frequency-conversion in macro-data initialization

macro_data/configuration/dataconfiguration.py

@@ -23,10 +23,7 @@
         initial_inventory_to_input_fraction=0.1
     )
 
-    banks_config = BanksDataConfiguration(
-        long_term_firm_loan_maturity=60,
-        mortgage_maturity=120
-    )
+    banks_config = BanksDataConfiguration()
 
     # Create country configuration
     france_config = CountryDataConfiguration(
@@ -54,9 +51,13 @@
 
 from pydantic import BaseModel, Field
 
+from macromodel.configurations.bank_configuration import BankParameters
+
 from .countries import Country
 from .region import Region
 
+_MODEL_BANK_PARAMETERS = BankParameters()
+
 
 class FirmsDataConfiguration(BaseModel):
     """
@@ -107,18 +108,29 @@ class BanksDataConfiguration(BaseModel):
     This class controls how synthetic bank data is generated, including loan
     maturities and interest rate settings.
 
+    Loan maturities are expressed as model-period counts, not calendar months.
+
     Attributes:
         constructor (Literal["Compustat", "Default"]): The data constructor to use
-        long_term_firm_loan_maturity (int): Maturity period (months) for long-term firm loans
-        consumption_exp_loan_maturity (int): Maturity period (months) for consumption loans
-        mortgage_maturity (int): Maturity period (months) for mortgages
+        long_term_firm_loan_maturity (int): Initial maturity in model periods, not months, for long-term firm loans
+        consumption_exp_loan_maturity (int): Initial maturity in model periods, not months, for consumption loans
+        mortgage_maturity (int): Initial maturity in model periods, not months, for mortgages
         interest_rates (InterestRates): Interest rate markup configuration
     """
 
     constructor: Literal["Compustat", "Default"] = "Compustat"
-    long_term_firm_loan_maturity: int = 60
-    consumption_exp_loan_maturity: int = 12
-    mortgage_maturity: int = 120
+    long_term_firm_loan_maturity: int = Field(
+        _MODEL_BANK_PARAMETERS.long_term_firm_loan_maturity,
+        description="Initial maturity in model periods, not months, for long-term firm loans.",
+    )
+    consumption_exp_loan_maturity: int = Field(
+        _MODEL_BANK_PARAMETERS.household_consumption_loan_maturity,
+        description="Initial maturity in model periods, not months, for consumption loans.",
+    )
+    mortgage_maturity: int = Field(
+        _MODEL_BANK_PARAMETERS.mortgage_maturity,
+        description="Initial maturity in model periods, not months, for mortgages.",
+    )
     interest_rates: InterestRates = InterestRates()
 
 

macro_data/data_wrapper.py

@@ -62,10 +62,44 @@
     EmissionsData,
     EmissionsEnergyFactors,
 )
-from macro_data.readers.exogenous_data import ExogenousCountryData
+from macro_data.readers.exogenous_data import ExogenousCountryData, convert_growth_rates_to_model_period
 from macro_data.readers.io_tables.icio_reader import ICIOReader
 
 
+def _quarter_start_date(year: int, quarter: int) -> pd.Timestamp:
+    return pd.Timestamp(year, 3 * (quarter - 1) + 1, 1)
+
+
+def _period_start_for_date(index: pd.Index, date: pd.Timestamp) -> pd.Timestamp:
+    index = pd.DatetimeIndex(index).sort_values()
+    if date in index:
+        return date
+
+    prior_dates = index[index <= date]
+    if len(prior_dates) > 0:
+        return prior_dates[-1]
+
+    later_dates = index[index > date]
+    if len(later_dates) > 0:
+        return later_dates[0]
+
+    raise ValueError("Cannot select a period from an empty calibration index.")
+
+
+def _period_values_at_date(data: pd.DataFrame, date: pd.Timestamp) -> pd.Series:
+    if date in data.index:
+        return data.loc[date]
+
+    return (
+        data.reindex(data.index.union(pd.DatetimeIndex([date])))
+        .sort_index()
+        .interpolate(method="time")
+        .ffill()
+        .bfill()
+        .loc[date]
+    )
+
+
 @dataclass
 class DataWrapper:
     """
@@ -181,6 +215,10 @@ def from_config(
 
         year = configuration.year
         quarter = configuration.quarter
+        # Backward compatibility: legacy configs omitted `time_unit` and implicitly used quarterly scaling.
+        fields_set = getattr(configuration, "model_fields_set", set())
+        effective_time_unit = configuration.time_unit if "time_unit" in fields_set else 3
+        yearly_factor = 12 / effective_time_unit
 
         scale_dict = {country: configuration.country_configs[country].scale for country in country_names}
 
