Serial Autocorrelation Research Articles

Ownership Structure and Firm Performance. A Panel Regression Study from Poland

This paper explores the relationship between ownership structure and firm performance, using the framework of agency theory. Given a sample of 126 Polish non-financial firms listed on the Warsaw Stock Exchange between 2016-2021, we applied fixed and random effects panel regressions with robust standard errors, tested for specification, endogeneity, cross-sectional heteroskedasticity, and serial autocorrelation. Our models identify several significant associations of adopted ownership structures with firm performance measured by accounting and market-based measures. We find that ownership concentration by the largest shareholder is negatively related to enterprise market-measured performance. Additionally, our results indicate the significance of shareholder heterogeneity.

Sustainable Finance and Economical Profitability in Africa

African continent is facing a considerable demonstration of financial institutions and especially bank group which are dominating the banking environment. In the absence of developed financial markets, these bank organisations are at the centre of the economic activity. They are even the main financing source for companies, States, households and represent about 80% of African continent assets. This situation is encouraged by the organisational structure of those banks (subsidiary companies, representative offices, affiliated banks and branches) which permit a better representatively at the level of the continent. This research analyses the influence of sustainable finance principles on the economical profitability of 42 banking groups implanted in African continent between 2010 and 2020. Thus, data used are extracted from annual reports and were analysed through a time cross-sectional regression corrected for any latent heteroscedasticity and serial autocorrelation. At the end, the findings of this research are plural. firstly, green credits and transparency have a non-considerable impact on economical profitability. secondly, employee shareholding and gender diversity have a negative influence on economical profitability. thirdly, corporate environmental responsibility is negatively and highly correlated to bank economic profitability. Finally, philanthropy positively and highly affects economical profitability. One can therefore conclude that the profitability of sustainable finance is mitigated in Africa.

Green Finance and Sustainable Development Nexus in Sub-Saharan Africa

This study comprehensively assesses the influence of green finance on sustainabledevelopment in sub-Saharan African countries from 1999-2023. The sub-Saharan Africancountries are considered most vulnerable to climate change due to their individual countries’ geographical location, economic structure, population density, limited adaptive capacitytoclimate change, and social vulnerability. By examining the intricate linkages between greenfinance measured by financial development indicators, and institutional frameworks, technology, urbanization, and educational levels, the study aims to address environmental, social, and economic challenges and the trajectory of green development in sub-SaharanAfrican countries. The pool mean group autoregressive distributed lags (PMG/ARDL) method was employed for its ability to rheostat endogeneity and serial autocorrelation, neglected by previous studies. The findings underscore the pivotal role of green finance, proxied by bank credit to the private sector, in promoting sustainable practices, throughtechnological advancements and educational levels to increase investment in industries that prioritize sustainability, conservation, and biodiversity preservation. The negative nexusbetween foreign direct investment and the potential adverse consequences is associatedwiththe influx of multinational corporations to sub-Saharan African countries, particularly duetolax environmental regulations linked to weak regulatory frameworks. In light of thesefindings, this study recommends aligning investments with sustainable development goals, enhancing regulatory oversight to improve environmental quality, and balancing economicgrowth and environmental stewardship through sustainable development strategies, giventheir countries' vulnerability to climate change.

Does Institutional Setting Contribute to the Public Debt — Income Inequality Relationship in Developing Economies?

In the context of increasing globalization, income inequality is one severe problem in several countries because it widens the income gap between the rich and the poor, which leads to social instability. Narrowing this gap has become one of the main agendas in many developing countries to satisfy the millennium goals proposed by the United Nations. Meanwhile, government expenditure is one crucial fiscal instrument as it contributes significantly to running the economy and overcoming economic cyclicality. In particular, governance/institutional can positively adjust the public debt — income inequality relationship in developing economies. The purpose of the study to identify the impact of institutional quality, public debt and their interaction on income inequality on a balanced data panel of 34 developing economies for the period 2002–2020. For monitoring endogenous problems and serial autocorrelation in empirical equations, two-step and one-step system GMM (Generalized Method of Moments) assessments are used. The results from the study show that public debt and the quality of institutions increase income inequality, but their interaction narrows it. These results seem to be counter-intuitive. Besides, education enhances income inequality in these economies. The results of the study provide some policy recommendations for reducing the inequalities in society through public debt and the quality of institutions in developing economies. Accordingly, governments in developing economies should use spending financed by public debt to support low-income individuals through social transfers throughout economic development. Importantly, they should spend more on education and health to help the poor improve their skills and knowledge, narrowing the income difference between the rich and the poor. In particular, they should be prudent in controlling and managing public debt to avoid a public debt crisis and social instability.

