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Road traffic accidents are a growing public health concern. In this study, we focused on analyzing and forecasting the monthly number of accidents, number of injuries, and number of deaths in Algeria over the period (2015–2020). For this purpose, hybrid forecasting models based on equal weights and in-sample errors were fitted, and we compared them with the seasonal autoregressive moving average (SARIMA) models. The three models retained for forecasting until 2022 are all hybrid models, one based on equal weight and two models based on in-sample errors (using the RMSE indicator). Furthermore, the hybrid models outperformed the SARIMA models for short (6 months), medium (12 months), and long horizon (24 months). The forecasting results showed that we expect an increase in the number of accidents, the number of deaths, and the number of injuries over the next 12 months. Policymakers must enhance strategies for prevention and road safety, especially in rural areas, where the highest rate of fatalities is recorded.

Exports were the motor of a country's economic growth and development. Therefore, the main goal of all governments was to reach a certain level of economic growth. Therefore, this study aimed to examine the relationship between agricultural and nonagricultural exports and India's economic development indicator (GDP proxy). Different cointegration tests investigated the longlasting relationship between the three variables with regime shifts. The findings showed that agriculture exports had a significant effect on real GDP. However, no evidence of non-agricultural exports significantly affecting real GDP was observed. The causality analysis showed two-directional causal relationships between India's agricultural, non-agricultural and real GDP exports. In India, policymakers can use agricultural exports as a catalyst to boost economic growth.

Objective The specific mortality rate (MR) due to COVID-19 is a useful indicator for monitoring and evaluating the health strategies of health systems in the pandemic era. The main objective of this study is to estimate the effects of social, health, and economic factors on MRs in 176 countries. Material and Methods Beta regression models were used, and MRs were estimated as the total number of deaths divided by the total number of confirmed cases (infection fatality rate) until December 2, 2021. Results The primary findings revealed heterogeneity in mortality rates between regions and countries. The estimated coefficients showed different patterns of association between the explanatory variables and mortality rates. In the American region, the results showed a strange pattern and nearly insignificant effect for almost all variables. In Asian countries, we found a significant effect of GDP per capita and the share of the population aged 65 years and older on mortality rates, whereas on the African continent, the significant variables affecting mortality rates were GDP per capita, human development index, and share of population aged 65 years and older. Finally, in the European region, we did not find clear evidence of an association between the explanatory variables and mortality rates. Conclusion These results show, in a heterogeneous way among regions, the impact of aging, development level and population density (especially with forms of distancing) on increasing the risk of death from the coronavirus. In conclusion, the pandemic has succeeded in demonstrating chaotic patterns of associations with social, health, and economic factors.

In recent decades, Fuzzy Time Series (FTS) has become a competitive, sometimes complementary, approach to classical time series methods such as that of Box-Jenkins. This study has two different purposes: a theoretical purpose, presenting an overview of the fuzzy logic and fuzzy time series models, and a practical purpose, which is to estimate and forecast monthly international coffee prices during the period 2000-2022. Analysing and forecasting the dynamics of coffee prices is of great interest to producers, consumers, and other market actors in managing and making rational decisions. The findings showed that international coffee prices exhibited significant fluctuations, with large increases and decreases influenced mainly by the level of top-ranked producers. The forecasted results revealed that a decrease in prices during the next six months (Jan 2023 to June 2023) is expected. Based on the results, it is also clear that the FTS models are more flexible and can be applied in forecasting time-series variables. At the same time, volatility and, sometimes, the unexpected swingsin coffee prices continue to draw more criticism and raise different issues regarding the roles of the markets and countries in ensuring food security.

Under-five-child mortality remains a major challenge for governments in the Arab world to achieve Sustainable Development Goals. Thus, further studies are needed to analyze the determinants of child mortality. The Multiple Indicators Cluster Surveys (MICS) datasets of six Arab countries (Algeria, Egypt, Iraq, Mauritania, Sudan, and Tunisia) have been used, which are consisting of 249.000 children nested within 54.644 mothers. The study was designed in a women-parity-covered one to six birth order. Binary multivariable logistic models were used to estimate the risk ratios of death by adjusting for child sex, birth outcome (twin vs. singleton), mother’s education level, maternal age, previous birth interval, place of residence (rural vs. urban), and the family wealth index. The findings revealed that the under-five child mortality rates were 87, 70,66,35,36, and 21 per 1000 live births in Sudan, Mauritania, Egypt, Iraq, Algeria, and Tunisia, respectively). First-born infants in these six countries have a higher risk of mortality during their five years of life. Second, third-and fourth-born infants were at a decreased risk of death compared to first-born infants in all countries; in contrast, fifth-and-sixth-born infants were at an increased risk in all countries except Sudan and Mauritania. Twin children have a higher risk of death than singletons in all countries and across all birth orders. Children of mothers with higher educational levels living in urban areas are at lower risk of death than their peers across all birth orders. Regarding policy implications, decision-makers can target three main axes: first, enhancing women’s educational levels; second, increasing birth intervals (birth spacing policies); and third, improving living standards and healthcare strategies, especially in rural areas to improve child and mother health.

