgage_rate = hh_mortgage_rate instance-attribute ¶

initialise_deposits_and_loans(synthetic_population: SyntheticPopulation, firm_deposits: np.ndarray, firm_debt: np.ndarray) -> None ¶

set_deposits_from_firms(firm_deposits: np.ndarray) -> None abstractmethod ¶

set_deposits_from_households(household_deposits: np.ndarray) -> None abstractmethod ¶

set_loans_to_firms(firm_debt: np.ndarray) -> None abstractmethod ¶

set_loans_to_households(household_mortgage_debt: np.ndarray, household_other_debt: np.ndarray) -> None abstractmethod ¶

set_bank_equity(bank_equity: float) -> None abstractmethod ¶

set_bank_deposits(firm_deposits: np.ndarray, household_deposits: np.ndarray, firm_debt: np.ndarray, household_debt: np.ndarray) -> None abstractmethod ¶

initialise_rates_profits_liabilities(policy_rate: float, tau_bank: float, risk_premium: float, consumption_loans_markup: float, mortgage_markup: float, household_overdraft_markup: float) ¶

set_interest_received_from_loans() -> None ¶

set_interest_received_from_deposits(central_bank_policy_rate: float) -> None ¶

set_profits() -> None ¶

set_corporate_taxes_paid(tau_bank: float) -> None ¶

set_market_share() -> None ¶

set_liability() -> None ¶

DefaultSyntheticBanks¶

Data Source Integration¶

Initial State Processing¶

Parameter Estimation¶

Factory Methods¶

Implementation¶

DefaultSyntheticBanks ¶

from_readers_compustat(country_name: Country, year: int, readers: DataReaders, single_bank: bool, scale: int, quarter: int, inflation_data: pd.DataFrame, exchange_rate_from_eur: float = 1.0, proxy_eu_country: Optional[Country] = None) -> DefaultSyntheticBanks classmethod ¶

initialise_rates(country_name, inflation_data, proxy_eu_country, quarter, readers, year) classmethod ¶

set_bank_equity(bank_equity: float) -> None ¶

set_deposits_from_firms(firm_deposits: np.ndarray) -> None ¶

set_deposits_from_households(household_deposits: np.ndarray) -> None ¶

set_loans_to_firms(firm_debt: np.ndarray) -> None ¶

set_loans_to_households(household_mortgage_debt: np.ndarray, household_other_debt: np.ndarray) -> None ¶

set_bank_deposits(firm_deposits: np.ndarray, firm_debt: np.ndarray, household_deposits: np.ndarray, household_debt: np.ndarray) -> None ¶

`SyntheticBanks` ¶

`country_name = country_name` `instance-attribute` ¶

`year = year` `instance-attribute` ¶

`quarter = quarter` `instance-attribute` ¶

`number_of_banks = number_of_banks` `instance-attribute` ¶

`bank_data = bank_data` `instance-attribute` ¶

`firm_passthrough = firm_passthrough` `instance-attribute` ¶

`firm_ect = firm_ect` `instance-attribute` ¶

`firm_rate = firm_rate` `instance-attribute` ¶

`hh_consumption_passthrough = hh_consumption_passthrough` `instance-attribute` ¶

`hh_consumption_ect = hh_consumption_ect` `instance-attribute` ¶

`hh_consumption_rate = hh_consumption_rate` `instance-attribute` ¶

`hh_mortgage_passthrough = hh_mortgage_passthrough` `instance-attribute` ¶

`hh_mortgage_ect = hh_mortgage_ect` `instance-attribute` ¶

`hh_mortgage_rate = hh_mortgage_rate` `instance-attribute` ¶

`initialise_deposits_and_loans(synthetic_population: SyntheticPopulation, firm_deposits: np.ndarray, firm_debt: np.ndarray) -> None` ¶

`set_deposits_from_firms(firm_deposits: np.ndarray) -> None` `abstractmethod` ¶

`set_deposits_from_households(household_deposits: np.ndarray) -> None` `abstractmethod` ¶

`set_loans_to_firms(firm_debt: np.ndarray) -> None` `abstractmethod` ¶

`set_loans_to_households(household_mortgage_debt: np.ndarray, household_other_debt: np.ndarray) -> None` `abstractmethod` ¶

`set_bank_equity(bank_equity: float) -> None` `abstractmethod` ¶

`set_bank_deposits(firm_deposits: np.ndarray, household_deposits: np.ndarray, firm_debt: np.ndarray, household_debt: np.ndarray) -> None` `abstractmethod` ¶

`initialise_rates_profits_liabilities(policy_rate: float, tau_bank: float, risk_premium: float, consumption_loans_markup: float, mortgage_markup: float, household_overdraft_markup: float)` ¶

`set_interest_received_from_loans() -> None` ¶

`set_interest_received_from_deposits(central_bank_policy_rate: float) -> None` ¶

`set_profits() -> None` ¶

`set_corporate_taxes_paid(tau_bank: float) -> None` ¶

`set_market_share() -> None` ¶

`set_liability() -> None` ¶

`DefaultSyntheticBanks` ¶

`from_readers_compustat(country_name: Country, year: int, readers: DataReaders, single_bank: bool, scale: int, quarter: int, inflation_data: pd.DataFrame, exchange_rate_from_eur: float = 1.0, proxy_eu_country: Optional[Country] = None) -> DefaultSyntheticBanks` `classmethod` ¶

`initialise_rates(country_name, inflation_data, proxy_eu_country, quarter, readers, year)` `classmethod` ¶

`set_bank_equity(bank_equity: float) -> None` ¶

`set_deposits_from_firms(firm_deposits: np.ndarray) -> None` ¶

`set_deposits_from_households(household_deposits: np.ndarray) -> None` ¶

`set_loans_to_firms(firm_debt: np.ndarray) -> None` ¶

`set_loans_to_households(household_mortgage_debt: np.ndarray, household_other_debt: np.ndarray) -> None` ¶

`set_bank_deposits(firm_deposits: np.ndarray, firm_debt: np.ndarray, household_deposits: np.ndarray, household_debt: np.ndarray) -> None` ¶