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Update example models guide (#406)
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2 changes: 1 addition & 1 deletion .pre-commit-config.yaml
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Expand Up @@ -19,7 +19,7 @@ repos:
hooks:
- id: reorder-python-imports
- repo: https://github.com/psf/black
rev: 21.4b0
rev: 21.4b1
hooks:
- id: black
- repo: https://github.com/asottile/blacken-docs
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219 changes: 206 additions & 13 deletions docs/how_to_guides/how_to_example_models.rst
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Expand Up @@ -63,21 +63,82 @@ Below are the example models that are currently available.

- ``robinson_crusoe_basic``
- ``robinson_crusoe_extended``


The models are centered around Robinson Crusoe, who is stranded on a desert
island. In each period, Robinson decides between fishing or relaxing in a
hammock. In the extended model, he might additionally ask for Friday's advice
to further develop his fishing skills.

These models are excellent examples to use for learning and prototyping: They
include a small number of available choices and a low number of periods such
that they are computationally feasible.

Overview of model characteristics defined by ``params`` and ``options``:

.. tabs::
.. tab:: Basic Model

.. csv-table::
:header: "Parameters", " "
:widths: 20, 40

"Number of choices", "2"
"Work choices", "Fishing"
"Education choices", "None"
"Leisure choices", "Hammock"
"Number of parameters", "7"
"Shock Correlations", "Negative between fishing and hammock"


.. csv-table::
:header: "Options", " "
:widths: 20, 40

"Number of periods", "5"
"Solution draws", "100"
"Estimation draws", "100"
"Solution seed", "456"
"Simulation seed", "132"
"Estimation seed", "100"
"Estimation tau", "0.001"

.. tab:: Extended Model

.. csv-table::
:header: "Parameters", " "
:widths: 20, 40

"Number of choices", "3"
"Work choices", "Fishing"
"Education choices", "Friday"
"Leisure choices", "Hammock"
"Number of parameters", "15"
"Shock Correlations", "None"
"Lagged choices", "Hammock period 1"
"Covariates", "Break in fishing, contemplation with Friday"

.. csv-table::
:header: "Options", " "
:widths: 20, 40

"Number of periods", "10"
"Simulation agents", "1000"
"Solution draws", "500"
"Estimation draws", "200"
"Solution seed", "456"
"Simulation seed", "132"
"Estimation seed", "500"
"Estimation tau", "0.001"

.. tab:: KW (1994)

Aside from toy models, **respy** also provides several models that stem from the
economic literature on dynamic life-cycle models. The most simple examples are a group
of models based on the following publication:

- Keane, M. P., & Wolpin, K. I. (1994). The Solution and Estimation of Discrete Choice
Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence.
*The Review of Economics and Statistics*, 648-672.
* Keane, M. P., & Wolpin, K. I. (1994). The Solution and Estimation of Discrete Choice
Dynamic Programming Models by Simulation and Interpolation: Monte Carlo
Evidence. *The Review of Economics and Statistics*, 648-672.

In the study, Keane and Wolpin (1994) develop an approximate solution method which
consists of Monte Carlo integration with simulation and an interpolation approach to
Expand All @@ -94,9 +155,40 @@ Below are the example models that are currently available.
The model consists of four mutually exclusive alternatives that individuals can choose
in each period. Agents can either choose to work in one of two sectors *a* or *b*,
invest in *education* or stay *home*. The work alternatives award a wage and experience,
while school only awards experience. In the home option, individuals gain neither a wage
nor experience. The plot below shows the choice patterns for the three parametrizations.
The model considers a time horizon of 40 periods.
while school only awards experience.

Overview of model characteristics defined by ``params`` and ``options``:

.. csv-table::
:header: "Parameters", "kw_94_one", "kw_94_two", "kw_94_three"
:widths: 20, 20, 20, 20

"Number of choices", ,"4"
"Work choices", ," Cccupation sector a, Cccupation sector b"
"Education choices", , "education"
"Either choices", , "home",
"Number of parameters", , "30"
"Initial schooling", , "10 periods"
"Maximal schooling", , "20 periods",
"Shock Correlations", "None", "None", "Positive (a and b), negative (home and educ)"
"Lagged choices", ,"Education in period 1"
"Covariates", ,"Squared experience, break education, HS Degree"


.. csv-table::
:header: "Options", " "
:widths: 20, 40

"Number of periods", "40"
"Simulation agents", "1000"
"Solution draws", "500"
"Estimation draws", "200"
"Solution seed", "15"
"Simulation seed", "132"
"Estimation seed", "500"
"Estimation tau", "0.001"
"Monte Carlo Sequence", "random"


.. tab:: KW (1997)

Expand All @@ -107,11 +199,11 @@ Below are the example models that are currently available.
model parametrization that is very similar to the model of Keane and Wolpin (1994) and
and "extended" parametrization that improves on the empirical fit of the basic model.

