Essays on the monetary aspects of the term structure of nominal interest rates

Interest rates are key economic variables to much of finance and macroeconomics, and an enormous amount of work is found in both fields about the topic. Curiously, in spite of their common interest, finance and macro research on the topic have seldom interacted, using different approaches to address its main issues with almost no intersection. Concerned with interest rate contingent claims, finance term structure models relate interest rates to lagged interest rates; concerned with economic relations and macro dynamics, macro models regress a few interest rates on a wide variety of economic variables. If models are true though simplified descriptions of reality, the relevant factors should be captured by both the set of bond yields and that of economic variables. Each approach should be able to address the other field concerns with equal emciency, since the economic variables are revealed by the bond yields and these by the economic variables.

KittyCat: a cognitive model of structure-form discovery

Cognition is a core subject to understand how humans think and behave. In that sense, it is clear that Cognition is a great ally to Management, as the later deals with people and is very interested in how they behave, think, and make decisions. However, even though Cognition shows great promise as a field, there are still many topics to be explored and learned in this fairly new area. Kemp & Tenembaum (2008) tried to a model graph-structure problem in which, given a dataset, the best underlying structure and form would emerge from said dataset by using bayesian probabilistic inferences. This work is very interesting because it addresses a key cognition problem: learning. According to the authors, analogous insights and discoveries, understanding the relationships of elements and how they are organized, play a very important part in cognitive development. That is, this are very basic phenomena that allow learning. Human beings minds do not function as computer that uses bayesian probabilistic inferences. People seem to think differently. Thus, we present a cognitively inspired method, KittyCat, based on FARG computer models (like Copycat and Numbo), to solve the proposed problem of discovery the underlying structural-form of a dataset.