Machine-learning-based analysis of biomedical time-series data: the monitoring and prediction of disease progression

Abstract This study examines the spatial and temporal patterns of influenza and malaria incidence using an ARMA-BP combination model. The approach employs the dynamic series method to identify epidemic patterns of these diseases while assessing serial autocorrelation coefficients, performing randomness tests, and establishing a forecasting model. Additionally, it evaluates the impact of seasonal and meteorological factors on the epidemiology of influenza and malaria to ascertain the model’s efficacy in predicting incidence rates and trends. The findings indicate that the peak period for influenza incidence typically occurs during the transition from winter to spring, specifically between weeks 2 and 14. The correlation coefficients between temperature variables and malaria incidence generally ranged from 0.7 to 0.9. The ARMA-BP model demonstrated robust short-term predictive capabilities for influenza, showing a high degree of concordance in predictions for 2021 and 2022, though it performed less satisfactorily for 2023. For malaria, the predicted and actual incidence trends were largely consistent, with prediction errors consistently below 0.01. Consequently, this underscores the need for enhanced data collection on factors influencing disease dynamics. This research provides valuable decision-making support, scientific insights, and theoretical guidance for enhancing disease monitoring and prediction strategies.

Vector Autoregressive Model of Maize Production in Northern Region of Ghana

Agricultural growth plays a crucial role in the Comprehensive African Agriculture Development Programme (CAADP, 2009) agenda. The program recognizes that increasing agricultural productivity is essential for reducing poverty, meeting food production targets, and lowering production costs and food prices for the impoverished. This study aimed to develop two types of models. The first model employed a vector autoregressive (VAR) approach, which involved regressing the production of maize in one district against the production of maize in other districts at various lags. The second model utilized a VAR framework where the maize production in each district was regressed against the production of maize in other districts and the corresponding climate conditions at different lag periods. The available data spanned from 1968 to 2018 and were recorded on an annual basis. Six climatic variables were included in the analysis. A lag order of 3 was selected for the models. The results of the autocorrelation test using the portmanteau test indicated no serial autocorrelation across all lag periods. The test for normality revealed that the residuals followed a normal distribution. Additionally, there was no evidence of heteroscedasticity in the data. Furthermore, a Granger causality test was conducted on the selected districts to explore causal relationships. Variance decomposition analysis was performed to assess the variance relation in the data and understand the contribution of different factors. Based on adjusted R-squared, mean absolute error (MAE), and root mean squared error (RMSE) values, the models that incorporated climatic variables were found to be the most suitable for forecasting maize production in the selected districts. The VAR model, which captures the interdependencies between the variables, was utilized in this analysis. All variables in the VAR model were treated symmetrically, meaning that their relationships were considered equally important.

Assessing market efficiency in Palestine Securities Exchange (PSE) market at weak form: Analysis from 2010–2022

The efficient market hypothesis (EMH) asserts that financial markets are efficient, meaning that current market values of stocks incorporate all available information. This study examines the weak-form efficiency of Palestine Stock Exchange stocks using the indices returns from 2010 to 2022. The study used parametric tests (Augmented Dickey-Fuller (ADF) unit root, serial autocorrelation) and nonparametric tests (Phillips-Perron (PP) unit root, run test, variance ratio test). The findings of these tests provide insights into the behavior of the Palestine Stock Exchange market. The run test outcomes reveal a statistically significant pattern in the data for general, insurance, and service indices, with a p-value below the significance level. Furthermore, the unit root tests indicate statistical significance for all indices with 0.00 p-values and t-statistic below the critical values of –2.86 for level and intercept, and –3.14 for level, trend, and intercept, signifying that the indices returns are stationary. In addition, serial autocorrelation test show that the general and Al-Quds indices show statistically significant links between consecutive observations at all four lags. However, the insurance, investment, and services indices show statistically significant results on three lags. The variance ratio test results challenge the random-walk hypothesis for all indices except industry and insurance. With low probability values, a discernible, long-term, predictable pattern is evident in the Palestine Stock Exchange indices. The analysis reveals that Palestine Stock Exchange index returns exhibit nonrandom behavior, suggesting predictability and patterns in daily returns, indicating the possibility of exploiting market inefficiencies in investment strategies.

