Autocorrelation Test Research Articles

Analysis of Spatial Effects of New Energy Vehicles Based on Semi-parametric Spatial Durbin Model

The global scientific and technological revolution and industrial change are developing rapidly, the integration of automotive technology with energy, transportation and information and communication fields is accelerating, and electrification, internet connectivity and intelligence have become the main trends. Therefore, studying the impact of digital development on the growth of new energy vehicle industry and consumer purchase intention is of great theoretical and practical significance for the digital transformation of automobile companies. In this paper, based on the provincial panel data of new energy vehicles in China from 2016 to 2023, we constructed the index system of digital economy and economic development, used the entropy method to calculate the level of development, and conducted spatial autocorrelation tests using global and local Moran indices. Considering the non-linear relationship between variables, a semi-parametric spatial Durbin model is established using local polynomial estimation to explore the impact of digital economy on new energy automobile industry. The results of the study show that the development of digital economy promotes the development of new energy automobile industry to a certain extent, but its development level may have a negative impact if it is too low or too high.

Pengaruh Likuiditas, Ukuran Perusahaan, Leverage dan Jaminan Terhadap Peringkat Obligasi pada Sektor Keuangan

This study aims to investigate the impact of Liquidity, Company Size, Leverage, and Collateral on Bond Ratings in the Financial Sector. The study centers on Real Estate and Property companies that are publicly traded on the Indonesia Stock Exchange (IDX) from 2017 to 2021, including a total of 28 enterprises. The sampling method used is nonprobability purposive sampling. The major data source for this study is the secondary data obtained from the financial reports of these firms, as published by the IDX. Data collection entails the systematic process of acquiring, recording, and reviewing secondary data using the documentation technique. Analytical approaches encompass descriptive analysis, classical assumption tests, tests for normalcy, multicollinearity, heteroskedasticity, and Autocorrelation. Additionally, hypothesis testing is conducted by t-tests, F-tests, and determination coefficient analysis. The research findings suggest that Liquidity, Company Size, Leverage, and Collateral exert a substantial impact on Bond Ratings within the Financial Sector.

Analyzing the Dynamics of Inflation, Exchange Rates and Economic Growth through the Gini Index: Modeling VAR in Morocco

In this study, we analyze the cross-impact of inflation, exchange rates and economic growth on income inequality in Morocco between 2000 and 2022, based on the GINI index as a measure of inequality. Its main objective is to understand how these macroeconomic variables influence income disparities. Our methodology is based on a VAR model to capture dynamic interactions between variables. To validate the robustness of the model, Granger causality tests and specification tests, including tests of homoscedasticity and autocorrelation of residuals, were used. The result is that inflation has a significant positive impact on income inequality, and exchange rate fluctuations directly influence inequality. Furthermore, economic growth helps to reduce inequality, although this effect depends on the distribution of the benefits of this growth. This study contributes to the existing literature by providing empirical evidence of the importance of macroeconomic stability and educational and fiscal policies in reducing income inequality.

Pengaruh Tenaga Kerja dan Investasi terhadap Pertumbuhan Ekonomi di Provinsi Jambi

The aims of the study were to determine: (1) the effect of labor on economic growth in Jambi Province; (2) the effect of investment on economic growth in Jambi Province, and (3)tThe effect of labor and investment on economic growth in Jambi Province. The type of data used in this study is published secondary data, namely in the form of time series for 14 years in Jambi Province from 2010-2023. The source of the data obtained is from the Central Bureau of Statistics (BPS) Jambi Province. This study uses multiple linier regression analysis. Data analysis prerequisite test used normality test, multicollinearity test, autocorrelation test and heteroscedasticity test. The hypothesis test used is the coefficient of determination R square, the F test and the t test. The results of this study indicate that partially: (1) labor has a significant positive effect on economic growth in Jambi Province; (2) investment has a significant positive effect on economic growth in Jambi Province; and (3) simultaneously labor and investment have a significant effect on economic growth in Jambi Province.

