Skip to main content

Springer

Learning to Become Rational: The Case of Self-Referential Autoregressive and Non-Stationary Models

No reviews yet
Product Code: 9783540612797
ISBN13: 9783540612797
Condition: New
$61.47

Learning to Become Rational: The Case of Self-Referential Autoregressive and Non-Stationary Models

$61.47
 
1. 1 Rational Expectations and Learning to Become Rational A characteristic feature of dynamic economic models is that, if future states of the economy are uncertain, the expectations of agents mat- ter. Producers have to decide today which amount of a good they will produce not knowing what demand will be tomorrow. Consumers have to decide what they spend for consumption today not knowing what prices will prevail tomorrow. Adopting the neo-classical point of view that economic agents are 'rational' in the sense that they behave in their own best interest given their expectations about future states of the ecomomy it is usually assumed that agents are Bayesian deci- sion makers. But, as LUCAS points out, there remains an element of indeterminacy: Unfortunately, the general hypothesis that economic agents are Bayesian decision makers has, in many applications, lit- tle empirical content: without some way of infering what an agent's subjective view of the future is, this hypothesis is of no help in understanding his behavior. Even psychotic behavior can be (and today, is) understood as "rational", given a sufficiently abnormal view of relevant probabili- ties. To practice economics, we need some way (short of psychoanalysis, one hopes) of understanding which decision problem agents are solving. (LucAs (1977, p. 15)) 2 CHAPTER 1. INTRODUCTION 1. 1.


Author: Markus Zenner
Publisher: Springer
Publication Date: Jul 12, 1996
Number of Pages: 205 pages
Binding: Paperback or Softback
ISBN-10: 3540612793
ISBN-13: 9783540612797
 

Customer Reviews

This product hasn't received any reviews yet. Be the first to review this product!

Faster Shipping

Delivery in 3-8 days

Easy Returns

14 days returns

Discount upto 30%

Monthly discount on books

Outstanding Customer Service

Support 24 hours a day