scholarly journals On the asymptotic normality of kernel estimators of the long run covariance of functional time series

2016 ◽  
Vol 144 ◽  
pp. 150-175 ◽  
Author(s):  
István Berkes ◽  
Lajos Horváth ◽  
Gregory Rice
Keyword(s):  
Time Series ◽  
Asymptotic Normality ◽  
Kernel Estimators ◽  
Functional Time Series ◽  
Long Run

LEARNING DYNAMICS AND NONLINEAR MISSPECIFICATION IN AN ARTIFICIAL FINANCIAL MARKET

2009 ◽  
Vol 13 (5) ◽  
pp. 625-655 ◽  
Author(s):  
Christophre Georges ◽  
John C. Wallace
Keyword(s):  
Time Series ◽  
Financial Market ◽  
Consistency Condition ◽  
Nonlinear Structure ◽  
State Variable ◽  
Bounded Memory ◽  
Long Run ◽  
Finite Memory ◽  
Aggregate Dynamics ◽  
True Structure

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.

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