Systemic Risk and Capital Budgeting Decisions of Quoted Industrial Goods Manufacturing Firms in Nigeria

The purpose of this study was to examine the effect of systemic risk on capital budgeting decisions of quoted industrial goods manufacturing firms in Nigeria. Panel data were collected from annual reports of 15 industrial goods manufacturing firms. Capital budgeting was modeled as the function of exchange rate risk, interest rate risk and consumer price risk. Ordinary least square method of multiple regressions was used as data analysis method. After cross examination of the models, the fixed model was adopted. The regression summary produced adjusted R 2 from the fixed effect regression model implied that 69.9 percent variation in capital budgeting decision of the quoted manufacturing firms were attributed to changes in systemic risk while the model is statistically significant by the value of F-statistics and F-probability. The Durbin Watson statistics implies absence of serial autocorrelation. The study found that exchange rate risk is negative and significant; interest rate risk is positive and significant while consumer price risk is positive but not significant effect on capital budgeting. from the findings, it recommend that There is need for management of the manufacturing firms to formulate strategies of managing systemic risk and the implementation should not just be formulated but strategic and tactical measures should be put in place to absorb, retain and transfer systemic risk and there should be policy to fully deregulate interest rate in the financial market. Systemic risk management should be considered as part of strategic plans which need to be reviewed on a more frequent basis and macroeconomics policies should directed towards stabilizing Nigerian exchange rate to avoid depreciating naira exchange rate against key currencies that exposes t he firms to exchange rate risk

Spatiotemporal Rainfall Variability and Trends over the Mahi Basin, India

Climate change can have an influence on rainfall that significantly affects the magnitude frequency of floods and droughts. Therefore, the analysis of the spatiotemporal distribution, variability, and trends of rainfall over the Mahi Basin in India is an important objective of the present work. Accordingly, a serial autocorrelation, coefficient of variation, Mann–Kendall (MK) and Sen’s slope test, innovative trend analysis (ITA), and Pettitt’s test were used in the rainfall analysis. The outcomes were derived from the monthly precipitation data (1901–2012) of 14 meteorology stations in the Mahi Basin. The serial autocorrelation results showed that there is no autocorrelation in the data series. The rainfall statistics denoted that the Mahi Basin receives 94.8% of its rainfall (821 mm) in the monsoon period (June–September). The normalized accumulated departure from the mean reveals that the annual and monsoon rainfall of the Mahi Basin were below average from 1901 to 1930 and above average from 1930 to 1990, followed by a period of fluctuating conditions. Annual and monsoon rainfall variations increase in the lower catchment of the basin. The annual and monsoon rainfall trend analysis specified a significant declining tendency for four stations and an increasing tendency for 3 stations, respectively. A significant declining trend in winter rainfall was observed for 9 stations under review. Likewise, out of 14 stations, 9 stations denote a significant decrease in pre-monsoon rainfall. Nevertheless, there is no significant increasing or decreasing tendency in annual, monsoon, and post-monsoon rainfall in the Mahi Basin. The Mann–Kendall test and innovative trend analysis indicate identical tendencies of annual and seasonal rainfall on the basin scale. The annual and monsoon rainfall of the basin showed a positive shift in rainfall after 1926. The rainfall analysis confirms that despite spatiotemporal variations in rainfall, there are no significant positive or negative trends of annual and monsoon rainfall on the basin scale. It suggests that the Mahi Basin received average rainfall (867 mm) annually and in the monsoon season (821 mm) from 1901 to 2012, except for a few years of high and low rainfall. Therefore, this study is important for flood and drought management, agriculture, and water management in the Mahi Basin.

