Statistika: Statistics and Economy Journal - No. 3/2025

Statistika, 105(3): 291–305
https://doi.org/10.54694/stat.2024.71

Abstract
Scanner data from retailers like supermarkets, electronics stores, and online shops provides detailed transaction information at the barcode level (e.g., GTIN, EAN), allowing for the use of various price index formulas, including weighted ones. Due to high product turnover and seasonality, multilateral index methods are ideal, as they use a whole-time window and are transitive, avoiding chain drift. However, most commonly used multilateral indices (e.g., GEKS, CCDI, GK, TPD) fail the identity test, which requires the index to return to one when prices revert to their original levels. This paper proposes a new multilateral index inspired by GEKS but incorporating quality adjustments like the Geary-Khamis method. The index satisfies the identity test and other key axioms, demonstrating its robustness. Comparisons with the SPQ index and quality-adjusted indices (GEKS-AQU, GEKS-AQI) confirm its effectiveness, making it a highly useful tool for scanner data analysis in both theory and practice.

Keywords
Scanner data, multilateral indices, the GEKS-index

Statistika, 105(3): 306–317
https://doi.org/10.54694/stat.2024.66

Abstract
Rising rice demand in Indonesia, driven by population growth, causes price fluctuations that impact household spending. Accurate forecasting is crucial for price stability and government planning. This study employs the Generalized Space-Time Autoregressive (GSTAR) model with spatial weight variations to forecast rice prices across six provinces in Java. The results indicate that the GSTAR (71)I(1) model, utilizing radial distance weights (RDW), was identified as the optimal model. It satisfies the white noise assumption and achieves superior performance metrics, with a mean squared error (MSE) that is considerably lower than those obtained from other spatial weight models tested in this study. The mean absolute error (MAE) also demonstrates a strong accuracy, and the mean absolute percentage error (MAPE) is exceptionally small, suggesting minimal deviation from actual values when compared to other models. These values are notably lower compared to those of other spatial weight models tested in this study.

Keywords
GSTAR, Spatial Weighted Variance, rice price

Statistika, 105(3): 318–332
https://doi.org/10.54694/stat.2024.68

Abstract
This study examines how international oil and food prices affected domestic inflation in Algeria from 1994 to 2022. Using advanced time-series methods and spectral analysis, we provide updated evidence on how global price shocks influence Algeria's consumer prices. The results from vector autoregression (VAR) models with structural breaks show that Algeria’s inflation is strongly influenced by external supply-side factors, particularly food prices, while exchange rate pass-through and domestic demand pressures remain weak. Wavelet coherence analysis highlights how the relationship between global and domestic prices changes over time, with strong short-term connections but weaker long-term ones. Our findings reveal Algeria’s high exposure to imported inflation due to its dependence on oil and food imports, though recent institutional reforms have created more room for stabilization policies. This study fills gaps in the literature by exploring price transmission dynamics during major global shocks, including the COVID-19 pandemic, and offers guidance on managing inflation risks in a volatile global economy.

Keywords
Price transmission, inflation, international commodity prices, Vector Auto-regression (VAR) models

Statistika, 105(3): 333–351
https://doi.org/10.54694/stat.2024.47

Abstract
This study investigates the behaviour of agricultural productivity and its determinants using nonlinear Markovswitching regression. The objective is to investigate how agricultural productivity reacts to global factors and if the regression function varies due to threshold breaks. The study focuses on three agricultural sectors (crop, food, and livestock) from 1961 to 2021. The results are compared. The results show that the world uncertainty index has negative effects on the production of crops and food, but not on livestock. Besides, other global factors, namely GDP, inflation, energy, and non-energy commodity prices, have limited or no direct impact on agricultural production growth, but these factors may affect agricultural production indirectly. Furthermore, all agricultural production categories tend to grow at a decreasing rate. Additionally, agricultural production growth for all categories is expected to remain in a high production state with a higher probability. The findings might provide useful information to policymakers to improve the production and development of agricultural sectors.

