dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions

Functions

create_utility_function_dict()	Create dictionary with utility functions.
utility_crra(→ jax.numpy.array)	Computes the agent's current utility based on a CRRA utility function.
marginal_utility_crra(→ jax.numpy.array)	Computes marginal utility of CRRA utility function.
inverse_marginal_utility_crra(→ jax.numpy.array)	Computes the inverse marginal utility of a CRRA utility function.
create_final_period_utility_function_dict()	Create dictionary with utility functions for the final period.
utility_final_consume_all(choice, wealth, params)
marginal_utility_final_consume_all(→ jax.numpy.array)	Computes marginal utility of CRRA utility function.

Module Contents

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.create_utility_function_dict()

Create dictionary with utility functions.

Returns:

Dictionary with utility functions.

Return type:

utility_functions (dict)

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.utility_crra(consumption: jax.numpy.array, choice: int, params: Dict[str, float]) → jax.numpy.array

Computes the agent’s current utility based on a CRRA utility function.

Parameters:

• consumption (jnp.array) – Level of the agent’s consumption. Array of shape (i) (n_quad_stochastic * n_grid_wealth,) when called by map_exog_to_endog_grid() and get_next_period_value(), or (ii) of shape (n_grid_wealth,) when called by get_current_period_value().

• choice (int) – Choice of the agent, e.g. 0 = “retirement”, 1 = “working”.

• params (dict) – Dictionary containing model parameters. Relevant here is the CRRA coefficient theta.

Returns:

Agent’s utility . Array of shape

(n_quad_stochastic * n_grid_wealth,) or (n_grid_wealth,).

Return type:

utility (jnp.array)

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.marginal_utility_crra(consumption: jax.numpy.array, params: Dict[str, float]) → jax.numpy.array

Computes marginal utility of CRRA utility function.

Parameters:

• consumption (jnp.array) – Level of the agent’s consumption. Array of shape (n_quad_stochastic * n_grid_wealth,).

• params (dict) – Dictionary containing model parameters. Relevant here is the CRRA coefficient theta.

Returns:

Marginal utility of CRRA consumption

function. Array of shape (n_quad_stochastic * n_grid_wealth,).

Return type:

marginal_utility (jnp.array)

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.inverse_marginal_utility_crra(marginal_utility: jax.numpy.array, params: Dict[str, float]) → jax.numpy.array

Computes the inverse marginal utility of a CRRA utility function.

Parameters:

• marginal_utility (jnp.array) – Level of marginal CRRA utility. Array of shape (n_grid_wealth,).

• params (dict) – Dictionary containing model parameters.

Returns:

Inverse of the marginal utility of

a CRRA consumption function. Array of shape (n_grid_wealth,).

Return type:

inverse_marginal_utility(jnp.array)

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.create_final_period_utility_function_dict()

Create dictionary with utility functions for the final period.

Returns:

Dictionary with utility functions

for the final period.

Return type:

utility_functions_final_period (dict)

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.utility_final_consume_all(choice: int, wealth: jax.numpy.array, params: Dict[str, float])

dcegm.toy_models.cons_ret_model_dcegm_paper.utility_functions.marginal_utility_final_consume_all(choice, wealth: jax.numpy.array, params: Dict[str, float], model_specs: Dict[str, Any]) → jax.numpy.array

Computes marginal utility of CRRA utility function.

Parameters:

• consumption (jnp.array) – Level of the agent’s consumption. Array of shape (n_quad_stochastic * n_grid_wealth,).

• params (dict) – Dictionary containing model parameters. Relevant here is the CRRA coefficient theta.

Returns:

Marginal utility of CRRA consumption

function. Array of shape (n_quad_stochastic * n_grid_wealth,).

Return type:

marginal_utility (jnp.array)