Research background: Public spending is a generator of economic growth as well as its components; this reality is more depicted in the era of the COVID-19 world pandemic where a recession in economic activities has touched all countries. Purpose: In this paper, we tried to study the impact of shocks in public expenditure on some macroeconomic variables in Algeria during the period (1970–2019). Research methodology: The VAR Structural models were used to study the response of these variables to shocks in public spending in Algeria. Results: The results of the modeling indicate a direct response of both exports and imports to a shock in the levels of public expenditure, but this response is relatively weak to the variable value of exports (especially in the short term), which is mainly due to the structure of the Algerian economy that is mainly dependent on the export of oil and gas, which in turn is mainly affected by international energy factors e.g. prices, supply, and demand. For the rate of inflation, there was an inverse response to shocks in the level of public spending. In the context of the global health and economic crisis, we will witness a further faltering of economic growth in Algeria. Novelty: Our contribution is a new feature of the application of the SVAR model in the era of the COVID-19 pandemic that focused on analyzing the impacts of public expenditure on exports and imports

The challenges of fighting poverty and enhancing food security in South Asia have made maize a strategic crop in this region. In this study, maize production in South Asia, encompassing Afghanistan, Bangladesh, Bhutan, China, India, Nepal, Pakistan, and Sri Lanka, was analysed and projected from 1961 to 2027 using state-space and ARIMA models. The estimation outcomes demonstrated the state-space models’ superior performance in predicting trends in maize output for all eight time series. Additionally, the forecast estimation revealed that we anticipated an uptick in the output of maize in these nations; this finding would be encouraging for the countries in this region as it would heighten the problem of food security. India would be leading countries in maize with production of 380438 thousand tonnes in 2027.

An attempt has been made to study the behavioural pattern of prices and market arrivals for rice by generating a monthly seasonal index of market arrivals and prices for the crop in five selected markets, namely Bardhaman, Siliguri, Contai, Chakdah, and Bishnupur of West Bengal from 2013–14 to 2018–19. It establishes that the month of February has the greatest average monthly arrival, followed by the months of March and May. It has a declining pattern up until October, after which it climbs upward and reaches its peak in February. In case of prices, long term movement over time has a significant increasing trend pattern expect Contai and Siliguri markets. The variability associated with arrival is very high may be due to the uncertain prices and seasonal nature of agricultural production system. But variability related with prices are comparatively less. All five markets have relatively little intra-year price variance. The relationship between market arrival and current price is positive for all markets except Siliguri and Bishnupur where one year lagged price is positively correlated with arrival only for Contai and Bishnupur markets.

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread. In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.

L'evenement de jour dans le monde est le virus corona; la propagation de ce virus peut mettre tous les pays en situation d'incapacite a gerer et a faire face. Cet article portait sur la classe des modeles ARIMA et les series temporelles a logique floues. Ces deux techniques sont appliquees a la trajectoire du virus Corona dans trois pays africains: l'Algerie, l'egypte et l'Afrique du Sud sur la periode (2020-02-15 / 2020-03-19). Bien que l'hyper stochastique de cette pandemie, on voit que les modeles ARIMA correspondent bien a la trajectoire de Covid-19. Nous prevoyons une tendance continue a la propagation du virus dans les prochains jours, un fait qui alertera les gouvernements de ces pays et l'ensemble des pays africains pour renforcer encore les politiques de prevention et d'intervention pour lutter contre cette epidemie.

A large proportion of the Indian population is vegetarian and pulses are important sources of protein in the daily diet .In this paper an attempt has been made to summarize the overall nature of area, production and productivity of mung in India. By and large there has been considerable expansion in area, production and productivity of mung in all the states under study including whole India during the study period. Among the states under study, the maximum annual growth in area (9.75%) and production (14.55%) of mung was observed in Rajasthan. Bihar stands first in productivity of mung among the states under study. Rajasthan, Karnataka and Andhra Pradesh have fails to reach national average per hectare production of 367.37 kg/ha. In this paper an attempt has been made to summarize these measures along with some new measures with an objective to study the yield sustainability of particular crop over the growing regions and compare across the states/regions. Sustainability in yield of mung in different states along with whole India has been measured with the help of existing and proposed measures of sustainability indices. Whole India is showing higher sustainability in yield of mung as per the two existing and proposed methods. According to all the indices including developed two methods Rajasthan is having comparatively lower sustainability to produce mung among the states under study. Results of existing measures and proposed measure are almost in conformity with each other. From the forecasted value, it can be said that, mung productivity of India would increase to 408.84 kg/ha in 2022 as compared to 2012. In Mung, area, production and productivity Rajasthan would be leading state of India in 2022.This projection would be helpful for policy implication and planning.

