Lars Peter Hansen

The concept of 'prudent' decision-making under uncertainty is central to robust control.

We must acknowledge that economic agents are often operating with incomplete information and imperfect models.

The GMM estimator is consistent and asymptotically normal under relatively weak assumptions.

Robustness is about designing policies that perform well even if our understanding of the economy is imperfect.

The future is inherently uncertain, and our models should reflect that uncertainty.

The strength of GMM lies in its ability to exploit moment conditions derived from economic theory.

We need to be humble about our ability to perfectly model complex economic systems.

Robust control theory offers a framework for making decisions when the true model is unknown but lies within a known set of possibilities.

The goal of econometrics is not just to describe the past, but to provide tools for understanding and influencing the future.

Model misspecification is a pervasive problem in economics, and we need tools to address it.

The beauty of the GMM is its flexibility; it can be applied to a wide range of economic models.

Policymakers should consider the robustness of their policies to different economic scenarios.

The world is a messy place, and our models should reflect that messiness to some extent.

The generalized method of moments has revolutionized empirical work in many fields of economics.

Uncertainty aversion plays a significant role in how agents make decisions, especially in financial markets.

The challenge is to find a balance between model complexity and tractability.

Robust control is about making decisions that are resilient to our ignorance.

The GMM provides a way to test the overidentifying restrictions of a model, which is crucial for model validation.

Economic models are tools, not ultimate truths.

The concept of 'worst-case' scenarios is central to robust decision-making.