MODELING THE DYNAMICS OF SPECIES NUMBERS IN COLLEMBOLA COMMUNITIES

The conventional time series methods tend to explore the modeling process and statistics tests to find the best model. On the other hand, machine learning methods are concerned with finding it based on the highest performance in the testing data. This research proposes a mixture approach in the development of the ARDL (Autoregressive Distributed Lags) model to predict the Cayenne peppers price. Multiple time series data are formed into a matrix of input-output pairs with various lag numbers of 3, 5, and 7. The dataset is normalized with the Min-max and Z score transformations. The ARDL predictor variables of each lag number and dataset combinations are selected using the forward selection method with a majority vote of four criteria namely the Cp (Cp Mallow), AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and adjusted R2 . Each ARDL model is evaluated in the testing data with performance metrics of the RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 . Both AIC and adjusted R2 always form the majority vote in the determining optimal predictor variable of ARDL models in all scenarios. The ARDL predictor variables in each lag number are different but they are the same in the different dataset scenarios. The price of Cayenne pepper yesterday is the predictor variable with the most contribution in all of the 9 ARDL models yielded. The ARDL lag 3 with the original dataset outperforms in the RMSE and MAE metrics while the ARDL lag 3 with the Z score dataset outperforms in the R2 metric.

Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

Addressing climate change impact on the energy system: a technoeconomic and environmental approach to decarbonisation

The objective of this study is to examine the short‐run and long‐run impact of macroeconomic variables on E7 stock indices across bullish, bearish and normal states of the stock markets. For this purpose, this study uses both autoregressive distributed lag (ARDL) and quantile ARDL (QARDL) models. The findings based on the ARDL model indicate that, in the long‐run, foreign direct investment (FDI), trade balance and industrial production index (IPI) significantly affect emerging stock indices. In addition, the findings based on the QARDL model indicate that the short‐run effect of FDI, consumer price index, interest rate and exchange rate varies across bullish, bearish and normal states of the emerging stock markets, whereas the long‐run effect varies for all macroeconomic variables except IPI. These findings indicate that the results change when QARDL model is used; however, these findings remain same across seven emerging stock indices. Finally, this study proposes important policy recommendations based on the findings of this study.

PurposeThis study aims to examine the symmetric and asymmetric impact of external debt on inflation in Sudan from 1970 to 2020 within a multivariate framework by including money supply and the nominal effective exchange rate as additional inflation determinants.Design/methodology/approachThe authors utilize an Auto Regressive Distributed Lag (ARDL) model to examine the symmetric impact of external debt on inflation, while the asymmetric impact is examined using a Nonlinear ARDL (NARDL) model. The existence of a long-run relationship between inflation and external debt is tested using the bounds-testing approach to cointegration, and a vector error-correction model is estimated to determine the short parameters of equilibrium dynamics.FindingsThe linear ARDL model results show that external debt has no statistically significant impact on inflation in the long run. On the contrary, the results of the NARDL model show that positive and negative external debt shocks statistically affect inflation in the long run. The estimated long-run elasticity coefficients of the linear and nonlinear ARDL models reveal that the domestic money supply has a statistically significant positive impact on inflation. In contrast, the nominal effective exchange rate has a statistically significant negative impact on inflation.Practical implicationsThe reliance on symmetric analysis may not be sufficient to uncover the existence of a linkage between external debt and inflation. Proper external debt management is crucial to control inflation rates in Sudan.Originality/valueTo date, no empirical study has assessed the external debt-inflation nexus and its potential asymmetry in Sudan, and the current study aims to fill this gap in the literature.

Purpose: This study was conducted to investigate the socioeconomic determinants of tuberculosis (TB) in Nigeria. The prevalence of TB in Nigeria in recent years has been on thunderous increase, and this has led to poor health outcome and dwindled economic growth. Nigeria government has put different measures to stop the prevalence of TB in Nigeria, but it seems their efforts are fruitless. This situation becomes a great challenge to the people and the government. These facts motivated this study to empirically investigate socioeconomic factors/determinants which may have been related to TB continuous prevalence despite the government efforts to stop its menace in Nigeria. Design/methodology/approach: This study used auto regressive distributed lag (ARDL) model for its design and methodology. Unit root test was conducted at the initial stage which led to the decision of using the ARDL model. The ARDL bound test, coefficient test, error correction model, and diagnostic test were conducted. The data used in this study is annual secondary data ranging from 1985 to 2018. The data were sourced from a reliable means. Findings: This study finding shows that there are socioeconomic determinants/factors which related to TB and can control the prevalence of TB in Nigeria. Socioeconomic determinants like income, education, savings, and final consumption expenditure (FCE) were used in this study and they showed a positive relationship with TB. It was only savings and FCE that were significant at 5% and 10%, respectively proving that increase in savings and FCE leads to increase in TB prevalence in Nigeria, which simply implies that people should stop savings in order to fight, control and reduce TB prevalence. Secondly, when FCE is increased meaning no money left to spend to curtail TB, then TB prevalence will increase. Income and education were not significant with TB because savings and FCE are components of income, and they were used in the study. Increase in education may lead to increase in TB prevalence because of the nature of TB transmission from one person, one place to another.