The world as well as in India, rice is playing a major role in food security. Production factors (like rainfall, minimum temperature, fertilizer consumption, an area under irrigation for a particular crop) are very crucial for crop productivity. Forecasting is always important for policy implication and planning purposes of the country. In the present investigation, the projection has been made using simple ARIMA and ARIMAX (with the inclusion of crop inputs in ARIMA models). In terms of less error in model and projection, wise ARIMAX model was found better compared to simple ARIMA. In this present study, forecasting has been attempted with the inclusion of meteorological factors using ARIMA modeling up to the year 2022. This study reveals the future trend of rice production as well as a factor affecting productivity. Among the major states, West Bengal would lead the state in India in rice production, with a productivity of 4758 kg/ha, while Punjab will be the leader in productivity in the year 2022. This prediction would be helpful for policy implication and food security of the country.

Bayesian statistics can be presented as a generalization of the frequentist approach. The choice of probabilized the space of states is truly revolutionary. Statisticians draw a hermetic border between the notion of unknown parameter and random parameter. In this article, we want to show the interest of Bayesian econometrics and this analytical approach on three important points; in the level of characteristics, in the advantages and finally in the properties, {"references":["1. Basturk, N, (2013),Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14. Tinbergen Institute Discussion Paper 191/III","2. Bernardo, J. et Smith, A, (1994),Bayesian Theory. John Wiley, New York.","3. Carlin, B. et Louis, T, (2001), Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall, New York, seconde edition.","4. Gelman, A., Carlin, J., Stern, H., et Rubin, D, (2003), Bayesian Data Analysis. Chapman and Hall, New York, second ́edition.","5. Geweke, J, (2005),Contemporary Bayesian econometrics and statistics(Vol. 537). John Wiley & Sons","6. Ibragimov, I. A., & Hasminskii, R. Z, (1981),Statistical estimation, volume 16 of Applications of Mathematics.","7. Koop, G, (2003),Bayesian econometrics. John Wiley & Sons.","8. Koop, G., Poirier, D. J., & Tobias, J. L, (2007),Bayesian econometric methods. Cambridge University Press.","9. O'Hagan, A, (1994),Bayesian Inference. Numero 2B dans Kendall's Advanced Theory of Statistics. Chapman and Hall, New York.","10. O'Hagan, A. et Forster, J, (2002), Bayesian Inference.Numero2Bdans Kendall's Advanced Theory of Statistics. Chapman and Hall, New York, seconde ́edition.","11. Schervish, M, (1995),Theory of Statistics. Springer-Verlag, New York"]}

Bayesian statistics can be presented as a generalization of the frequentist approach. The choice of probabilized the space of states is truly revolutionary. Statisticians draw a hermetic border between the notion of unknown parameter and random parameter. In this article, we want to show the interest of Bayesian econometrics and this analytical approach on three important points; in the level of characteristics, in the advantages and finally in the properties., {"references":["[1]\tBasturk, N, (2013), Historical Developments in Bayesian Econometrics after Cowles Foundation Monographs 10, 14. Tinbergen Institute Discussion Paper 191/III","[2]\tBernardo, J. et Smith, A, (1994), Bayesian Theory. John Wiley, New York.","[3]\tCarlin, B. et Louis, T, (2001), Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall, New York, seconde edition.","[4]\tGelman, A., Carlin, J., Stern, H., et Rubin, D, (2003), Bayesian Data Analysis. Chapman and Hall, New York, second ´edition.","[5]\tGeweke, J, (2005), Contemporary Bayesian econometrics and statistics (Vol. 537). John Wiley & Sons.","[6]\tIbragimov, I. A., & Hasminskii, R. Z, (1981), Statistical estimation, volume 16 of Applications of Mathematics.","[7]\tKoop, G, (2003), Bayesian econometrics. John Wiley & Sons.","[8]\tKoop, G., Poirier, D. J., & Tobias, J. L, (2007), Bayesian econometric methods. Cambridge University Press.","[9]\tO'Hagan, A, (1994), Bayesian Inference. Numero 2B dans Kendall's Advanced Theory of Statistics. Chapman and Hall, New York.","[10]\tO'Hagan, A. et Forster, J, (2002), Bayesian Inference.Numero2Bdans Kendall's Advanced Theory of Statistics. Chapman and Hall, New York, seconde ´edition.","[11]\tSchervish, M, (1995), Theory of Statistics. Springer-Verlag, New York"]}

Objectives Analyzing the dynamics and patterns of birth intervals in the Algerian population is an important issue in developing an effective population policy. In this study, we attempted to estimate the effects of socioeconomic and demographic factors on the birth spacing process. Methods Semi-Markov models were used, based on data from the Multiple Indicator Cluster Survey (MICS), where the birth histories of 13,453 infants nested within a sample of 6,958 married women were analyzed. Results The findings stated that the birth intervals depend on: (i) mothers’ educational level, whereas wider intervals have been found for highly educated women, (ii) the wealth index, as women from poor families have short birth intervals, and (iii) there was no clear difference between rural and urban areas. Conclusions Policymakers can act through these axes to develop more efficient strategies for family planning.

