Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/242588 
Year of Publication: 
2021
Series/Report no.: 
Economics Working Paper Series No. 2021/01
Publisher: 
Auckland University of Technology (AUT), Faculty of Business, Economics and Law, Auckland
Abstract: 
This paper evaluates how the way agents weight information when forming expectations can affect the econometric estimation of models with adaptive learning. One key new finding is that misspecification of the uncertainty about initial beliefs under constantgain least squares learning can generate a time-varying profile of weights given to past observations, distorting the estimation and behavioural interpretation of this mechanism in small samples of data. This result is derived under a new representation of the learning algorithm that penalizes the effects of misspecification of the learning initials. Simulations of a forward-looking Phillips curve model with learning indicate that (i) misspecification of initials uncertainty can lead to substantial biases to estimates of expectations relevance for inflation, and (ii) that these biases can spill over to estimates of inflation rates responsiveness to output gaps. An empirical application with U.S. data shows the relevance of these effects.
Subjects: 
expectations
adaptive learning
bounded rationality
macroeconomics
JEL: 
E70
D83
D84
D90
E37
C32
C63
Document Type: 
Working Paper

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