This study examines the connection between electricity consumption and economic growth of India from 1980 to 2017. Initially, the study conducted stationary test to study the stationarity properties of the variables. Autoregressive distributive lag (ARDL) model was used to estimate the long-run relationship among the variables. In addition to this, to estimate the causality between the variables, the study has employed Toda–Yamamoto Granger causality test. The long-run estimation of the ARDL model suggests that electricity consumption does not impact output per capita, while financial development and physical capital have positive and significant impact on output per capita. In line with the ARDL model, the Toda–Yamamoto Granger causality test also does not show any causal relationship among the variables. Based on the empirical outcomes, the study suggests few policy prescriptions.

This study aims to approach the Fisher effect issue from a different methodological perspective. To this aim, the nonlinear autoregressive distributed lag (ARDL) model, recently developed by Shin et al. (2014), is applied for South Korea between 2000Q4Ƀ2017Q4. This model allows us to decompose one variable (changes in inflation) into two new variables (increases and decreases in inflation) under the manners of nonlinearity and asymmetry. Hence, it enables us to monitor the Fisher effect in terms of increases and decreases separately. We also apply the linear version of the same model since the nonlinear ARDL model is the extended version of linear ARDL model. While the empirical findings of the nonlinear model support asymmetrically partial Fisher effects in the long-run for 1, 3, 5 and 10-years Korean bond rates, the linear model does not. Additionally, the nonlinear model detects lower size partial Fisher effects when the maturity of interest rates gets longer. Another finding of this study is that the nonlinear model may mathematically identify and introduce a different version of the partial Fisher effect based on singular (separate) effects of each decomposed variable in a parametric manner.

This paper examined the theory of purchasing power parity (PPP) for a group of developed and developing countries from January 2003 to May 2016 using both linear and nonlinear panel autoregressive distributed lag (ARDL) models. In addition, the paper extended a time series nonlinear ARDL model to a panel nonlinear ARDL model in testing for the PPP. Further, several panel tests of unit root were carried out to inspect the stationary properties of the variables. Outcome of the tests showed that the variables are a combination of I(1) and I(0). Since we have a combination of I(1) and I(0), linear and nonlinear panel ARDL models were estimated. The linear ARDL models were not valid since they failed to provide evidence for cointegration. However, the extended nonlinear panel ARDL models provided evidence of cointegration indicating that the PPP theory is valid for this group of countries. Unlike previous studies on the PPP, this study made a significant contribution by the provision of useful policy implication on the results found.

Orientation: Some recent studies have been published that demonstrated the value of remote sensing night-time lights as descriptors and/or proxies for human activity. Research purpose: This article investigated the association between night-time light emissions and gross domestic product (GDP) estimates for South Africa. Motivation for the study: Satellite night-lights data seemed to be a useful proxy for economic activity at temporal and geographic scales for which traditional data are of poor quality, are unavailable or only available with a large time lag. Research approach/design and method: The article primarily used the remote sensing of night-time light emissions using satellite technologies. The methodology employed in this study involved estimating both a vector error correction modelling (VECM) and autoregressive distributed lag (ARDL) models that map light growth into a proxy for GDP growth. Main findings: Both the VECM and ARDL models confirmed a long-term co-integrating relationship between GDP (per capita) and night-time lights (total light intensity), a statistically significant short-term error correction term could, however, not be established through the VECM, but indeed through the ARDL model. Practical/managerial implications: The results of the study suggested that satellite remote sensing technologies held much promise and opportunities in terms of the field of Economics and Development. Contribution/value-add: This study contributes to our understanding of the spatial and temporal behaviour and trends in economic activity. It also suggested the use of satellite remote sensing technologies as part of official statistical frameworks and methodologies.

BackgroundHuman behavioral responses to changes in risks are often delayed. Methods for estimating these delayed responses either rely on rigid assumptions about the delay distribution (e.g., Erlang distribution), producing a poor fit, or yield period-specific estimates (e.g., estimates from the Autoregressive Distributed Lag (ARDL) model) that are difficult to integrate into simulation models. We propose a hybrid ARDL–Erlang approach that yields an interpretable summary of behavioral responses suitable for incorporation into simulation models.MethodWe apply the ARDL–Erlang approach to estimate the effect of COVID-19 deaths on mobility across US counties from October 2020 to July 2021. A standard panel autoregressive distributed lag (ARDL) model first estimates the effect of past deaths and past mobility on current mobility. The ARDL model is then transformed into an Infinite Distributed Lag (IDL) model consisting of only past deaths. The coefficients of the past deaths are aggregated into an overall effect and fit to an Erlang distribution, summarized by average delay length and shape parameter.ResultsOur results show that on the national level, a one-standard-deviation permanent increase in weekly deaths per 100,000 population (log-transformed) is associated with a 0.46-standard-deviation decrease in human mobility in the long run, where the delay distribution follows a first-order Erlang distribution, and the average delay length is about 3.2 weeks. However, there is much heterogeneity across states, with first- to third-order Erlang delays and 2 to 18 weeks of average delay providing a theoretically cogent summary of how mobility followed changes in deaths during the first year and a half of the pandemic.ConclusionThis study provides a novel approach to estimating delayed human responses to health risks using a hybrid ARDL-Erlang model. Our findings highlight significant variability in the impact and timing of responses across states, underscoring the need for tailored public health policies. This study can also serve as guidelines and an example for identifying delayed human behavior in other settings.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12874-025-02696-x.

