Initial Beliefs Uncertainty and Information Weighting in the Estimation of Models with Adaptive Learning
Jaqueson Galimberti ()
No 2021-01, Working Papers from Auckland University of Technology, Department of Economics
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.
Keywords: expectations; adaptive learning; bounded rationality; macroeconomics (search for similar items in EconPapers)
JEL-codes: C32 C63 D83 D84 D90 E37 E70 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-mac and nep-ore
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Working Paper: Initial beliefs uncertainty (2021)
Working Paper: Information weighting under least squares adaptive learning (2020)
Working Paper: Information weighting under least squares learning (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:aut:wpaper:202101
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