Biresponses Kernel Nonparametric Regression
Inflation and Economic Growth
Bibliographic Data
| ID | 12969848 |
|---|---|
| Authors | Suparti 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) |
| Year | 2020 |
| Volume | 10 |
| Pages | 465-471 |
| Publication date | 2020-12-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Criminology and Sociology (JOURNAL) |
| Journal identifiers | ISSN: 1929-4409 • E-ISSN: 1929-4409 |
| Publisher | Lifescience Global (PUBLISHER • CA) |
| DOI | 10.6000/1929-4409.2021.10.54 |
| OpenAlex | W3132451862 |
| Language | EN |
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
| Highly cited | No |
|---|