The Second Order Logistic Smooth Transition Autoregressive Model for Unemployment Rate of Latvia

2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2024) 2024
Oksana Pavļenko, Liāna Zeltiņa

To account for structural changes in data, the second order logistic smooth transition autoregressive model (LSTAR2) is obtained as the best smooth transition model using popular R software for the differences of the unemployment rate among 15-75 years old residents of Latvia. The specially adjusted R functions for estimation and forecasting of LSTAR2 based on the available in R package tsDyn functions for the first order logistic smooth transition autoregressive model (LSTAR1) are used. The constructed LSTAR2 model also is compared with the best chosen linear autoregressive, multiplicative seasonal autoregressive, self-exciting threshold and LSTAR1 models. LSTAR2 is superior than the compared models for these data, which indicates that the new R functions may be useful for economic data analysis.

Atslēgas vārdi
Time series | Autoregressive model | Threshold autoregression | Smooth transition autoregressive model
DOI
10.1109/ITMS64072.2024.10741945
Hipersaite
https://ieeexplore.ieee.org/abstract/document/10741945

Pavļenko, O., Zeltiņa, L. The Second Order Logistic Smooth Transition Autoregressive Model for Unemployment Rate of Latvia. No: 2024 IEEE 65th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS 2024), Latvija, Rīga, 3.-4. oktobris, 2024. Piscataway, NJ: IEEE, 2024, 41.-45. lpp. ISBN 979-8-3315-3384-7. e-ISBN 979-8-3315-3383-0. ISSN 2771-6953. e-ISSN 2771-6937. Pieejams: doi:10.1109/ITMS64072.2024.10741945

Publikācijas valoda
English (en)