We aim for improvements to respy in three domains: Economics, Software Engineering, and Numerical Methods.

## Economics and Statistics¶

### Support Keane and Wolpin (1997)¶

We want to support the full model of Keane and Wolpin (1997). This is almost completed. We just need to implement a fifth choice (the military sector).

### Explore Simulation Based Estimation¶

We want to add simulation based estimation to respy and compare the accuracy of parameters estimated with maximum likelihood and simulation based methods. As respy already has the ability to simulate data, it would be very simple to implement method of simulated moments or indirect inference estimation.

### Benchmark Different Optimizers¶

Explore the speed and reliability of local and global optimizers for maximum likelihood estimation of the model. The results should be transferable to other estimation problems with a noisy criterion function.

### Improve the Likelihood Estimation¶

We suspect that the generation of multivariate normal shocks in KW94 is flawed. We want to derive a numerically robust method for correct sampling from a multivariate normal distribution. Compare the precision of estimates with the old and new methods on simulated samples. This is especially interesting for students who want to deeply understand estimation of structural models with maximum likelihood.

### Estimate a model by CCP¶

Implement estimation by CCP and outline the trade-offs and discuss the validity of estimation. This could be combined with having a dataset simulated that does not conform to the CCP model and the respy model.

## Software Engineering¶

• research the hypothesis package to replace the hand-crafted property-based testing routines

## Numerical Methods¶

• link the package to optimization toolkits such as TAO or HOPSPACK

• implement additional integration strategies following Skrainka and Judd (2011)