The change points have considerable effects in different areas of applied research. We will use in this work the pseudo-bayes factor in three autoregressive models of order (1); this method permits to analyse the impact of choice between models and allows the use of a simpler technique with model selection in time series. For application, the monthly fluctuations of the DOW-JONES series between January 1999 and September 2009 have been used; we try to detect the financial crisis between 2007 and 2008 to evaluate the model selection method. Les points de changement ont des effets considerables dans diff ´ erents ´ domaines de la recherche appliquee. Nous utiliserons dans ce travail le facteur ´ pseudo-bayesien dans trois mod ´ eles d’ordre autor ` egressif (1); cette m ´ ethode per- ´ met d’analyser l’impact du choix entre les modeles et permet l’utilisation d’une ` technique plus simple avec la selection des mod ´ eles en s ` eries temporelles. Pour ´ l’application, les fluctuations mensuelles de la serie DOW-JONES entre janvier ´ 1999 et septembre 2009 ont et´ e utilis ´ ees; nous essayons de d ´ etecter la crise fi-nanciere entre 2007 et 2008 pour ` evaluer la m ´ ethode de s ´ election des mod ´ eles. ` Key words: change point; AR(1); Bayes factor

India is accounting for almost 20 percent of total milk production in the world and 70 percent of this share is coming from small, marginal farmers and landless people of the country residing in rural areas and this shows that dairy industry has an important role in social and economic development in India. Dairy is growing with a positive rate as per capita availability has reached to 375 (gms/day) in 2017-18 from 178 (gms/day) in 1990-91. In this study, time series data (2001-02 to 2015-16) on milk production and different milching species population of Chhattisgarh have been used to find out the suitable forecasting models for milk production and population of these mulching animals of Chhattisgarh. To meet the objective of study different Autoregressive Integrated Moving Average (ARIMA) models have been tried and among all ARIMA (0,2,0) model has been found more suitable for production of milk in India and Chhattisgarh both. Availability of milk is forecasted suitably by ARIMA (0,2,1) and ARIMA(0,1,1) for India and Chhattisgarh respectively. Similarly different ARIMA models have been fitted for population of different species animals. By this study milk production is expected to reach 219.73 MMT and 1.599 MMT by 2022-23 in India and Chhattisgarh respectively.

In this paper, we deal with the issue of missing data, in particular in the database (ENPMO) of the national survey of the shortage in agriculture workers in Algeria carried out by the Institut National de la Vulgarisation Agricole (INVA), where the main concern is the worker's assurance. We concluded with the existence a systematic process of non-response by the farmers which were surveyed, which indirectly violated the random distribution hypothesis of missing data. By using R and Sphinx softwares, we build a logistic model in the frame of the Bayesian inference to estimate the effect of three variables : Age, Education level and the farm superficies, which ares supposed have an impact of the non-response process.

La determination du nombre et de la longueur des intervalles de la fonction de risque de base $\lambda_{0}(t)$, est un probleme important dans les modeles exponentiels constant par morceaux (ECM) et les modeles a risque proportionnels (PH); notamment, lorsque nous utilisons l'inference bayesienne. Dans ce contexte, nous proposons une methode simple permettant d'estimer le nombre et la longueur des intervalles afin de construire un modele bayesien de Cox PH. Sur la base des donnees reelles, les resultats obtenus montrent que les parametres d'estimation n'ont pas ete affectes, mais que les criteres de log-vraisemblance et d'information sont tres sensibles. Sur cette base, le probleme de la selection du modele est considere pour evaluer l'influence sur la prise de decision.

Pulses are popularly known as poor man’s meat. Pulses mainly constituted of gram, arhar, mung, urad and lentil etc. The energy content of most pulses have been found to be between 300 and 540 Kcal/100g. India is the largest producer and consumer of pulses in the world contributing around 14.36% of the total global production. Decision support system (DSS) has been designed and developed using a visual basic platform for major pulse crops grown in India. The main objective is to provide decision support regarding recommended water, weed, pest and diseases management, state wise varieties and nutrient management based on the soil test value to the farmer, agriculture workers, students, research workers, extension workers and others, even in absence of the experts. The system is console application, menu driven and user friendly. A sincere effort has been made with the available data to develop the decision support system to bridge the gap between the farmers and the experts using visual basic platform.