Mayer, Alexander . ESTIMATION AND INFERENCE IN ADAPTIVE LEARNING MODELS WITH SLOWLY DECREASING GAINS. J. Time Ser. Anal.. HOBOKEN: WILEY. ISSN 1467-9892

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Abstract

An asymptotic theory for estimation and inference in adaptive learning models with strong mixing regressors and martingale difference innovations is developed. The maintained polynomial gain specification provides a unified framework which permits slow convergence of agents' beliefs and contains recursive least squares as a prominent special case. Reminiscent of the classical literature on co-integration, an asymptotic equivalence between two approaches to estimation of long-run equilibrium and short-run dynamics is established. Notwithstanding potential threats to inference arising from non-standard convergence rates and a singular variance-covariance matrix, hypotheses about single. as well as joint restrictions remain testable. Monte Carlo evidence confirms the accuracy of the asymptotic theory in finite samples.

Item Type: Journal Article
Creators:
CreatorsEmailORCIDORCID Put Code
Mayer, AlexanderUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
URN: urn:nbn:de:hbz:38-565984
DOI: 10.1111/jtsa.12636
Journal or Publication Title: J. Time Ser. Anal.
Publisher: WILEY
Place of Publication: HOBOKEN
ISSN: 1467-9892
Language: English
Faculty: Unspecified
Divisions: Unspecified
Subjects: no entry
Uncontrolled Keywords:
KeywordsLanguage
STOCHASTIC-APPROXIMATION; RATIONAL-EXPECTATIONS; MAXIMAL INEQUALITY; REGRESSION-MODELS; CONVERGENCE; GMM; VARIABLESMultiple languages
Mathematics, Interdisciplinary Applications; Statistics & ProbabilityMultiple languages
URI: http://kups.ub.uni-koeln.de/id/eprint/56598

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