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- W2892381282 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. Among tests of joint hypotheses, superiority of an Anderson-Rubin inspired test in terms of local power is established. Monte Carlo evidence confirms the accuracy of the asymptotic theory in finite samples." @default.
- W2892381282 created "2018-09-27" @default.
- W2892381282 creator A5070755000 @default.
- W2892381282 date "2019-01-01" @default.
- W2892381282 modified "2023-09-24" @default.
- W2892381282 title "Estimation and Inference in Adaptive Learning Models with Slowly Decreasing Gains" @default.
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- W2892381282 doi "https://doi.org/10.2139/ssrn.3373985" @default.
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