کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5106333 | 1481433 | 2017 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive models and heavy tails with an application to inflation forecasting
ترجمه فارسی عنوان
مدلهای سازگاری و دماهای سنگین با استفاده از پیش بینی تورم
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کلمات کلیدی
موضوعات مرتبط
علوم انسانی و اجتماعی
مدیریت، کسب و کار و حسابداری
کسب و کار و مدیریت بین المللی
چکیده انگلیسی
This paper introduces an adaptive algorithm for time-varying autoregressive models in the presence of heavy tails. The evolution of the parameters is determined by the score of the conditional distribution, with the resulting model being observation-driven and being estimated using classical methods. In particular, we consider time variation in both the coefficients and the volatility, emphasizing how the two interact with each other. Meaningful restrictions are imposed on the model parameters in order to achieve local stationarity and bounded mean values. The model is applied to the analysis of inflation dynamics with the following results: allowing for heavy tails leads to significant improvements in terms of both the fit and forecasts, and the adoption of the Student-t distribution proves to be crucial for obtaining well-calibrated density forecasts. These results are obtained using the US CPI inflation rate and are confirmed by other inflation indicators, as well as for the CPI inflation of the other G7 countries.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Forecasting - Volume 33, Issue 2, AprilâJune 2017, Pages 482-501
Journal: International Journal of Forecasting - Volume 33, Issue 2, AprilâJune 2017, Pages 482-501
نویسندگان
Davide Delle Monache, Ivan Petrella,