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Learning about Consumption Dynamics
Author(s) -
JOHANNES MICHAEL,
LOCHSTOER LARS A.,
MOU YIQUN
Publication year - 2016
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/jofi.12246
Subject(s) - stylized fact , rational expectations , econometrics , consumption (sociology) , economics , volatility (finance) , bayesian probability , benchmark (surveying) , dividend , asset (computer security) , dynamics (music) , computer science , artificial intelligence , macroeconomics , finance , psychology , social science , computer security , geodesy , sociology , geography , pedagogy
This paper characterizes U.S. consumption dynamics from the perspective of a Bayesian agent who does not know the underlying model structure but learns over time from macroeconomic data. Realistic, high‐dimensional macroeconomic learning problems, which entail parameter, model, and state learning, generate substantially different subjective beliefs about consumption dynamics compared to the standard, full‐information rational expectations benchmark. Beliefs about long‐run dynamics are volatile, with counter‐cyclical conditional volatility, and drift over time. Embedding these beliefs in a standard asset pricing model significantly improves the model's ability to match the stylized facts, as well as the sample path of the market price‐dividend ratio.