Reflections of Economic Shocks and Price of Crude Oil on Gold Price Volatility within Asian Nations

The focus of this study is to examine the effects of economic shocks along with the crude oil price on gold prices in Asian countries. This study utilizes a regression analysis to measure the relationship between the macroeconomic indicators, crude oil price, and gold price in six selected nations from 1990 until 2022. The validity of the data was diagnostically checked by performing a VIF procedure, autocorrelation test, and normality test on residual, and data were analyzed via EViews software. The analysis showed mixed findings, however, a major variable that positively affects the fluctuation of the gold price is “the crude oil price” and it reflects global economic stability in the six countries. A sudden rise in crude oil prices might trigger conflicts in oil-exporting countries and limit access to material sources. This situation may force investors to move their funds into a safe-haven commodity like gold. The fluctuations in crude oil prices are not only a response to current market conditions but also alarming a lot more fundamental problems in an economy, which include approaching economic recessions, currency devaluation and a major financial crisis. For practical implications, this study suggests that policymakers pay extra attention to this commodity besides other economic indicators like inflation, interest and currency rates, and a GDP rate to ensure a sustainable economic condition. To the best of the authors’ knowledge, this study is the first that examines the economic shocks along with crude oil prices pre-and-post COVID-19 pandemic focusing on a few Asian countries.

The Influence of Return on Asset, Debt to Total Asset Ratio, and Economic Value Added, on Company Value with Dividends as a Moderating Variable in Industrial Sector Companies

The purpose of this study is to determine the Effect of Return On Assets, Debt To Total Assets Ratio, and Economic Value Added on Company Value with Dividend Policy as a Moderating Variable in Industrial Sector Companies. The sampling technique in this study is purposive sampling. So that the number of samples obtained is 54 companies. The tests used are Normality Test, Multicollinearity Test, Heteroscedasticity Test, Autocorrelation Test, Interaction Test / MRA. The correlation coefficient value is 0.177, meaning that between ROA, DAR, EVA and Dividend Policy on company value, the relationship is low. The determination coefficient value (R²) or R square obtained is 0.031, indicating that 3.1% of the influence on company value is explained by the variables ROA, DAR, EVA and Dividend Policy. While the remaining 96.6% is influenced by other variables not examined in this study.

Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

We introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.

Uncovering true significant trends in global greening

The global greening trend, marked by significant increases in vegetation cover across ecoregions, has attracted widespread attention. However, even robust traditional methods, like the non-parametric Mann-Kendall test, often overlook crucial factors such as serial correlation, spatial autocorrelation, and multiple testing, particularly in spatially gridded data. This oversight can lead to inflated significance of detected spatiotemporal trends. To address these limitations, this research introduces the True Significant Trends (TST) workflow, which enhances the conventional approach by incorporating pre-whitening to control for serial correlation, Theil-Sen (TS) slope for robust trend estimation, the Contextual Mann-Kendall (CMK) test to account for spatial and cross-correlation, and the adaptive False Discovery Rate (FDR) correction. Using AVHRR NDVI data over 42 years (1982–2023), we found that conventional workflow identified up to 50.96% of the Earth's terrestrial land surface as experiencing statistically significant vegetation trends. In contrast, the TST workflow reduced this to 38.16%, effectively filtering out spurious trends and providing a more accurate assessment. Among these significant trends identified using the TST workflow, 76.07% indicated greening, while 23.93% indicated browning. Notably, considering areas (pixels) with NDVI values above 0.15, greening accounted for 85.43% of the significant trends, with browning making up the remaining 14.57%. These findings strongly validate the ongoing global greening of vegetation. They also suggest that incorporating more robust analytical methods, such as the True Significant Trends (TST) approach, could significantly improve the accuracy and reliability of spatiotemporal trend analyses.

The Effect of Political Connections, Independent Commissioners and Capital Intensity on Tax Aggressiveness with Transfer Pricing as a Moderating Variable (Empirical Study of Mining Companies Listed on the IDX for the 2018-2022 Period)

This study aims to examine the effect of political connections, independent commissioners, and capital intensity on tax aggressiveness with transfer pricing as a moderating variable. The population in this study is all mining companies listed on the Indonesia Stock Exchange (IDX) in the 2018-2022 period. The purposive sampling method is a sample selection technique used in this study with various specific criteria. The data analysis technique used is the classical assumption test consisting of the normality test, multicollinearity test, heteroscedasticity test and autocorrelation test. Meanwhile, hypothesis testing is carried out using multiple linear regression analysis, f test, coefficient of determination (R2) test and t test. The results of the data analysis show that political connections have a significant effect on tax aggressiveness with a significant value of 0.029 (p <0.05). Likewise, independent commissioners also have a significant effect on tax aggressiveness with a significant value of 0.034 (p <0.05). On the other hand, capital intensity does not have a significant effect on tax aggressiveness with a significant value of 0.333 (p > 0.05). Furthermore, political connections have a significant effect on tax aggressiveness with transfer pricing as a moderating variable, with a significant value of 0.027 (p < 0.05). Independent commissioners also show a significant effect on tax aggressiveness with transfer pricing as a moderating variable, with a significant value of 0.004 (p < 0.05). However, capital intensity does not have a significant impact on tax aggressiveness when moderated by transfer pricing, with a significant value of 0.480 (p > 0.05).

