Scenarios PichlerEtAl2022DIO#

The PichlerEtAl2022DIO model uses time-varying exogenous shocks to simulate the economic impact of pandemic-induced supply and demand disruptions. Scenarios are managed by macrostat.models.PichlerEtAl2022DIO.scenarios.ScenariosPichlerEtAl2022DIO.

Scenario variables#

The following scenario variables are defined with default (no-shock) values in the steady state:

  • SupplyShock – \(\epsilon^S_{i,t} \in [0, 1]\), vector (T, N). Fraction of industry i’s labour supply that is unavailable. Default: 0 (no supply disruption).

  • DemandPreferences – \(\theta_{i,t}\), vector (T, N). Time-varying household consumption preference shares across industries. Default: initial steady-state consumption shares.

  • FearOfInfection – \(\tilde{\epsilon}^D_t \in [0, 1]\), scalar (T, 1). Aggregate demand reduction due to fear of infection. Default: 0.

  • PermanentIncomeExpectation – \(\xi_t\), scalar (T, 1). Scaling factor on perceived permanent income. Default: 1 (no pessimism).

  • OtherFinalDemand – \(f^d_{i,t}\), vector (T, N). Exogenous non-household final demand (government, investment, exports). Default: initial steady-state value.

Building scenarios#

Scenarios can be constructed in two ways:

  1. From the shock CSV (recommended for replication of the paper):

    from macrostat.models.PichlerEtAl2022DIO import (
    ParametersPichlerEtAl2022DIO,
    ScenariosPichlerEtAl2022DIO,
)

params = ParametersPichlerEtAl2022DIO.from_wiod_uk(
    data_dir="path/to/data",
    ihs_dir="path/to/ihs",
    inv_file="path/to/inventories.csv",
)
scenarios = ScenariosPichlerEtAl2022DIO.from_shocks_csv(
    parameters=params,
    shock_csv="path/to/shock_scenarios.csv",
)

    The from_shocks_csv class method reads the same shock_scenarios.csv used by the original R replication code and constructs the full (T, N) shock tensors (supply shocks, demand preference reshuffling, fear-of-infection, and other final demand reductions) with the correct timing relative to the lockdown start date.

  2. Manually via add_vector_scenario:

    scenarios.add_vector_scenario(
    timeseries={
        "SupplyShock": my_supply_tensor,       # shape (T, N)
        "FearOfInfection": my_fear_tensor,      # shape (T, 1)
    },
    name="Custom scenario",
)