model_funcs.state_space_objects¶
User-defined functions for creating state space objects.
Functions¶
Create dictionary with state space functions. |
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Select state-specific feasible choice set such that retirement is absorbing. |
Module Contents¶
- model_funcs.state_space_objects.create_state_space_function_dict()¶
Create dictionary with state space functions.
- Returns:
Dictionary with state space functions.
- Return type:
state_space_functions (dict)
- model_funcs.state_space_objects.get_state_specific_feasible_choice_set(lagged_choice: int, model_specs: Dict) numpy.ndarray¶
Select state-specific feasible choice set such that retirement is absorbing.
Will be a user defined function later.
This is very basic in Ishkakov et al (2017).
- Parameters:
state (np.ndarray) – Array of shape (n_state_variables,) defining the agent’s state. In Ishkakov, an agent’s state is defined by her (i) age (i.e. the current period) and (ii) her lagged labor market choice. Hence n_state_variables = 2.
map_state_to_state_space_index (np.ndarray) – Indexer array that maps a period-specific state vector to the respective index positions in the state space. The shape of this object is quite complicated. For each state variable it has the number of potential states as rows, i.e. (n_potential_states_state_var_1, n_potential_states_state_var_2, ….).
- Returns:
- 1d array of length (n_feasible_choices,) with the
agent’s (restricted) feasible choice set in the given state.
- Return type:
choice_set (np.ndarray)