Comparative Analysis of the Equations Used in Modeling Individual Lactation Curves in Anatolian Water Buffalo (Bubalus bubalis)

In this study, the equations used in modeling the individual lactation curves of Anatolian Water Buffalo were comparatively examined and the most suitable model or models were tried to be determined. For this purpose, individuals with 8, 9 and 10 lactation records were selected among 8057 Anatolian Water Buffalo and a total of 1591 individual lactation curves were modeled. Guo-Salve, Grossman, Cappio-Borlino, Parabolic Exponential, Cobby and Le Du, Logarithmic Quadratic, Wilmink, Logarithmic Linear, Quadratic, Inverse Polynomial and Wood equations were used in the study. When comparing the models, coefficient of determination, mean squared error, Durbin-Watson autocorrelation test, the Akaike Information Criterion were considered. Among the models used in the study, the Cobby and Le Du, Wood and Logarithmic Quadratic models gave the best results, as opposed to the Guo-Salve model.

The Influence of Price and Promotion on Purchasing Decisions in the Shopee Marketplace

This study aims to determine and analyse: the effect of price on purchasing decisions; the effect of promotion on purchasing decisions; and the effect of price and promotion on purchasing decisions. This type of research is quantitative research with a causal associative approach. The study sample consisted of 70 respondents aged 20 – 25 in Makassar. Data collection techniques using questionnaires and documentation techniques. Data analysis techniques are carried out after data from all respondents or other data sources are collected with analytical activities including test prerequisite analysis and hypothesis testing (multiple linear regression). Through instrument and data quality tests (validity test and reliability test), classic assumption tests (normality test, multicollinearity test, heteroscedasticity test, autocorrelation test), multiple linear regression test, t-test, F test, coefficient of determination. The results of the study show that: price had a positive and significant effect on purchasing decisions in marketplace Shopee with a significance value is 0,000 > 0,05; promotion had a positive and significant effect on buying decisions in marketplace Shopee with a significance value is 0,008 > 0,05; and overall, price and promotion had a positive and significant effect on purchasing decisions in marketplace Shopee with significance value is 0,000 > 0,05..

Pengungkapan pada Hubungan Sustainability Report dan Intellectual Capital terhadap Nilai Perusahaan

This research related to company value aims to analyze the relationship between sustainability reports and intellectual capital in manufacturing companies in the industrial and consumption sectors listed on the Indonesia Stock Exchange (BEI) during the 2020-2022 period. The sampling technique was purposive sampling, the results of which were 27 companies that met the research criteria. Data analysis uses SPSS Statistics 26 with descriptive statistical tests, normality tests, multicollinearity tests, heteroscedasticity tests, autocorrelation tests, multiple linear regression analysis, simultaneous tests, partial tests, and coefficient of determination tests. The research results show that sustainability reports do not provide an optimal contribution to company value. However, intellectual capital, especially the employee capital aspect, is a consideration for optimizing its contribution to company value.

THE INFLUENCE OF PROFITABILITY, LEVERAGE, AND COMPANY SIZE ON TAX AVOIDANCE

This research aims to determine the effect of profitability, leverage and company size on tax avoidance. This study uses a causal quantitative research with descriptive statistical analysis. The sampling technique uses purposive sampling was canducted on property and real estate companies listed on Indonesia Stock Exchange in 2021-2022 with a sample of 54 companies. The analysis method was processed using SPSS with data analysis techniques wich include normality test, multicolinearity test, heteroscedasticity test, autocorrelation test, coefficient of determination test (R²), and partial influence test (t test). The research results show that profitability and leverage influence tax avoidance. Meanwhile, company size has no effect on tax avoidance