EFFECT OF CREDIT RISK MANAGEMENT ON THE FINANCIAL PERFORMANCE OF BANKING SECTOR OF BANGLADESH: A STUDY ON GENERATION-BASED SELECTED LISTED COMMERCIAL BANKS

<p>The financial sector of the country is mostly comprised of banking institutions; those are leading the economy with great exposure with the contribution to the development process. However, the banking sector of Bangladesh is disturbed by the large-scale amount of non-performing loans while has become an essential part of the finance of the industries. As part of the measurement of credit risk and macro factors average lending rate, inflation, NPL size, capital adequacy ratio, liquidity ratio have been selected to test the influence on the financial performance found through the return on asset of the selected banks. To conduct the study 9 banks of three generations have been selected for the period of 2016 to 2022. Robust least square method of regression and error correction term have been run to oversee the real impact on financial performance while endogenity and random walk in the values are being considered to overcome through a dynamic regression model. NPL has a negative impact on the performance and average lending rate, inflation, liquidity ratio and capital adequacy ratio bring a positive impact on the financial performance of the banks. Breusch-Pagan LM test confirms that cross-sectional dependency exists and VAR serial correlation test finds autocorrelation in the data set. The policy implication of this study suggests that the high NPL ratio must be reduced and CAR and LR must be improved to get the desired results of the performance. Strong fiscal and banking regulation should implement so that governance can be ensured to create responsibility and financial strength.</p><p><strong>JEL:</strong> E4, D81, E33, E44</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0045/a.php" alt="Hit counter" /></p>

A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia

Abstract. Changes in aerosol loadings and properties are important for the understanding of atmospheric environment and climate change. This study investigates the characteristics and long-term trends of aerosols of different sizes and types in Asia from 2000 to 2020 by considering multi-source aerosol data and novel analysis methods and perspectives, all of which provides the groundwork for promoting the acquisition of new discoveries that are different from the past. The geometric mean aggregation method is applied, and serial autocorrelation is considered to avoid the overestimation of trend significance. Among regions in Asia, high values of aerosol optical depth (AOD) are mainly concentrated in East Asia (EA) and South Asia (SA) and are closely related to the population density. The AOD in EA showed the most significant negative trend, with a value of -5.28×10-4 per year, mainly owing to decreases in organic carbon (OC), black carbon (BC), and dust aerosols. It is also worth noting that this observed large-scale decrease in OC and BC is a unique and significant phenomenon to region of EA and mainly around China. In contrast, the aerosol concentrations in SA generally show a positive trend, with an increased value of AOD of 1.25 × 10−3 per year. This increase is mainly due to large emissions of fine-mode aerosols, such as OC and sulfate aerosol. Additionally, the high aerosol loading in northern SA has a lower AOD variability when compared with that of East China Plain, revealing a relatively more persistent air pollution situation. Over the whole region of Asia, the characteristics of the percentage changes in different types of AOD are seen in the increases in BC (6.23 %) and OC (17.09 %) AOD, together with a decrease in dust (−5.51 %), sulfate (−3.07 %), and sea salt (−9.80 %) AOD. Except for anthropogenic emissions, the large increase in the percentage of OC is also due to wildfires found in northern Asia in the summer, whereas the differently sized AOD only shows slight changes in Asia in that small-sized AOD decreases (−3.34 %) and that the total AOD did not show a significant change. This suggests that, from a trend perspective, decreases in aerosol in recent years have mostly been offsetting earlier increases in the anthropogenic emissions over Asia. To summarize, the above findings analyse the comprehensive characteristics of aerosol distributions and reappraise the long-term trends of different aerosol parameters, which will greatly enhance the understanding of regional and global aerosol environment and climatology, in addition to filling the gaps and breaking through the limitations of past knowledge.

