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All Journal Jurnal Matematika Sains dan Teknologi RAGAM: Journal of Statistics and Its Application
Nusyirwan Nusyirwan
University of Lampung
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health Other

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AN APPLICATION OF THE PANEL VECTOR AUTOREGRESSIVE MODEL TO ANALYZE INFLATION AND GRDP GROWTH RATES Khairunnisa Khairunnisa; Khoirin Nisa; Misgiyati Misgiyati; Nusyirwan Nusyirwan
RAGAM: Journal of Statistics & Its Application Vol 5, No 1 (2026): RAGAM: Journal of Statistics & Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v5i1.17067

Abstract

The Panel Vector Autoregressive (PVAR) model is an extension of the traditional Vector Autoregressive (VAR) framework that is specifically designed for panel data, allowing the integration of time-series and cross-sectional information across multiple regions. One of the main advantages of the PVAR approach is its ability to treat all variables as endogenous and to capture dynamic interdependencies simultaneously. This study aims to examine the dynamic relationship between inflation and regional economic growth, as measured by Gross Regional Domestic Product (GRDP) growth, across provinces in Indonesia using a PVAR model. The empirical analysis begins with panel unit root testing employing the Im–Pesaran–Shin (IPS) test to ensure stationarity of the variables, followed by optimal lag length determination based on the Moment and Model Selection Criteria (MMSC). Model estimation is conducted using the Generalized Method of Moments (GMM) to address endogeneity and unobserved heterogeneity. The validity of the instruments is evaluated through the Sargan–Hansen test, while causal linkages between variables are investigated using the Granger causality test. The results reveal evidence of a bidirectional relationship between inflation and economic growth in several provinces, indicating dynamic feedback effects. Stability tests confirm that the estimated PVAR model is stable. Furthermore, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses illustrate how shocks to inflation and economic growth propagate over time. These findings provide valuable insights for designing more effective and regionally targeted economic policies in Indonesia.
Modeling Air Quality Index in Indonesia Using Smoothing Splines and Truncated Splines Regression Nadhia Az Zahra; Khoirin Nisa; Misgiyati Misgiyati; Nusyirwan Nusyirwan
Jurnal Matematika Sains dan Teknologi Vol. 26 No. 2 (2025)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v26i2.14574.2025

Abstract

The Air Quality Index (AQI) is a composite indicator that reflects regional air quality conditions and is influenced by multiple determinants with complex and nonlinear relationships. In such circumstances, parametric regression may be restrictive because it requires a predetermined functional form. This study applies spline based nonparametric regression using smoothing splines and truncated splines to model AQI in Indonesia and to compare the performance of both approaches. AQI is treated as the response variable, while population density, land cover area within and outside forest areas, and the number of motor vehicles are considered as predictor variables. For smoothing splines, the optimal smoothing parameter is selected using Generalized Cross Validation, whereas truncated splines are estimated using Ordinary Least Squares under various knot configurations and selected based on the minimum Generalized Cross Validation value. Model performance is evaluated using Generalized Cross Validation, Mean Squared Error, and Adjusted R squared. The study aims to identify the most appropriate model and to determine key factors influencing AQI variation in Indonesia, thereby providing empirical support for environmental policy making. The results show that the smoothing spline model provides better performance than the truncated spline model, with a lower Mean Squared Error (MSE) of 0.0716 and a higher Adjusted R² of 0.794. These results indicate that smoothing splines are more effective in capturing the nonlinear relationships influencing AQI variation in Indonesia.
Co-Authors Khairunnisa Khairunnisa Khoirin Nisa Khoirin Nisa Misgiyati Misgiyati Misgiyati Misgiyati Nadhia Az Zahra