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• W3124192210 abstract "This paper proposes a novel and flexible framework to estimate autoregressive models with time-varying parameters. Our setup nests various adaptive algorithms that are commonly used in the macroeconometric literature, such as learning-expectations and forgetting-factor algorithms. These are generalized along several directions: specifically, we allow for both Student-t distributed innovations as well as time-varying volatility. Meaningful restrictions are imposed to the model parameters, so as to attain local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics. Allowing for heavy-tails leads to a significant improvement in terms of fit and forecast. Moreover, it proves to be crucial in order to obtain well-calibrated density forecasts." @default.
• W3124192210 created "2021-02-01" @default.
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• W3124192210 date "2016-01-01" @default.
• W3124192210 modified "2023-09-23" @default.
• W3124192210 title "Adaptive Models and Heavy Tails" @default.
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• W3124192210 doi "https://doi.org/10.2139/ssrn.2777989" @default.
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