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Biresponses Kernel Nonparametric Regression

Inflation and Economic Growth

Bibliographic Data

ID12969848
AuthorsSuparti Suparti (Diponegoro University, corresponding author), Budi Warsito (0000-0003-1948-4511, Diponegoro University), Rukun Santoso (0000-0003-4998-4437, Diponegoro University), Hasbi Yasin (0000-0002-4887-9646, Diponegoro University), Rezzy Eko Caraka (0000-0002-1812-7478, Seoul National University), Sudargo Sudargo (Universitas PGRI Semarang)
Year2020
Volume10
Pages465-471
Publication date2020-12-31
Open AccessYes
TypeARTICLE
VenueInternational Journal of Criminology and Sociology (JOURNAL)
Journal identifiersISSN: 1929-4409 • E-ISSN: 1929-4409
PublisherLifescience Global (PUBLISHER • CA)
DOI10.6000/1929-4409.2021.10.54
OpenAlexW3132451862
LanguageEN

The relation between inflation and economic growth is interesting to observe. To maintain the inflation rate, two factors should be taken into account, namely keeping the economic pulse at its optimal rate and keeping people's purchasing power from decreasing. Many factors influence the inflation and economic growth of a nation; one of which is the national bank interest rate. Since the data of inflation are closely related to economic growth, this study aims at modelling the data of inflation rate and economic growth of Central Java Province in Indonesia using bi-response kernel regression. Employing the data from the first trimester of 2007 up to those from the second trimester of 2019 which were processed using kernel Gauss, the best model to minimise the value of GCV was obtained with optimum h for inflation model amounting to 0.12 and 81.75 for economic growth model. The model performance was excellent because the MAPE out sample was less than 10%. The biresponses kernel model is better than the linear biresponses model in terms of GCV, MSE, R2, and MAPE values

Econometrics · Economics · Inflation (cosmology · Kernel (algebra · Statistics · Data Mining and Machine Learning Applications · Economic Growth and Fiscal Policies · Mathematics

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