The PI completed a paper that provides new identification results for skill formation models and investigates the effects of seemingly innocuous scale and location restrictions on parameters of interest. The results show, among other, that commonly used models are missspecified, and consequently, simply changing the units of measurements of observed variables might yield ineffective investment strategies and misleading policy recommendations. These serious misspecification issues can be illustrated using data sets from recently published studies. The paper also provides a new set of restrictions and policy relevant parameters that do not suffer from these issues.
The project team also worked on a new way to estimate skill formation models because popular existing estimators are generally not consistent and can therefore yield misleading conclusion, even once the misspecification issues are fixed. The new estimator uses an iterative approach to reduce the computational burden and leverages recent results from the numerical mathematics literature to achieve an efficient and accurate implementation.
Using the new estimation method, the team performed preliminary analyses of different data sets. For example, it started applying the new methodology to study the evolution and the determinants of mental and physical health. Just like skills, mental and physical health are not perfectly observed, but questionnaires can provide multiple measures of them. Moreover, they evolve over time, depend on treatments, and they might interact with each other. Hence, health formation also fits into the framework provided.
The team also made process on optimal estimation under shape restrictions, which are often reasonable assumptions in economic applications, as well as estimation in high-dimensional models. The newly proposed estimators perform well in preliminary Monte Carlo simulations.
The research has been presented at various invited seminars at renowned universities and international academic conferences.
