Recovering the actual trajectory of economic cycles

The paper deals with the development of a method for restoring the trajectory of economic cycles from estimates of the gross domestic product (GDP). The proposed approach to solve this problem is based on the interpretation of cycles in the form of random oscillations of the income with a certain natural frequency, also called a narrowband random process. The operators (Fourier transforms, filtering, etc.) used to recover the cycle trajectory are linear. Their inherent associativity property allows changing the sequence of implementation of the linear operators above. As a result, it is proposed to start the recovery with bandpass filtering of the GDP function, and after that to parry the influence of the inertia property of the GDP estimator. Taking the qualities of a narrowband random process into consideration made it possible to create a simplified procedure to recover the cycle trajectory. In the example of the Kuznets swing, the acceptability of this procedure is demonstrated for the practical econometrics. The developed method is applicable in problems that require knowledge of the trajectory of the considered cycle.

Ключевые слова

economic cycle, random oscillations, cycle trajectory, Fourier transforms, frequency response characteristics

Получено

15.03.2023

Дата публикации

30.06.2023

Кол-во символов

13096

Цитировать

ГОСТ	Кармалита В. А. Recovering the actual trajectory of economic cycles // Экономика и математические методы – 2023. – Tом 59. – № 2 C. 19-25 [Электронный ресурс]. URL: https://emm.jes.su/S042473880024867-2-1 (дата обращения: 25.04.2024). DOI: 10.31857/S042473880024867-2
MLA	Karmalita, Viacheslav A "Recovering the actual trajectory of economic cycles." Ekonomika i matematicheskie metody 59.2 (2023):19-25. DOI: 10.31857/S042473880024867-2
APA	Karmalita V. (2023). Recovering the actual trajectory of economic cycles. Ekonomika i matematicheskie metody 59(2), pp.19-25 DOI: 10.31857/S042473880024867-2

100 руб.

При оформлении подписки на статью или выпуск пользователь получает возможность скачать PDF, оценить публикацию и связаться с автором. Для оформления подписки требуется авторизация.

Оператором распространения коммерческих препринтов является ООО «Интеграция: ОН»

Всего подписок: 2, всего просмотров: 137

Оценка читателей: голосов 0

1. Павлейно М.А., Ромаданов В.М. (2007). Спектральные преобразования в MATLAB. Учеб-но-методическое пособие. Санкт-Петербург: Санкт-Петербургский государственный университет. 160 c.

2. Bolotin V.V. (1984). Random vibrations of elastic systems. Heidelberg: Springer. 468 p.

3. Brandt S. (2014). Data analysis: Statistical and computational methods for scientists and engi-neers. 4th ed. Cham, Switzerland: Springer. 523 p.

4. Karmalita V. (2020). Stochastic Dynamics of Economic Cycles. Berlin: De Gruyter. 106 p.

5. Karmalita V.A. (2022). Predicting the trajectory of economic cycles // Экономика и математи-ческие методы. Т. 58. № 2. С. 140–144.

6. Korotaev A.V., Tsirel S.V. (2010). Spectral analysis of world GDP dynamics: Kondratieff waves, Kuznets swings, juglar and kitchin cycles. In: Global economic development, and the 2008–2009 economic crisis. Structure and Dynamics, 4 (1), 3–57.

7. Schlichtharle D. (2011). Digital filters: Basics and design. 2nd ed. Berlin: Springer–Verlag. 527 p.

8. Cho S. (2018). Fourier transform and its applications using Microsoft EXCEL®. San Rafael: Mor-gan & Claypool. 123 p.

9. Tikhonov A.N., Arsenin V.Y. (1997). Solution of ill-posed problems. Washington: Winston & Sons. 258 p.