dcegm.pre_processing.batches.batch_creation

Functions

create_batches_and_information(model_structure, n_periods)

Batches are used instead of periods to have chunks of equal sized state choices.

Module Contents

dcegm.pre_processing.batches.batch_creation.create_batches_and_information(model_structure, n_periods, min_period_batch_segments=None)

Batches are used instead of periods to have chunks of equal sized state choices. The batch inparams=paramsformation dictionary contains the following arrays reflecting the.

steps in the backward induction:

  • batches_state_choice_idx: The state choice indexes in each batch to be solved.

To solve the state choices in the egm step, we have to look at the child states and the corresponding state choice indexes in the child states. For that we save the following:

  • child_state_choice_idxs_to_interp: The state choice indexes in we need to

    interpolate the wealth on.

  • child_states_idxs: The parent state indexes of the child states, i.e. the

    child states themself. We calculate the resources at the beginning of period before the backwards induction with the budget equation for each saving and income shock grid point.

Note: These two index arrays containing indexes on the whole state/state-choice space.

Once we have the interpolated in all possible child state-choice states, we rearange them to an array with row as states and columns as choices to aggregate over the choices. This is saved in:

  • child_state_choices_to_aggr_choice: The state choice indexes in the child

    states to aggregate over. Note these are relative indexes indexing to the batch arrays from the step before.

Now we have for each child state a value/marginal utility with the index arrays above and what is missing is the mapping for the exogenous/stochastic processes. This is saved via:

  • child_states_to_integrate_exog: The state choice indexes in the child states

    to integrate over the exogenous processes. This is a relative index to the batch arrays from the step before.