@@ -211,6 +249,8 @@ def from_config(
             use_disagg_can_2014_reader=configuration.can_disaggregation,
             use_provincial_can_reader=use_provincial_can_reader,
             regions_dict=regions_dict,
+            yearly_factor=yearly_factor,
+            simulation_quarter=quarter,
         )
 
         if regions_dict:
@@ -241,6 +281,7 @@ def from_config(
             readers=readers,
             country_names=country_names,
             single_firm_per_industry=single_firm_dict,
+            yearly_factor=yearly_factor,
         )
 
         year_range = 1 if single_hfcs_survey else 10
@@ -255,6 +296,7 @@ def from_config(
                 quarter=quarter,
                 industry_vectors=industry_data[country]["industry_vectors"],
                 proxy_country=proxy_country_dict.get(country, None),
+                time_unit=effective_time_unit,
             )
             for country in country_names
         }
@@ -269,6 +311,7 @@ def from_config(
                 inflation = readers.imf_reader.get_inflation(proxy_country)
                 if inflation is None:
                     inflation = readers.world_bank.get_inflation(proxy_country)
+                inflation = convert_growth_rates_to_model_period(inflation, effective_time_unit)
                 proxy_inflation[country] = inflation
             else:
                 proxy_inflation[country] = None
@@ -291,6 +334,7 @@ def from_config(
                 country=country,
                 year=year,
                 quarter=quarter,
+                time_unit=effective_time_unit,
                 country_configuration=configuration.country_configs[country],
                 industries=industries,
                 readers=readers,
@@ -316,14 +360,15 @@ def from_config(
                     country=country,
                     proxy_country=configuration.country_configs[country].eu_proxy_country,
                     year=year,
+                    quarter=quarter,
+                    time_unit=effective_time_unit,
                     country_configuration=configuration.country_configs[country],
                     industries=industries,
                     readers=readers,
                     exogenous_country_data=exogenous_data[country],
                     country_industry_data=industry_data[country],
                     year_range=year_range,
                     goods_criticality_matrix=readers.goods_criticality.criticality_matrix,
-                    quarter=quarter,
                     proxy_inflation_data=proxy_inflation[country],
                     emission_factors=(
                         EmissionsData.from_readers(
@@ -372,7 +417,7 @@ def from_config(
                 EmissionsEnergyFactors.from_readers(readers.icio[year], country_names) if add_emissions else None
             ),
             aggregation_structure=configuration.aggregation_structure,
-            time_unit=configuration.time_unit,
+            time_unit=effective_time_unit,
         )
 
     @classmethod
@@ -423,7 +468,8 @@ def calibration_before(self):
         year = self.configuration.year
         quarter = self.configuration.quarter
         calibration_index = self.calibration_data.index
-        calibration_before_index = calibration_index[calibration_index < f"{year}-Q{quarter}"]
+        start_date = _period_start_for_date(calibration_index, _quarter_start_date(year, quarter))
+        calibration_before_index = calibration_index[calibration_index < start_date]
         return self.calibration_data.loc[calibration_before_index]
 
     @property
@@ -438,7 +484,8 @@ def calibration_during(self):
         year = self.configuration.year
         quarter = self.configuration.quarter
         calibration_index = self.calibration_data.index
-        calibration_during_index = calibration_index[calibration_index == f"{year}-Q{quarter}"]
+        start_date = _period_start_for_date(calibration_index, _quarter_start_date(year, quarter))
+        calibration_during_index = calibration_index[calibration_index == start_date]
         return self.calibration_data.loc[calibration_during_index]
 
 
@@ -472,8 +519,9 @@ def add_row_to_calibration(
     all_exports = calibration_data.xs("Exports (Value)", axis=1, level=1)
     all_imports = calibration_data.xs("Imports (Value)", axis=1, level=1)
 
-    scaled_exports = all_exports / all_exports.loc[f"{year}-Q{quarter}"].iloc[0]
-    scaled_imports = all_imports / all_imports.loc[f"{year}-Q{quarter}"].iloc[0]
+    base_date = _quarter_start_date(year, quarter)
+    scaled_exports = all_exports / _period_values_at_date(all_exports, base_date)
+    scaled_imports = all_imports / _period_values_at_date(all_imports, base_date)
 
     total_country_exports = sum(
         [country_scaled_exports(country, industry_data, scaled_exports) for country in countries]

macro_data/default_country_conf.yaml

@@ -17,17 +17,11 @@ central_bank_configuration:
     0.02
 
 banks_configuration:
-  long_term_firm_loan_maturity:
-    60
-  consumption_exp_loan_maturity:
-    12
-  mortgage_maturity:
-    120
-
+  # Loan maturities are expressed in model periods, not calendar months.
   interest_rates:
     consumption_loans_markup:
       0.01
     mortgage_markup:
       0.1
     household_overdraft_markup:
-      0.01
+      0.01

macro_data/processing/synthetic_central_government/default_synthetic_central_government.py