Keywords
Agriculture, commodity price, inflation, macroeconomic factors, nonlinear Markov-switching regression, uncertainty

Statistika, 105(3): 352–365
https://doi.org/10.54694/stat.2024.59

Abstract
The manufacturing sector plays a crucial role in driving economic growth and productivity in a country by creating jobs, advancing technology, and significantly contributing to GDP. Its prowess not only bolsters domestic industries but also underpins international competitiveness, cementing its pivotal role in sustaining economic dynamism.The present study examines the role played by manufacturing sector in productivity dynamics of India by examining relevance of Kaldor's growth laws from 1981–82 to 2019–20, affirming their empirical validity. To achieve this objective, ARDL bounds testing approach has been used for estimation. The results of the study reveal that first and third law does hold for the country during the study period. The findings reveal a substantial and positive contribution of manufacturing growth to overall economic and productivity growth in India. Based on the findings of the study it is advised for policymaker need to prioritize manufacturing sector growth through incentives like, improved infrastructure and a favourable business environment.

Keywords
Manufacturing sector, productivity growth, ARDL, Kaldor’s Growth laws, India

Statistika, 105(3): 366–384
https://doi.org/10.54694/stat.2024.62

Abstract
While territorial conflicts in the South China Sea (SCS) between ASEAN member states and China are escalating, their economic dependency and geographical proximity suggest trade will continue. This study examines the impact of the SCS conflict on China-ASEAN trade. The generalized method of moments (GMM) was used to examine data collected between 2005 and 2022 from China and 10 Southeast Asian countries. The primary variable in this study, the SCS conflict, had no impact on ASEAN countries' trade with China. This is due to several reasons: China has a long history of trade with the region, trade is primarily driven by economic factors that outweigh the potential disruption of SCS conflicts, not all ASEAN members are claimants in the dispute, and a lack of integration in ASEAN internal trade. The findings also highlight that ASEAN’s GDP, China’s GDP, and exchange rate have the potential to boost ASEAN countries' trade with China. On the contrary, the distance between ASEAN countries and China and the average duty reduce total ASEAN countries-China trade.

Keywords
Territorial conflict, international trade, gravity model, GMM

Statistika, 105(3): 385–402
https://doi.org/10.54694/stat.2024.75

Abstract
This paper analyses recent data on AI technology use among EU enterprises, highlighting AI’s rapid advancement and its benefits in transport safety, manufacturing efficiency, sustainable energy, and decision-making. AI technologies – such as text mining, computer vision, speech recognition, natural language generation, machine learning, and deep learning – enable data-driven predictions, recommendations, and decisions with varying autonomy. AI systems can be software-based (e.g., virtual assistants) or device-embedded (e.g., autonomous robots, drones). Our findings reveal a significant increase in AI adoption rates between 2021 and 2023, driven by improvements in digital infrastructure, regulatory support, and sectoral advancements. However, barriers such as limited resources, skill shortages, and data security remain significant for SMEs. The study examines current AI adoption trends across EU countries and models key factors influencing AI adoption from 2021 to 2023 using panel data analysis.

Keywords
AI technologies, EU enterprises, panel modelling, statistical analysis, AI adoption

Statistika, 105(3): 403–419
https://doi.org/10.54694/stat.2024.80

Abstract
Dynamic predictions of large dimensional conditional covariance matrices are considered in the context of large financial portfolios. Since numerically simple prediction methods are usually recommended for multivariate conditional covariances (covolatilities), one prefers in this paper the multivariate EWMA (exponentially weighted moving average) processes extending the recursive estimation of EWMA processes to the multivariate case. Moreover, various modifications of recursive MEWMA projections are suggested to improve the quality of covolatility projections. An extensive numerical study for real stock indices portfolios compares types of covolatility projections employing various criteria and tests.

Keywords
Covolatilities projections, large covariance matrices, multivariate EWMA, multivariate GARCH models, recursive estimation