Economic and financial theories postulate that stocks should provide a hedge against expected inflation. Since the work of Fisher (1930), there has been on-going empirical investigation into testing the relationships between stock price indices and consumer price indices, in levels and first differences. The findings of these investigations are mixed. One concern is that the methodological issues associated with these studies are not adequately addressed in the literature. The main contribution of this thesis is to identify and improve upon the weaknesses of some of the methodologies employed for testing this relationship and apply the improved methods to Australian data. This thesis conducts investigation into the short run and long run relationships between Australian stock and consumer price indices, in levels and first differences, using bivariate and multivariate frameworks. In addition, this thesis examines, whether or not, the major monetary policy change introduced by the Reserve Bank of Australia in January 1990, has had any significant influence on these relationships. During the period leading up to this change, Australia experienced a high inflationary environment. Using the quarterly data for the period 1969 to 2008 and employing vector autoregression (VAR), autoregressive distributed lag (ARDL) models and bootstrap methods, this thesis presents robust statistical inference on the relationships between stock and consumer price indices. A review of the literature suggests that previous empirical studies investigating this relationship paid inadequate attention to improving the statistical inference on the long run parameters. This thesis makes two major improvements to the methodologies used by previous empirical studies: one is the construction of bootstrap confidence intervals for VAR impulse responses. The other is the estimation of the long run model parameters that are nonlinear functions of those of ARDL models by employing bootstrap methods. Traditionally, OLS and delta methods are used to estimate these long run parameters, although the latter method is known to work well only with large samples under normality. Such strong requirements do not appear to be satisfied for the empirical models studied in the thesis. Here, a bias-corrected bootstrap method for estimating long run model parameters and their confidence intervals is adopted when the normality assumption is violated, and the wild bootstrap method is adopted when both normality and homoscedasticity assumptions are violated. Based on the VAR impulse response functions and bootstrap confidence intervals, this thesis finds that there is a short run negative relationship between stock returns and inflation. The long run ARDL model estimates indicate that the real stock returns are independent of expected inflation, suggesting that Australian stocks constitute a good hedge against expected inflation. Furthermore, the empirical results indicate that the relationship between stock returns and inflation is not affected by the major monetary policy change introduced in January 1990. No evidence of a long run relation between real stock prices and consumer prices was found for the more recent low inflationary period. Based on the empirical evidence presented in the thesis, the overall conclusion is that Australian stocks provide a hedge against inflation, from which domestic and foreign investors can benefit.

This paper brings into play artificial neural network (ANN) to forecast yearly Tunisian health expenditures. We also compare the prediction accuracy of ANN with that of autoregressive distributed lag (ARDL) model. deals with the modelling of the health expenditures in Tunisia in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population ageing , medical density and environmental quality) using artificial neural network (ANN) and the . Thus, the future health expenditures of Tunisia are calculated by means of this model under a scenario. Mean Absolute Percent Error (MAPE) and Root Mean Square Error (RMSE) are used in the comparison of both models. The model that has this low statistic is superior to the other model. In the context, the results obtained revealed an ANN model superior to an ARDL model in health expenditures forecasting. Moreover, the scenario used showed that the future health expenditures of Tunisia would increase in a non proportional way with the GDP. Health would be a luxury good in 2020.

The signal-in-space (SIS) anomaly of BeiDou navigation satellite system (BDS) is an important factor affecting its high accuracy SIS quality assessment. Detecting and eliminating SIS anomaly is not only an important method to build SIS fault model of BDS, but also helps to guarantee the integrity of BDS navigation and positioning. Based on the problem that the traditional empirical threshold method cannot accurately identify the start and end times of anomalies in anomaly detection, which leads to anomaly detection leakage, a combined detection method based on autoregressive distributed lag model and empirical threshold is proposed in this paper. Before the calculation, the spurious anomalies of SIS are removed by data cleaning. The high-precision SIS ranging error (SISRE) is recovered by Space State Representation (SSR) correction number, and then projected to the user’s line of sight direction, and the anomaly detection threshold is determined by using the combined threshold of empirical threshold and autoregressive distributed lag (ARDL) model. The feasibility and effectiveness of the method were analyzed by using the data collected in 2021. The test results show that, compared with the traditional threshold method, the proposed method can more accurately detect the start and end points of SIS anomalies caused by clock anomalies, which is also the detection accuracy is improved. In addition, the anomaly detection method proposed in this paper is used to count the anomalies throughout the year, and the results show that the highest frequency of anomalies is found in geostationary orbit (GEO) and inclined geosynchronous orbit (IGSO), and these anomalies are mainly caused by satellite clocks.

A Multiple-Regime Econometric Model of Inflation for Mauritius