@@ -100,6 +100,7 @@ def from_readers(
         year_range: int = 10,
         regression_window: int = 48,
         equity_injection: float = 0.0,
+        yearly_factor: float = 4.0,
     ) -> SyntheticCentralGovernment:
         """Create a preprocessed central government data container using standard data sources.
 
@@ -129,7 +130,11 @@ def from_readers(
         if country_exogenous_data is not None:
             # if exogenous data is available, use it to fit the benefits models
             benefits_inflation_data = readers.get_benefits_inflation_data(
-                country_name, year_min=year - year_range, year_max=year, exogenous_data=country_exogenous_data
+                country_name,
+                year_min=year - year_range,
+                year_max=year,
+                exogenous_data=country_exogenous_data,
+                yearly_factor=yearly_factor,
             )
             unemployment_benefits_model = build_unemployment_model(
                 benefits_inflation_data, regression_window=regression_window
@@ -160,8 +165,15 @@ def from_readers(
             # if exogenous data is not available, set the benefits models to None
             unemployment_benefits_model = None
             other_benefits_model = None
-            current_unemployment_benefits = readers.get_total_unemployment_benefits_lcu(country_name, year)
-            current_other_benefits = readers.get_total_benefits_lcu(country_name, year) - current_unemployment_benefits
+            current_unemployment_benefits = readers.get_total_unemployment_benefits_lcu(
+                country_name,
+                year,
+                yearly_factor=yearly_factor,
+            )
+            current_other_benefits = (
+                readers.get_total_benefits_lcu(country_name, year, yearly_factor=yearly_factor)
+                - current_unemployment_benefits
+            )
 
         # TODO: debt in USD or in local currency?
 

macro_data/processing/synthetic_country.py

@@ -176,6 +176,7 @@ def eu_synthetic_country(
         country: Country,
         year: int,
         quarter: int,
+        time_unit: int,
         country_configuration: CountryDataConfiguration,
         industries: list[str],
         readers: DataReaders,
@@ -215,7 +216,14 @@ def eu_synthetic_country(
             This method should only be used for EU member countries. For non-EU
             countries, use proxied_synthetic_country instead.
         """
-        central_government = DefaultSyntheticCGovernment.from_readers(readers, country, year, year_range=year_range)
+        yearly_factor = 12 / time_unit
+        central_government = DefaultSyntheticCGovernment.from_readers(
+            readers,
+            country,
+            year,
+            year_range=year_range,
+            yearly_factor=yearly_factor,
+        )
 
         total_unemployment_benefits = central_government.central_gov_data["Total Unemployment Benefits"].values[0]
 
@@ -245,6 +253,7 @@ def eu_synthetic_country(
             total_unemployment_benefits=total_unemployment_benefits,
             country_name_short=country.to_two_letter_code(),
             exogenous_data=exogenous_country_data,
+            yearly_factor=yearly_factor,
         )
 
         firms = DefaultSyntheticFirms.from_readers(
@@ -355,6 +364,7 @@ def proxied_synthetic_country(
         proxy_country: Country,
         year: int,
         quarter: int,
+        time_unit: int,
         country_configuration: CountryDataConfiguration,
         industries: list[str],
         readers: DataReaders,
@@ -394,7 +404,14 @@ def proxied_synthetic_country(
         Returns:
             SyntheticCountry: Initialized synthetic country instance
         """
-        central_government = DefaultSyntheticCGovernment.from_readers(readers, country, year, year_range=year_range)
+        yearly_factor = 12 / time_unit
+        central_government = DefaultSyntheticCGovernment.from_readers(
+            readers,
+            country,
+            year,
+            year_range=year_range,
+            yearly_factor=yearly_factor,
+        )
 
         total_unemployment_benefits = central_government.central_gov_data["Total Unemployment Benefits"].values[0]
 
@@ -433,6 +450,7 @@ def proxied_synthetic_country(
             proxied_country=country,
             quarter=quarter,
             exogenous_data=exogenous_country_data,
+            yearly_factor=yearly_factor,
         )
 
         firms = DefaultSyntheticFirms.from_readers(