How to Load Example Models ========================== In the tutorials and guides of the **respy** documentation, you will frequently encounter *example models*. These are pre-defined models that can be easily accessed with just one function call to facilitate an introductory workflow and use in explanatory material. The set of examples consists of simple as well as very advanced models to cover a wide range of use cases and challenges that come with an increasing degree of complexity in dynamic discrete choice models. For instance, in the tutorials, you will encounter two very simple models that are based on the story of Robinson Crusoe. Other example models are based on actual economic publications and thus serve to illustrate the scalability of `respy` models. You can access example models by typing .. code-block:: python params, options, data = rp.get_example_model("model_name") If you only want to get the `params` and `options`, set the argument `with_data` to **False**. .. code-block:: python params, options = rp.get_example_model("model_name", with_data=False) .. raw:: html
To API

For more information, checkout the function in theAPI

Below are the example models that are currently available. ----- .. tabs:: .. tab:: Toy Models The package provides users with two toy models. These models can be used to acquaintance oneself with **respy**'s functionalities and can be encountered in the tutorials. .. raw:: html
To Tutorials

Check out the Tutorials

The models are called: - ``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. 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 ease the computational burden of solving the DCDP model. They utilize one model with three different parametrizations to assess their solution method. This model and its three parametrizations are used as example models in the **respy** interface. They are called: - ``kw_94_one`` - ``kw_94_two`` - ``kw_94_three`` 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. 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) A more advanced group of examples are given by the models developed by Keane and Wolpin (1997). In this study, the authors implement an empirical structural life-cycle model of occupational choice and human capital investment. They estimate their model on data from the National Longitudinal Survey of Youth (NLSY). The study includes a "basic" 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. **respy** supports both the basic and extended parametrization from the paper. They models are named: - ``kw_97_basic`` - ``kw_97_extended`` However, the parametrization from the paper returns different life-cycle patterns for **respy** than presented in the paper. You can thus also access our estimates based for the models that are based on the same empirical data by adding ``_respy`` to the model name. The models consist of three occupational choices (*white collar*, *blue collar*, and *military*), one educational choice (*school*), and a *home* option. Both models consider a life-cycle of 50 periods. These models are decidedly larger than the toy 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. The model is named - ``kw_2000`` The model is very similar to the extended model specification in Keane and Wolpin (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"