Kejenuhan Belajar dan Prokrastinasi Akademik terhadap Kemampuan Pemecahan Masalah Matematika

This study used multiple linear regression data analysis techniques to determine the effect of learning burnout and academic procrastination of students, both individually and together, on mathematical problem-solving abilities. The study was conducted on 88 students of class X of SMA Muhammadiyah 1 Palangka Raya, who were selected by simple random sampling. Data collection used a test for problem-solving abilities and a questionnaire for each learning burnout and academic procrastination. Before conducting multiple linear regression, prerequisite tests were carried out, namely: normality test, heteroscedasticity test, linearity test, autocorrelation test, and multicollinearity test. After meeting the prerequisite tests, the analysis was carried out and the results obtained were that there was an effect of learning burnout on students' mathematical problem-solving abilities, there was an effect of academic procrastination on students' mathematical problem-solving abilities, and there was an effect of learning burnout and academic procrastination together on the mathematical problem-solving abilities of class X students of SMA Muhammadiyah 1 Palangka Raya.

Pengaruh Keputusan Investasi, Keputusan Pendanaan dan Kebijakan Dividen Terhadap Nilai Perusahaan pada Perusahaan Sub Sektor Kesehatan yang Terdaftar di Bursa Efek Indonesia (BEI) Periode 2020-2022

This study aims to analyze the influence of Investment Decisions, Funding Decisions and Dividend Policies on Company Value in Health Sub-Sector Companies Listed on the Indonesia Stock Exchange for the 2020-2022 Period. This research is an associative quantitative research. Samples using purposive sampling techniques. The sample amounted to 11 companies from 31 health sub-sector companies listed on the Indonesia Stock Exchange for the 2020-2022 period. Multiple linear regression data analysis technique using SPSS 2022 for windows software. The data analysis used is normality test, heteroscedasticity test, autocorrelation test, multicolonicity test, multiple linear regression test, t test, f test and determination coefficient test. The results showed that Investment Decisions had an insignificant negative effect on company value, Funding Decisions had a positive insignificant effect on company value and Dividend Policy had a negative insignificant effect on company value.

Autocorrelation Spatial Based on Specific Nutritional Interventions Achievement with Stunting Cases in Toddlers at Kendari City Using Local Indicator of Spatial Autocorrelation (LISA) Method

Background:Stunting is a priority target both globally and in Indonesia. There are 10 provinces in Indonesia that are the main focus of the stunting reduction program, one of which is Southeast Sulawesi Province. Kendari City, located in Southeast Sulawesi, has experienced an increase in stunting incidence over the past three years. However, progress in reducing stunting in Kendari City has not been evenly distributed across its regions and sub-regions, with significant disparities in stunting rates between different sub-districts. Objective:To determine the spatial autocorrelation based on the achievement of specific nutritional interventions for toddlers and the incidence of stunting in Kendari City using the Local Indicator of Spatial Autocorrelation (LISA). Method:This quantitative study used the Local Indicator of Spatial Autocorrelation (LISA) method. The data on stunting incidence consisted of the number of stunting cases among toddlers in 2023 across 11 sub-districts in Kendari City. The sub-districts analyzed were Abeli, Baruga, Kadia, Kambu, Kendari, West Kendari, Mandonga, Nambo, Poasia, Puuwatu, and Wua-Wua. The study was conducted from November 2023 to May 2024 in Kendari City. A local autocorrelation test with LISA was performed to determine the spatial relationships among the sub-districts based on the research variables, with results displayed in the form of Moran's scatterplot, cluster map, and significance map. Results:The results of Moran's local bivariate test (LISA) indicated that the majority of sub-districts, particularly Kambu, exhibited significant positive autocorrelation with neighboring sub-districts and fell into the cold-spot category. This indicates that the number of specific nutritional intervention programs for toddlers and the cases of stunting in toddlers in 2023 were low in Kambu and its surrounding sub-districts, which also had similarly low values. Conclusion:There is spatial autocorrelation among the sub-districts in Kendari City. Although the cases of stunting in the Kambu sub-district are low, the achievement of intervention programs should remain optimal, as cases still exist in the area. Additionally, since Kambu has a spatial correlation with its neighboring areas, the government should target these areas for appropriate interventions to accelerate stunting reduction, particularly in Kendari City. Keywords:Spatial Autocorrelation; LISA; Specific Nutrition Interventions; Stunting Toddlers

