What Information Theory Brings to Modelling and Inference with Complex Data

About this event

The available information is usually too complex, insufficient and imperfect to deliver a unique solution for most economic modelling and inference problems. Information Theory within a constrained optimization setup provides a way to deal with complexity under such deep uncertainty. It provides us with a way to sort and rank solutions and then choose the one that satisfies our desired properties. As such, it provides us with a different way of thinking about solving complex problems and a way to nest models in terms of the information and decision criteria they use. It also provides new insights into economic modelling and allows us to solve inference problems that cannot be solved with conventional methods without imposing additional structure or assumptions. Though Information-Theoretic inference provides us with a general framework for inference (I call it, info-metrics), the exact specification is problem-specific. In this talk I will discuss the basic idea of info-metrics via a number of graphical representations of the theory and will then provide a number of examples. I will also discuss the way some other traditional approaches fit within that framework (such as partial identification and misspecification) and some of the benefits of combining classical and information-theoretic econometric modelling.