New Modeling Approach for Predicting Disaggregated Time-Series Traffic Crashes

Short-window crash prediction is a fundamental step in proactive traffic safety management that can monitor traffic conditions in real time, identify unsafe traffic dynamics, and implement suitable interventions for traffic conflicts. Short-window (e.g., hourly) traffic-collision count data, however, exhibits excessive zeros and serial autocorrelation. Most of the commonly used regression-based models fail to address excessive zeros and temporal structure simultaneously in hourly traffic-collision prediction. For example, Hurdle models and zero-inflated (ZI) models can address the overdispersion issue caused by excessive zeros but lack power to control the significant spatiotemporal characteristics inherent in the time-based collision data. To overcome these issues simultaneously, this paper develops a novel statistical model termed Zero-Inflated Logarithmic link for count Time series (ZILT) which is based on the framework of ZI models. Covariates (e.g., speed, vehicle type, and traffic volume) were extracted through deep-learning computer vision methods in vehicle detection and tracking on the image space. This new statistical model (i.e., ZILT) performs better at solving the issues of excessive zeros and serial dependencies. The prediction accuracy of the ZILT model improved by around 5% in relation to zero-inflated Poisson (ZIP) and Hurdle models. Results show that traffic crashes happening in the previous hour and other covariates such as truck-to-car ratio, holiday effect, traffic flow, and speed have significant influence on collision occurrence. Findings from this study could be utilized by relevant transport agencies in developing engineering interventions and countermeasures to proactively manage road safety.

Safety evaluation of centerline rumble strips on rural two-lane undivided highways: Application of intervention time series analysis

Centerline rumble strips are low-cost effective countermeasures installed on the center of the highway segments to reduce crashes, especially roadway departure crashes. For safety evaluation of centerline rumble strips, methodologies such as naïve before-after analysis and cross-sectional study with Empirical Bayes have been widely utilized. The implementation of these methodologies may be limited due to the lack of relevant control groups, and/or other temporal variations in crashes such as seasonality and serial autocorrelation. This study aims to explore Intervention Time Series Analysis approach as an alternative method for the safety evaluation of centerline rumble strips on rural-two-lane undivided highways in Louisiana. Two different methodologies are explored in the intervention time series approach including the Forecast modeling technique and the Auto-regressive Integrated Moving Average intervention model. The forecast models are based on the exponential smoothing technique, state-space framework, and neural network model. The database consists of monthly observations of total and target crashes on 312 highway segments of 1274 miles in length in which centerline rumble strips were installed during the 2010–2012 period. The time frame 2005–2012 is defined as the pre-intervention period whereas the time frame 2013–2017 is defined as the post-intervention period. The analysis revealed that the Auto-regressive Integrated Moving Average intervention model performed better in terms of error estimates including root means square error, mean absolute error, and mean absolute percentage error. The proposed Auto-regressive Integrated Moving Average intervention model reveals a 17.75% total and 40.54% target crash reduction on the selected rural-two-lane undivided highway segments during the post-intervention period. All the findings are found statistically significant at a 95% confidence level.

Drought assessment and rainfall trend analysis in southwestern Nigeria

AbstractProlonged instability and insufficiency of rainfall resulting in drought, extreme weather events, and food insecurity are climate change‐related challenges bedeviling humanity. This study assessed drought conditions and rainfall trends in southwestern Nigeria, using the 1984–2014 rainfall dataset from the Nigerian Meteorological Agency (NIMET). The coefficient of variation (CV), long‐term standardized precipitation index (SPI), serial correlation test, Mann–Kendall trend analysis, Sens's slope estimation, and Pettit test were carried out using various R programming packages. Long‐term mean annual rainfall ranged between 1224.7 and 1537.1 mm. The highest variation (17.3%) was observed for Abeokuta and Ikeja. SPI analysis revealed Ibadan and Oshogbo as the driest and wettest stations, respectively. There was no serial autocorrelation in the dataset. The Mann–Kendall trend showed an upward rainfall trend for Abeokuta, Ibadan, and Ikeja and a downward trend for Akure and Oshogbo. Sen's slope annual rates of change in mm for Abeokuta, Akure, Ibadan, Ikeja, and Oshogbo are 6.69, −2.36, 2.07, 7.82, and 1.42 respectively. The Pettit test revealed change points that are not statistically significant. The drought assessment largely showed a cyclical SPI pattern with greater years of dryness. Given the increasing impact of climate change, future studies are needed to simulate the local drivers of drought severity and frequency and the changing rainfall pattern in rapidly urbanizing cities. There is also the need to incorporate climate‐related adaptive strategies in existing water management policies.