Empirical likelihood ratio test for autocorrelation in least squares regression

Detecting autocorrelation in regression models is crucial for accurate estimation and testing procedures. Among the various methods available, the Durbin–Watson (D-W) test is commonly utilized. However, a notable limitation of the D-W test is its reliance on an indeterminate range due to the intractable distribution of the D-W statistic. Consequently, the test yields inconclusive results when the statistic falls within this range. This paper introduces a distribution-free Empirical Likelihood Ratio Test (ELRT) for detecting autocorrelation in regression models. A Monte Carlo simulation study is carried out to compare the performance of the proposed ELRT with the D-W test, ( a + b d U ) approximation and bootstrapped D-W test based on size and power. The results show that the proposed ELRT considerably outperforms the original D-W test and ( a + b d U ) approximation test irrespective of sample size, level of autocorrelation and number of regressors. The proposed test is competitor for bootstrapped D-W test in terms of maintaining the empirical size and power for large samples, n ≥ 100 . As a real-life application, the working of the proposed test is illustrated through air quality data of Delhi.

Pengaruh Growth Opportunities, Konservatisme Akuntansi dan Ukuran Perusahaan terhadap Earnings Response Coefficient

This research aims to analyze the influence of growth opportunities, accounting conservatism, and company size on the earnings response coefficient. The sample for this research is mining companies listed on the Indonesia Stock Exchange (BEI), totaling 18 companies during the 2018-2022 period. Data was taken from the company's financial reports using a purposive sampling method. The classical assumption tests used consist of Data Normality Test, Multicollinearity Test, Heteroscedasticity Test, and Autocorrelation Test. Meanwhile, the types of hypothesis testing used are the T Test (partial), F Test (simultaneous), and Determination Coefficient Test (R2) with multiple linear regression analysis. The research results show that growth opportunity and accounting conservatism have no effect on the earnings response coefficient, while company size has a negative effect on the earnings response coefficient.

Lightweight High‐Throughput TRNG Based on Single‐Node Boolean Chaotic Structure

ABSTRACTWith the rise of the Internet and electronic devices, the security of network information is gaining attention, and the true random number generator (TRNG) is playing an increasingly crucial role in information security. TRNG, based on Boolean chaotic entropy source, has drawn significant interest due to its uncomplicated circuit design and minimal hardware resource usage. However, most existing structures consist of two‐input or three‐input logic devices, forming a complex multinode, geometrically symmetric Boolean chaotic network using multiple logic devices. This network configuration results in increased complexity and reduced throughput. This study introduces an entropy source based on Boolean chaos utilizing single‐node and four‐input XOR gates, which can be easily placed and routed on Xilinx Artix‐7 FPGA. It requires only 29 LUTs and 5 DFFs without any postprocessing, achieving a throughput of up to 700 Mb/s. The output of TRNG has successfully passed various tests including the autocorrelation test, NIST SP800‐22, NIST SP800‐90B, AIS‐31, and TESTU01 tests with favorable results. Furthermore, by applying a three‐stage XOR chain postprocessing on Xilinx Spartan‐6 FPGA and Xilinx Virtex‐6 FPGA, it has passed the NIST SP800‐22 and NIST SP800‐90B tests at 300 Mb/s. The structure was also tested using Xilinx Virtex‐6 FPGA under different temperature and voltage conditions, passing the NIST SP800‐90B IID test.

Water and socioeconomic inequalities: spatial analysis of water consumption in Brazil

ABSTRACT To achieve equitable water access, it is necessary to understand and quantify the heterogeneity of water consumption. Thus, this study assessed the spatial dependence of water consumption for Brazil and an urban center. Two spatial autocorrelation tests were applied: the Global Moran’s Index (I) and the Local Indicator of Spatial Association (LISA). It was found that, both at the national and municipal scales, water consumption is not uniformly distributed in space, indicating the formation of clusters of regions with High-High, High-Low, Low-High, and Low-Low aggregation patterns. In this way, water and socioeconomic inequalities in Brazil are highlighted, emphasizing the gap between the Center-South and the North-Northeast regions. Additionally, water and socioeconomic disparities within the city of Fortaleza are also observed, resulting from its urbanization process. Finally, the spatial autocorrelation between water consumption and per capita income is strongly evident at both the national and municipal levels.