- Keane, M. P., & Wolpin, K. I. (1997). The Career Decisions of Young Men.
*Journal of Political Economy*, 105(3), 473-522.
- Keane, M. P., & Wolpin, K. I. (1997). The Career Decisions of Young
Men. *Journal of Political Economy*, 105(3), 473-522.

**respy** supports both the basic and extended parametrization from the paper.
They are named:
They models are named:

- ``kw_97_basic``
- ``kw_97_extended``
Expand All @@ -127,19 +219,120 @@ Below are the example models that are currently available.
models and require a considerable amount of computation power to solve, especially the
extended model.

Overview of model characteristics defined by ``params`` and ``options``:

.. tabs::

.. tab:: kw_97_basic

.. csv-table::
:header: "Parameters", " "
:widths: 20, 20

"Number of choices", "5"
"Work hoices", "Blue collar, White collar, Military"
"Education choices", "School"
"Either choices", "Home"
"Number of parameters", "63"
"Initial schooling", "7-11 periods"
"Maximal schooling", "20 periods"
"Lagged choices", "None"
"Covariates", "School degrees, squared experience"
"Unobserved Heterogeneity", "4 types"

.. csv-table::
:header: "Options", " "
:widths: 20, 20

"Number of periods", "50"
"Simulation agents", "5000"
"Solution draws", "200"
"Estimation draws", "200"
"Solution seed", "456"
"Simulation seed", "132"
"Estimation seed", "500"
"Estimation tau", "500"
"Monte Carlo Sequence", "random"

.. tab:: kw_97_extended

.. csv-table::
:header: "Parameters", " "
:widths: 20, 20

"Number of choices", "5"
"Work choices", "Blue collar, White collar, Military"
"Education choices", "Education"
"Either choices", "Home"
"Number of parameters", "115"
"Initial schooling", "7-11 periods"
"Maximal schooling", "20 periods"
"Lagged choices", "School or Home in period 1"
"Covariates", "School degrees, squared experience, age, any or no prev. experience, military dropout, break in schooling"
"Unobserved Heterogeneity", "4 types"
"Measurement Error Wage", "Yes"

.. csv-table::
:header: "Options", "Value"
:widths: 20, 20

"Number of periods", "50"
"Simulation agents", "5000"
"Solution draws", "200"
"Estimation draws", "200"
"Solution seed", "1"
"Simulation seed", "2"
"Estimation seed", "3"
"Estimation tau", "500"
"Monte Carlo Sequence", "random"


.. tab:: KW (2000)

Another example model provided in the respy interface is the model presented in Keane
and Wolpin (2000). The model incorporates an observable characteristic to account for
race, aiming to analyze the effects of monetary incentive schemes designed to reduce
racial differences in school attainment and earnings.

- Keane, M. P., & Wolpin, K. I. (2000). Eliminating Race Differences in School
Attainment and Labor Market Success. *Journal of Labor Economics*, 18(4), 614-652.
- Keane, M. P., & Wolpin, K. I. (2000). Eliminating Race Differences in School Attainment
and Labor Market Success. *Journal of Labor Economics*, 18(4), 614-652.

The model is named

- ``kw_2000``

The model is very similar to the extended model specification in Keane and Wolpin
(1997).
(1997). Overview of model characteristics defined by ``params`` and ``options``:

.. csv-table::
:header: "Parameters", " "
:widths: 20, 20

"Number of choices", "5"
"Choices", "home, school, blue collar, white collar, military"
"Work choices", "blue collar, white collar, military"
"Education choices", "school"
"Either choices", "home"
"Number of parameters", "125"
"Initial education", "7-11 periods"
"Maximal Schooling", "20 periods"
"Correlations", "positive correlation for all work alternatives"
"Lagged choices", "School or Home in Period 1"
"Covariates", "School degrees, squared experience, age, any or no prev. experience, military dropout, break in schooling"
"Observables", "Ethnicity"
"Measurement Error Wage", "Yes"


.. csv-table::
:header: "Options", " "
:widths: 20, 20

"Number of periods", "50"
"Simulation agents", "5000"
"Solution draws", "500"
"Estimation draws", "200"
"Solution seed", "456"
"Simulation seed", "132"
"Estimation seed", "500"
"Estimation Tau", "500"

2 changes: 2 additions & 0 deletions docs/release_notes.rst
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Expand Up @@ -19,6 +19,8 @@ releases are available on `Anaconda.org
- :gh:`395` Adds guides and tutorials for exogenous proccesses, covariates, and maximum
likelihood estimation. Improves structure and appearance of documentation.
(:ghuser:`MaxBlesch`, :ghuser:`amageh`).
- :gh:`406` More information in example models guide (:ghuser:`carolinalvarez`,
:ghuser:`amageh`).


2.0.0 - 2019-2020
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