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VARIANSI: Journal of Statistics and Its Application on Teaching and Research
Published by Universitas Negeri Makassar
ISSN : -     EISSN : 26847590     DOI : http://dx.doi.org/10.35580/variansiunm26374
Core Subject : Economy, Science, Education,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics
VARIANSI: Journal of Statistics and Its application on Teaching and Research memuat tulisan hasil penelitian dan kajian pustaka (reviews) dalam bidang ilmu dasar ataupun terapan dan pembelajaran dari bidang Statistika dan Aplikasinya dalam pembelajaran dan riset berupa hasil penelitian dan kajian pustaka.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 82 Documents
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Robust Panel Data Regression Model of Standard Error in Firm Size, Leverage, and Profitability on Firm Value (Case Study: The Indonesian Mining Sector, 2022–2024, Listed on the Indonesia Stock Exchange) Alfairus, Muh Qodri; Mubaraq, Muhammad Raihan; Amalia, Alia Rezki
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm515

Abstract

Corporate financial information such as firm size, leverage, and profitability sends signals to the market that are reflected in firm value. However, previous studies have yielded inconsistent results, likely due to differences in estimation methods and the disregard of violations of classical assumptions in panel data. This study aims to analyze the effects of firm size (Size), leverage (DER), and profitability (ROA) on firm value (PBV) by applying panel data regression with robust standard error correction. Data were collected from 21 mining sector companies listed on the Indonesia Stock Exchange (IDX) during the 2022–2024 period, yielding 63 observations. The model selected based on the Chow Test (p=1.46E-09) and the Hausman Test (p=0.002) is the Fixed Effects Model (FEM). The results of the classical assumption tests indicate violations of heteroscedasticity (p=0.029) and autocorrelation (p=0.005), so the estimation was continued using cluster-robust standard errors (clustering by time). After adjusting for the model, it was found that all three variables simultaneously had a significant effect on firm value (F-statistic, p = 0.0538). Partially, firm size had a significant negative effect (coefficient -0.481; p=0.038), leverage had a significant positive effect (coefficient 0.672; p=0.018), and profitability had a marginally significant negative effect (coefficient -0.796; p=0.092). An R-squared value of 17.6% indicates that there are still other factors outside the model that influence firm value. The conclusion of this study confirms that in the context of the Indonesian mining sector in the post-pandemic period, the market responds negatively to companies with large assets and high profitability, but responds positively to increased debt. These findings imply that investors should not focus solely on short-term profitability, and that company management should determine the optimal capital structure to increase firm value.
Trend, Cycle, and Forecasting Analysis of Monthly Inflation in Indonesia Using the Hodrick–Prescott Filter and ARIMA Ikhwana, Nur; Syalsabila, Annisa; Mangiri, Nalto Batty
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm526

Abstract

This study aims to analyze the structure of inflation and forecast monthly inflation in Indonesia using a time series approach. The method used is the Hodrick–Prescott Filter to decompose data into trend and cycle components, and the ARIMA model to forecast inflation. The data used is monthly inflation data for the period 2010–2025. The decomposition results show that inflation has a relatively stable long-term trend with short-term fluctuations reflecting the presence of economic shocks. Based on model identification, the best model is ARIMA(2,0,1)(1,0,1)[12] which is able to capture past influences, seasonal components, and short-term shocks. The evaluation results show that the model meets the white noise assumption and is suitable for use in forecasting. The forecasting results show that inflation tends to be stable with a moderate increasing tendency, although uncertainty increases over longer periods. This study shows that the combination of structural analysis and time series modeling provides a more comprehensive understanding of inflation dynamics and produces relevant predictions to support decision making.

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