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Learning with data (quasi-) differencing
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TOPIC:
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Learning with data (quasi-) differencing
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ABSTRACT
The paper studies the stability of Rational Expectations Equilibrium (REE) under adaptive learning assuming that agents do not know the econometric specification of the REE and are alert to a potential model misspecification, i.e., serially correlated residuals. Their forecasting model may be under-parameterized with omitting some regressors or correctly specified by coincidence. They recursively apply Feasible Generalized Least Squares (FGLS) estimators to address potential misspecifications. In a general class of models, the condition governing the convergence of FGLS learning of under-parameterized (or correctly specified) models to REE is shown to be no stronger than (or identical to) the usual E-stability condition. The stability results are applied to evaluate alternative monetary policies in New Keynesian models allowing for agents' lack of knowledge of the correct specification.
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PRESENTER
Pei Kuang
University of Birmingham
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RESEARCH FIELDS
macroeconomics,
monetary economics,
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DATE:
28 Oct 2016 (Friday)
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TIME:
4pm - 5.30pm
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VENUE:
Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903
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