Measurement and modelling of business cycles using linear and nonlinear methods

Bratčikovienė, Nomeda

Use this URL to cite this publication: https://hdl.handle.net/20.500.14911/118623

Measurement and modelling of business cycles using linear and nonlinear methods

Details

Publication Type (CRIS)

Straipsnis recenzuojamoje užsienio tarptautinės konferencijos medžiagoje / Article in peer-reviewed foreign international conference proceedings (P1d)

Publication Type (eLABa)

Straipsnis recenzuotame konferencijos darbų leidinyje / Article published in peer-reviewed conference proceedings (P1d)

VILNIUS TECH Research Priorities and Topics

Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes (FM0101)

Lithuanian Intelligent Specialization

Įtrauki ir kūrybinga visuomenė / Inclusive and creative society (L103)

Author(s)

Title

Measurement and modelling of business cycles using linear and nonlinear methods

[en]

Nomeda Bratčikovienė

Is part of

ITISE 2018. International conference on time series and forecasting, 19-21 September 2018, Granada (Spain) : proceedings of papers, Vol. 3 / editors and chairs: Olga Valenzuela, Fernando Rojas, Héctor Pomares, Ignacio Rojas

Published In

Year	Start Page	End Page
2018	1565	1573

Publisher

Granada, 2018

Extent

p. 1565-1573

URI

URI
https://hdl.handle.net/20.500.14911/118623

Cited references

Science / Art Area

Gamtos mokslai / Natural Sciences (N)

Field of Science / Art

Matematika / Mathematics (N001)

Keywords (en)

Abstract (en)

This paper focuses on business cycle component analysis and modelling in small economy countries. Lithuanian economy is one of such example with specific properties. The list of major leading indicators business cycles was studied in this paper in the case of Lithuania. The results suggest that economical methods aren’t suitable for modelling of main indicators business cycles in such cases. Standard univariate methods are preferable. However various time series business cycles may be nonlinear, therefore linear ARIMA and nonlinear SETAR methods were used and availability to capture features of the real time series business cycle with complex nature was tested.