Initial beliefs uncertainty

Jaqueson Galimberti

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: This paper evaluates how initial beliefs uncertainty can affect data weighting and the estimation of models with adaptive learning. One key finding is that misspecification of initial beliefs uncertainty, particularly with the common approach of artificially inflating initials uncertainty to accelerate convergence of estimates, generates time-varying profiles of weights given to past observations in what should otherwise follow a fixed profile of decaying weights. The effect of this misspecification, denoted as diffuse initials, is shown to distort the estimation and interpretation of learning in finite samples. Simulations of a forward-looking Phillips curve model indicate that (i) diffuse initials lead to downward biased estimates of expectations relevance in the determination of actual inflation, and (ii) these biases spill over to estimates of inflation responsiveness to output gaps. An empirical application with U.S. data shows the relevance of these effects for the determination of expectational stability over decadal subsamples of data. The use of diffuse initials is also found to lead to downward biased estimates of learning gains, both estimated from an aggregate representative model and estimated to match individual expectations from survey expectations data.

Keywords: expectations; adaptive learning; bounded rationality; macroeconomics (search for similar items in EconPapers)
JEL-codes: C32 C63 D83 D84 E37 E70 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2021-07
New Economics Papers: this item is included in nep-isf, nep-mac and nep-ore
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Citations: View citations in EconPapers (1)

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https://cama.crawford.anu.edu.au/sites/default/fil ... 2021_galimberti0.pdf (application/pdf)

Related works:
Journal Article: Initial Beliefs Uncertainty (2024)
Working Paper: Initial Beliefs Uncertainty and Information Weighting in the Estimation of Models with Adaptive Learning (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:een:camaaa:2021-68

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