دانلود رایگان مقاله انگلیسی تورم هسته ای ماشین یادگیری - الزویر 2018

عنوان فارسی
تورم هسته ای ماشین یادگیری
عنوان انگلیسی
Machine learning core inflation
صفحات مقاله فارسی
0
صفحات مقاله انگلیسی
11
سال انتشار
2018
نشریه
الزویر - Elsevier
فرمت مقاله انگلیسی
PDF
نوع مقاله
ISI
نوع نگارش
Short communication
رفرنس
دارد
پایگاه
اسکوپوس
کد محصول
E10206
رشته های مرتبط با این مقاله
مهندسی کامپیوتر
گرایش های مرتبط با این مقاله
هوش مصنوعی
مجله
اسناد اقتصادی - Economics Letters
دانشگاه
Banco de México - Mexico
کلمات کلیدی
یادگیری ماشین، الگوریتم k-means، تورم هسته ای.
doi یا شناسه دیجیتال
https://doi.org/10.1016/j.econlet.2018.05.001
چکیده

Abstract


In this article a novel methodology for building core inflation measures is proposed based on the k-means clustering machine learning algorithm. This new methodology is explored using Mexican CPI data in the spirit of getting a clear signal and having good predictions of the inflationary process based on selecting items with low volatility and assigning them to clusters. The results show that the core inflation built captures better the inflation signal and also outperforms the short-term inflation forecasts obtained by the trimmed means method and the core inflation excluding food and energy.

نتیجه گیری

Conclusion


This study explores the k-means machine learning algorithm for building core inflations measures. As an empirical example, a core inflation measure, the k-vol core, is built based on reducing volatility. This indicator is evaluated on two criteria: its ability to grasp the inflation signal and its capacity to forecast future inflation. The results show that the k-vol core outperforms trimmed mean indicators and core inflation excluding food and energy. The study aims to contribute to the inflation literature and to provide a useful tool to policy makers when taking monetary policy decisions.


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