Long-term trend analysis of extreme climate in Sarawak tropical peatland under the influence of climate change

Extreme climate is one of the important variables which determine the capability of tropical peatland to act as either carbon sink and/or carbon source. The purpose of this study is to reveal the spatio-temporal trend in the long-term time series of extreme rainfall and temperature in Sarawak peatland cause by climate change. Gridded-based Princeton datasets were used for trend analysis spanning 68-year (1948–2016) based on Modified Mann-Kendall (m-MK) test which has the capability of distinguishing unidirectional trend with multi-scale variability. The m-MK test was also used to confirm the increasing or decreasing trend produce by Mann-Kendall (MK), and to discriminate the exaggeration in trend caused by serial auto-correlation due to the high effect of large scale climate events regulating the climate in the region. By using R-based program, RClimDex for extreme climate indices output, extreme climate under Northeast (NE) and Southwest (SW) monsoon showed lower grid point with significant changes under m-MK test compared to MK test at 95% significance level. Here, the exaggeration of trend by MK test has been reduced by using m-MK test which can accommodate the scaling effect in the time series due to inherent natural climate variability. Diurnal temperature range (DTR) was expected to decrease for both monsoons in the central-coastal region as minimum temperature (TN) increased more than maximum temperature (TX). Significant increase in extreme rainfall (R10, R20, Rnn) was spatially observed more during SW monsoon compared to NE monsoon, although with high spatial variability. Significant increase of TN indices of TNn and TN90p might cause increased rainfall intensity in the south and central-coastal region, while high TX indices of TXn might cause increased rainfall intensity in the north. Due to the imminent threat of climate change, this study gives scientists an essential view on the behavior of different extreme climate variables and its potential impact on the peatland area which is susceptible to flood and risk of fire during the NE and SW monsoon, respectively.

Trend Analysis and Identification of the Meteorological Factors Influencing Reference Evapotranspiration

Investigating the trends of reference evapotranspiration (ETo) is fundamental importance for water resource management in agriculture, climate variability analysis, and other hydroclimate-related projects. Moreover, it would be useful for understanding the sensitivity of such trends to basic meteorological variables, as the modifications of these variables due to climate change are more easily predictable. This study aims to analyze ETo trends and sensitivity in relation to different explanatory meteorological factors. The study used a 17 year-long dataset of meteorological variables from a station located in Piazza Armerina, Sicily, a region characterized by a Mediterranean climate. First, the FAO-Penman-Monteith method was applied for estimation of ETo. Next, the Mann-Kendall test with serial autocorrelation removal by Trend-free pre-whitening (TFPW) was applied to analyze ETo trends and the basic meteorological variables on which they depend. Sen’s slope was also used to examine the magnitude of the trend of monthly ETo and its related meteorological variables. According to the obtained results, ETo only showed a downward trend of 0.790 mm per year in November, while no trend is shown in other months or on seasonal and annual time scales. Solar radiation (November and Autumn) and rainfall (Autumn) showed a downward trend. The other meteorological variables (minimum temperature, maximum temperature, mean temperature, wind speed, and relative humidity) showed an upward trend both at monthly and seasonally scale in the study area. The highest and lowest sensitivity coefficients of ETo in the study area are obtained for specific humidity and wind speed, respectively. Specific humidity and wind speed give the highest (44.59%) and lowest (0.9%) contribution to ETo trends in the study area. These results contribute to understanding the potential and possible future footprint of climate change on evapotranspiration in the study area.

