Predicting the trajectory of economic cycles

This paper deals with the development of a method for predicting the trajectory of a pseudo-stationary fragment of the economic cycle. The latter is represented by discrete values (readouts) of random oscillations of the income function. The statistical equivalence of these readouts and second-order autoregression (Yule series) led to the adaptation of the autoregressive model to the specified fragment of the cycle. It is proposed to use the adapted autoregressive model as a tool for predicting cycle values via the method of statistical tests (Monte-Carlo) by forming the most probable cycle trajectory. The procedure for the formation of the cycle trajectory is described in detail and its parameters have formal justifications. The content of the subsequent statistical analysis of the simulation results is illustrated by the example of determining the instant of the predicted peak value of the cycle. The presented method is applicable in macroeconomic and econometric problems, the solution to which requires knowledge of the predicted trajectory of the cycle under consideration.

Keywords

economic cycle, random oscillations, Yule series, maximum likelihood estimates, pseudo-stationarity, cycle trajectory

Received

02.06.2022

Publication date

18.06.2022

Number of characters

11014

Cite

GOST	Karmalita V. Predicting the trajectory of economic cycles // Ekonomika i matematicheskie metody – 2022. – V. 58. – Issue 2 C. 92-96 [Electronic resource]. URL: https://emm.jes.su/S042473880020017-7-1 (circulation date: 05.10.2024). DOI: 10.31857/S042473880020017-7
MLA	Karmalita, Viacheslav A "Predicting the trajectory of economic cycles." Ekonomika i matematicheskie metody 58.2 (2022):92-96. DOI: 10.31857/S042473880020017-7
APA	Karmalita V. (2022). Predicting the trajectory of economic cycles. Ekonomika i matematicheskie metody 58(2), pp.92-96 DOI: 10.31857/S042473880020017-7

100 rub.

When subscribing to an article or issue, the user can download PDF, evaluate the publication or contact the author. Need to register.

Number of purchasers: 0, views: 364

Readers community rating: votes 0

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

2. Box G.E.P., Jenkins G.M., Reinsel G.C., Ljung G.M. (2015). Time series analysis: Forecasting and control. 5th ed. Hoboken, New Jersey: Willey. 712 p.

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

4. Karmalita V. (2020). Stochastic dynamics of economic cycles. Berlin: De Gruyter. 106 p.

5. Mazhdrakov M., Benov D., Valkanov N. (2018). The Monte Carlo method: Engineering applications. Cambridge: ACMO Academic Press. 250 p.

6. Zacks S. (1981). Parametric statistical inference: Basic theory and modern approaches. New York: Pergamon. 404 p.