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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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APPLICATION OF PANEL VECTOR AUTOREGRESSIVE (PVAR) MODEL ON THE ANALYSIS OF INFLATION AND GDRP RATE 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

PVAR is an extension of the VAR model applied to panel data, combining time series with cross-sectional data from various regions. This model enables all variables to be treated as endogenous and analyzed simultaneously. This study aims to examine the relationship between inflation and economic growth (GRDP) across Indonesian provinces using the Panel Vector Autoregressive (PVAR) model. The analysis includes stationarity testing (IPS test), optimal lag selection (MMSC), and parameter estimation using the Generalized Method of Moments (GMM). The validity of instruments is assessed through the Sargan-Hansen test, while causal relationships are analyzed using the Granger causality test. Results indicate a bidirectional relationship between inflation and economic growth in several provinces. The model is proven to be stable. Furthermore, the Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) analyses illustrate how shocks to one variable influence the other over time. These findings are expected to contribute to more effective formulation of regional economic policies.
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