In the last several decades, it has been extensively studied how strategic behavior of economic agents could affect the outcomes of various institutions. Game theory and mechanism design theory play key roles in understanding economic agents' possible behavior in those institutions, its welfare consequences, and how we should design economic institutions to achieve desired social objectives even if the agents behave strategically for their own interests. However, existing studies mostly focus on somewhat narrow classes of economic environments by imposing restrictive assumptions. The proposed projects aim at providing novel theoretical frameworks which enable us to study agents' behavior and desirable institutions under much less assumptions. I believe that the projects have significant relevance in policy recommendation in practice and empirical studies, even though the proposed projects are primarily theoretical. In mechanism design, most papers in the literature focus on environments with independently distributed private information. We propose two novel (robustness-based) approaches to analyze mechanism design in correlated environments, motivated by their practical and empirical relevance. The robustness brought by my approach can be useful to mitigate certain types of misspecifications in mechanism design in practice. Moreover, the desirable robust mechanisms I obtain appear to be more sensible, and hence, can be useful for empirical studies of auction and other mechanism design problems. In game theory, it is often assumed that the game to be played is common knowledge, or even with uncertainty, uncertain variables are assumed to follow a common-knowledge prior .However, in many situations in reality, those do not seem to be satisfied. Our goal is to provide a novel theoretical framework to predict players' behavior in such incompletely specified games, and to identify conditions for (monotone) comparative statics. Both could be useful in empirical studies.

The objective of the present project is to analyze the channels of rent-seeking and its impact on economic development by combining original theoretical work, using the tools from industrial organization and contract theory, and systematic micro-level empirical evidence (the first of this type and scale), based on a unique database consisting of over 50,000 public procurement operations in Paraguay, covering the period 2004 to 2007 (i.e. panel over 4 years, with a view to extend it to more recent years). Its aim is to study the channels of rent seeking through procurement operations, providing strong micro-foundations for the country observed macroeconomic stylized facts, and helping understanding the mechanisms through which rent-seeking and corruption have, in the last 20 years, produced a dismal industrial sector, and a sluggish growth rate in this country. The project will also offer new insights on the 'natural resources curse'. Indeed, the rents generated by hydro-electric power free the government from the need to tax firms. Since it gets a steady flow of revenue from the dams, having a healthy industrial sector is no longer necessary, an effect different from standard macro-economic result such as the Dutch disease. Beyond the case of Paraguay, the project also aims at reaching conclusions relevant more generally for developing countries and at producing policy insights on the organization of procurement rules and government institutions to limit the impact of rent-seeking.

The IPANEMA project is devoted to the development of inference for non-, semi-parametric and high-dimensional structural economic models. It aims at producing reliable results that are based on inference on model that incorporate features of the unknown parameter/function that is coming from economic theory only. Non-parametric and semi-parametric inference for structural models usually gives rise to inverse problems. High-dimensional methods aim at computationally tractable model selection and inference in models with many more possible unknowns than observations when there is some parsimony or approximate parsimony. Parsimony can correspond to the fact that many coefficients are zero, that there are piecewise constant over time, the model is a discrete mixture of regression models, etc. Approximate parsimony corresponds to the case where the model can be well approximated by a parsimonious model. In that case, a high-dimensional method should select the best sub-model without knowing it in advance or using knowledge on the unknown. It should also account for the approximation error in the inference. Inverse problem techniques for non- and semi-parametric models have been introduced in structural econometrics since the beginning of the 2000’s. High-dimensional methods have been introduced in the econometrics in the last three years only. Though they already had a high impact in theoretical econometrics, we strongly believe that this is just the premises and that it will play an increasing role in empirical applications. The group that we form through the IPANEMA proposal consists of specialists of inverse problems and high-dimensional estimation, of economists and statisticians interested in economics. The objectives of our proposal are twofold. We will pursue the theoretical developments for inference based on non- and semi-parametric / high-dimensional structural economic models, as for instance: minimax theory, the development of data-driven methods for the choice of the regularization parameter that allow to achieve the minimax lower bounds, the construction of confidence sets -- possibly robust to identification -- and testing procedures, using bootstrap techniques or proper self-normalization, inference on functionals or in the presence of nuisance parameters (finite or infinite dimensional) - possibly under partial identification, and inference under shape restrictions. We will study structural models from various fields from economics including: evaluation of public policies, industrial organizations, labor economics, game theory, auctions and finance, among others. Importantly, we will consider both frequentist and Bayesian procedures. Interestingly, our group has strengths in both frequentist and Bayesian statistics. Bayesian procedures are appealing for designing data-driven methods and for incorporating economic restrictions. Incorporating prior knowledge can prove particularly useful when sample size is small. We also plan to go beyond cross-sections and propose methods for panel data or time series models. Handling dependence is very challenging and barely untouched for such models, even in the statistics literature. The second objective of our proposal is to make our techniques: (1) easy to implement and (2) accessible. To handle point (1) we will bear attention on algorithmic issues. This is for example the motivation for the recent advances in the high-dimensional literature in comparison to earlier model selection methods. Algorithmic issues have been absent of econometrics until very recently and become increasingly important to handle rich new data configurations. Our proposal addresses point (2) through: systematic development of purely data-driven methods that achieve optimal theoretical bounds, carrying empirical applications, developing computer programs – written either in Matlab or in R - and making them accessible to the general public.