THE NIGERIA CURRENT ACCOUNT BALANCE AND INFLATION RATE: AN EMPIRICAL ANALYSIS

The study examines the impact of inflation rate on current account balance in Nigeria between 1985 to 2020. The study adopted descriptive analysis, unit root test, Bound test, Autoregressive Distributed Lags Analysis, (ARDL), Error Correction Cointegrating Model (ECCM), and other post estimation tests such as Heteroskedasticity and Serial LM autocorrelation test. Based on the findings from the study, it was discovered that that there were short-run and long-run relationships between inflation rate and current account balance in Nigeria. It was also discovered that there was a direct impact of inflation rate and other independent variables such as exchange rate, trade openness, interest rate and gross capital formation on current account balance. However, the study recommend that government should redesign monetary policy to support international trade to address persistence leakages across all the country’s boarder. By so doing, the local investment and productivity of goods and services would rise hence, inflation would fall to curb external shocks.

The spectral color of natural and anthropogenic time series and its impact on the statistical significance of cross correlation

The cross-correlation between time series is a common tool to study and quantify the impact of climatic and anthropogenic changes on ecosystems. The traditional method for estimating the statistical significance of correlation relies on the assumption that the data are independent, but time series found in nature are often strongly auto-correlated because of low-frequency environmental variability and ecosystem inertia. Previous authors have used Monte Carlo simulations to study the impact of serial auto-correlation on the significance of cross-correlations. Most studies have used random time series that are often a poor representation of those found in nature, e.g., low-order auto-regressive models with normally distributed noise. Moreover, we are not aware of any tests of the applicability of those methods to anthropogenic time series. Here, we study the effect of serial auto-correlation on the performance of two methods for estimating the significance of cross-correlations determined from Monte Carlo simulations with time series that are generated synthetically based on power-law specification of spectral characteristics. Such time series have an auto-correlation structure defined by a single parameter, their spectral “color”, and are generally more convenient representations of natural time series than the autoregressive models. Our results show that one of the two methods considered here accurately reproduces prescribed error rates for the wide range of spectral colors representative of climatic, ecological and anthropogenic time series. For this, we characterized roughly 1800 observational records in different categories of spectral colors, including climate variability, abundance of vertebrate species, and pollution. We specifically focus on time series with annual sampling over data records of at least 40 years, which are particularly relevant for climate studies. The methodology advocated in this study provides a simple and realistic assessment of the significance of sample estimates of cross correlation for time series with any sample interval and record length.

Testing network autocorrelation without replicates.

In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrelation, including spatial and spatial-temporal autocorrelation as special cases. Numerical simulations are carried out to demonstrate and confirm the derived asymptotic results. Convergence is found to occur quickly depending on the number of lags included in the test statistic, and a significant increase in statistical power is also observed relative to some recently proposed permutation tests. An example application is presented by fitting spatial autoregressive models to the distribution of COVID-19 cases across counties in New York state.

Spatio-temporal vegetation cover analysis to determine climate change in Papua New Guinea

PurposeThe climate change and related impacts are experienced around the world. There arise different triggering factors to climate change and impact. The purpose of this study is to figure out how changes in vegetation cover may or may not have an impact to climate change. The research will produce ideas for vegetation preservation and replant.Design/methodology/approachThe investigation was probed for 34 years’ time period starting from the year 1981 to 2015. After testing and checking for serial autocorrelation in the vegetation data series, Mann–Kendal nonparametric statistical evaluation was carried out to investigate vegetation cover trends. Sen’s method was deployed to investigate the magnitude of vegetation cover change in natural differential vegetation index (NDVI) unit per year. Furthermore, the ArcGIS spatial analysis tools were used for the calculation of mean NDVI distribution and also for carrying out the spatial investigation of trends at each specific location within the study region.FindingsThe yearly mean NDVI during the study period was observed to have a decreasing trend. The mean NDVI value ranges between 0.32 and 0.98 NDVI unit, and hence, this means from less or poor vegetated zones to higher or healthier vegetated zones. The mean NDVI value was seen decreasing toward the highlands regions. The NDVI-rainfall correlation was observed to be stronger than the NDVI-temperature correlation. The % area coverage of NDVI-rainfall positive correlation was higher than the negative correlation. The % area coverage of NDVI-temperature negative correlation was higher than the positive correlation within the study region. Rainfall is seen as a highly influencing climatic factor for vegetation growth than the temperature within the study region.Originality/valueThis study in this country is a new approach for climate change monitoring and planning for the survival of the people of Papua New Guinea, especially for the farmer and those who is living in the coastal area.