Tag: Models

#ICYMI on the BGSE Data Science blog: Randomized Numerical Linear Algebra (RandNLA) For Least Squares: A Brief Introduction

Dimensionality reduction is a topic that has governed our (the 2017 BGSE Data Science cohort) last three months. At the heart of topics such as penalized likelihood estimation (Lasso, Ridge, Elastic Net, etc.), principal component analysis and best subset selection lies the fundamental trade-off between complexity, generalizability and computational feasibility.

David Rossell taught us that even if we have found a methodology to compare across models, there is still the problem of enumerating all models to be compared… read the full post by Robert Lange ’17 on Barcelona GSE Data Scientists

Talking Past Each Other

(Originally posted at EconPointOfView.com)

While deep methodological differences exist across economists, many disagreements involve “talking past each other.” Each side uses similar words to discuss fundamentally distinct, though related, concepts. This is especially a problem with every-day language words and leads to more confusion than understanding.

One problematic term is information. Everyone believes they have a reasonable definition and that others have the same concept in mind. This is unfortunate and stagnated the discussion. Only through clarity of thought and language can these issues be resolved.

Complete and Perfect Information, or Ignore for Now

Doing what was necessary for early models, the economists started easy. They ignored it. They approximated that every actor knows everything. That made life easy.

Since Marshall and Walras, economics focused on equilibria. Starting from perfect competition, complete and perfect information are crucial. How do supply and demand equilibrate? Everyone knows everything. After a few easy steps, boom, supply=demand.

While all economists admit perfect information is an untrue assumption, it is still the default in many models.

Information as Commodity Xn

Stigler believed that academics should understand that information is not free. Academics make their living by selling information. Continue reading “Talking Past Each Other”