STUDY OF TESTS FOR TREND IN TIME SERIES

The time series methodology is an important tool when using data over time. The time series can be composed of the components trend (Tt), seasonality (St) and the random error (at). The aim of this study was to evaluate the tests used to analyze the trend component, which were: Pettitt, Run, Mann-Kendall, Cox-Stuart and the unit root tests (Dickey-Fuller, Dickey-Fuller Augmented and Zivot and Andrews), given that there is a discrepancy between the test results found in the literature. The four series analyzed were the maximum temperature in the Lavras city, MG, Brazil, the unemployment rate in the Metropolitan Region of S~ao Paulo (RMSP), the Broad Consumer Price Index (IPCA) and the nominal Gross Domestic Product (GDP) of Brazil. It was found that the unit root tests showed similar results in relation to the presence of the stochastic trend for all series. Furthermore, the turning point of the Pettitt test diverged from all the structural breaks found through the Zivot and Andrews test, except for the GDP series. Therefore, it was found that the trend tests diverged, obtaining similar results only in relation to the unemployment series.

NAIVE STATISTICAL ANALYSES FOR COVID-19: APPLICATION TO DATA FROM BRAZIL AND ITALY

This article is a direct consequence of the authors' desire to discuss the role of statistics in data analysis. The analysis of coronavirus (COVID-19) databases are used as to show simple, but powerful statistical frameworks. We do believe that models for assessing future trends in temporal data in general, and in cases and/or deaths of COVID-19, belongs to the area of (Bio)Statistics. Just as engineers use knowledge of physics, chemistry and often architecture, when constructing bridges, buildings and roads, statisticians use knowledge of mathematics, computer science and even physics for modelling, analysing, and forecasting in order to transform data into information. While the statistician's contribution is rarely acknowledged, everyone knows that a building is a work of an engineer. Nonetheless, nowadays statistics has been gaining the attention that it deserves due to the rise of big data and data science that was built on the foundationsof statistics. This article shows that, even with only basic knowledge of statistics, one can adequately collaborate with the community in dealing with very important issues such as the COVID-19 numbers. In order to model and to obtain predictions we use well-known distributions to statisticians working on survival analysis: gamma, Weibull and log-normal distributions. We also make use of singular spectrum analysis, a simple non-parametric time series methodology, for an analogous purpose. Survival analysis is a research area widely used in Biostatistics and even in Reliability, while time series analysis is widely used across areas where the data is measured along the time.

Impact of meningitis vaccination on mortality and notified cases of the disease in Brazil by region between 2009 and 2019

Introduction: Meningitis is a disease with an important history of epidemics during the 20th century. In Brazil, the National Immunization Program (PNI) reduced the general incidence rate of meningitis. However, meningococcal, bacterial,viral and unspecified meningitis are still a challenge in controlling the disease. Objective: Describe data related to vaccination coverage of meningitis, number of deaths and reported cases, between 2009 and 2019, by brazilian region. Design and Setting: Observational study of time series. Methodology: Used secondary data published in DATASUS for the period 2009-2019.The following data were used:Mortality considers deaths from meningitis and meningococcal infection, according to CID- BR-10. Cases were considered by the year of the first symptom and vaccines were evaluated:Meningococcus A/C(MnAC),Meningococcus B/C(MnBC), Meningococcal Conjugate-C (MncC),Meningococcal ACYW1325, Meningococcal B.Proportions were calculated to analyze the trend. Results: 204,211 notifications and 14,562 deaths between 2009 and 2019 were analyzed.The reported cases and deaths from meningitis were decreased by 29% and 34%,respectively.Northeast and Southeast regions stood out with the largest proportional reductions in deaths, with 45% and 37% respectively.For notifications,the Northeast had 54% and the Midwest had a 41% reduction. For Vaccination, all regions had an increase in the period described, with emphasis on the North with an increase of 18,006%,and the Northeast with 30,839%.In addition,the South region increased its applied doses by 499%,with a 4% reduction in deaths,and 10% in notifications. Conclusion:Despite it’s limitations, the analysis suggests the expansion of vaccine coverage contributes positively to the incidence and deaths from meningitis in the Brazilian population.

The Spanish paradox: demand growth with productivity stagnation

PurposeThis paper studies one of the most paradoxical facts of the Spanish economic growth during the period 1982–2007: high growth of investment and aggregate demand accompanied by the stagnation of labor productivity, especially from 1994.Design/methodology/approachThe authors propose two hypotheses: first, that the productive structure neutralized the mechanisms that link investment with productivity, essentially due to the low capital efficiency of the job-creating sectors (JCs); and consequently, investment drove production almost exclusively through employment, generating a trade-off between employment and productivity.FindingsThe econometric results find evidence in favor of both hypotheses applying a time-series methodology (ARIMA) to EU KLEMS data for a period of 25 years and 25 industries of the Spanish economy.Originality/valueThe first contribution of this paper is to offer an interpretation of the phenomenon from a perspective that combines elements of productive supply and aggregate demand, representing a novel contribution to the specialized literature. In addition, the authors show how the Kaldor-Verdoorn law could be neutralized due to employment creation (Okun's law) and the presence of a productivity-employment trade-off.

Forecasting the COVID-19 Outbreak: An Application of Arima and Fuzzy Time Series Models

Purpose: The whole world is surfaced with an inordinate challenge of mankind due to COVID-19, caused by 2019 novel coronavirus (SARS-CoV-2). After taking hundreds of thousands of lives, millions of people are still in the substantial grasp of this virus. This virus is highly contagious with reproduction number R0, as high as 6.5 worldwide and between 1.5 to 2.6 in India. So, the number of total infections and the number of deaths will get a day-to-day hike until the curve flattens. Under the current circumstances, it becomes inevitable to develop a model, which can anticipate future morbidities, recoveries, and deaths. Methods: We have developed some models based on ARIMA and FUZZY time series methodology for the forecasting of COVID-19 infections, mortalities and recoveries in India and Maharashtra explicitly, which is the most affected state in India, following the COVID-19 statistics till “Lockdown 3.0” (17th May 2020). Results: Both models suggest that there will be an exponential uplift in COVID-19 cases in the near future. We have forecasted the COVID-19 data set for next seven days. The forecasted values are in good agreement with real ones for all six COVID-19 scenarios for Maharashtra and India as a whole as well.Conclusion: The forecasts for the ARIMA and FUZZY time series models will be useful for the policymakers of the health care systems so that the system and the medical personnel can be prepared to combat the pandemic.

Gaming alone: Videogaming and sociopolitical attitudes

What sustains prosocial attitudes and political engagement in the era of online connectivity? Scholars disagree on whether frequent consumers of virtual entertainment disconnect from sociopolitical life. Using the Swedish Political Socialization Panel dataset and partial-pool time series methodology, I investigate the relationship between playing videogames and adolescents’ political and social attitudes over time. I find that those gamers who spend more time engaging in their favorite pastime become less interested in sociopolitical issues and less prosocial than non-gamers from year to year. My findings tell a cautionary tale about the adverse effects of extensive gaming on the development of democratic attitudes among adolescents.

An empirical investigation of the relationship between the real exchange rate and net FDI inflows in Mauritius

Purpose The purpose of this paper is to find out the impact of real exchange rate on foreign direct investment (FDI) in Mauritius. Design/methodology/approach Autoregressive distributed lag time series methodology is used. Findings Real exchange rate depreciation enhances inflows of FDI in both the short and long run. Originality/value The research is original, and data used are from official sources.