Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Using Interrupted Time Series Design to Analyze Changes in Hand, Foot, and Mouth Disease Incidence during the Declining Incidence Periods of 2008-2010 in China

Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.

Does Monetary Support Increase the Number of Scientific Papers？ An Interrupted Time Series Analysis

Purpose： One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council （TUBITAK） to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so.Design/methodology/approach： We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science （WoS） database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series （ITS） analysis technique （also known as ＂quasi-experimental time series analysis＂ or ＂intervention analysis＂） to test if TOBITAK＇s support program helped increase the number of publications. We defined ARIMA （1,1,0） model for ITS data and observed the impact of TOBiTAK＇s support program in 1994, 1997, and 2003 （after one, four and 10 years of its start, respectively）. The majority of publications （93%） were full papers （articles）, which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis.Findings： TUBITAK＇s support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TUBITAK＇s support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations. Research limitations： Interrupted time series analysis shows if the ＂intervention＂ has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the＂intervention＂, other ＂event（s）＂ that might affect the time series data （e.g., increase in the number of research personnel over the years） should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher. Practical implications： TUBITAK＇s ＂cash-for-publication＂ program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as ＂micropayments.＂ Originality/value： Based on 25 years＇ worth of payments data, this is perhaps one of the first large-scale studies showing that ＂cash-for-publication＂ policies or ＂piece rates＂ paid to researchers tend to have little or no effect on the increase of researchers＇ productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs （based usually on bibliometric measures such as journal impact factors） and revising their reward policies.

An Interrupted Time Series Analysis of COVID-19 Positivity before, during and after Lockdown in Four States of India

Objectives: The objective of this study was to examine the impact of large scale non-pharmaceutical interventions on COVID-19 pandemic. Methods: We used interrupted time series analysis (ITS), a quasi-experimental model to evaluate the effect of interventions in four states of India by comparing the COVID-19 positivity before lockdown, during lockdown and opening-up period. Results: The positivity in all the four states declined during lockdown and the trends reversed soon after the lockdown measures were relaxed as the states opened-up. The rate of reduction of positivity was significantly different between states. Between the lockdown and opening-up period, an increase in positivity was recorded in all the states with significant variation between states. Conclusion: The analysis provides conclusive evidence that the lockdown measures had a positive effect in reducing the burden of COVID-19 and establishes a causal relationship.

ON THE COMPLETION TIME OF A JOB PROCESSED ON AN UNRELIABLE MACHINE

In this paper, we consider the completion time of a job processed on an unreliable machine. Assume that the lifetime and the repair time of the machine and the service time of the job have general distributions. We obtain the Laplace-Stieltjes transforms and the expectations of the distributions of the completion time, the interruption time and the actual service time. Under some special cases, we derive sufficient and necessary (or sufficient) conditions such that the completion time is larger and smaller than the service time in the sense of the Laplace transform order (or the expectation), respectively

Congenital heart diseases trends in S?o Paulo State,Brazil:a national live birth data bank analysis

Background Congenital heart diseases(CHD)are the most common type of birth defects,affecting millions of newborns every year;no prevalence data are available in S?o Paulo State,Brazil's most populous state.The objective is to identify trends in prevalence and risk factors for CHD in Sao Paulo State,Brazil.Methods We developed a population-based study to estimate the CHD trend of prevalence in recent years,stratified by maternal age and geographical clusters,using all cases of CHD identified by the Live Births Information System(SINASC-Sistema de Informa??o Sobre Nascidos Vivos)from January 1,2005,to December 31,2018.We calculated the trends of prevalence,the regression coefficient(β),the annual percent change(APC),and 95%confidence interval using the Prais-Winsten regression model,with the Durbin-Watson test.Results We found 10,594 cases of CHD among 8,536,101 live births(LB),a prevalence of 12.4/10,000 LB.There was no difference in the sex distribution;they are primarily Caucasian(60.2%),75.2%born at term,and 74.4%weight>2500 g,66.9%of births were by cesarean section.S?o Paulo State presented an increasing CHD trend of prevalence(APC=18.9%).The highest CHD prevalence rate was in mothers aged≥35 years(22.2/10,000 LB).There were 12,271 specific congenital heart defects among 10,594 patients(1.16 CHD/patient).Atrial septal defect has the highest number of cases(3835),with a prevalence of 4.49/10,000 LB,corresponding to 31.3%of all CHD.Conclusion CHD had an increasing prevalence trend in recent years,being highest in S?o Paulo City and≥35-year mothers.

COVID-19 economic policy effects on consumer spending and foot traffic in the U.S.

To battle with economic challenges during the COVID-19 pandemic,the US government implemented various measures to mitigate economic loss.From issuance of stimulus checks to reopening businesses,consumers had to constantly alter their behavior in response to government policies.Using anonymized card transactions and mobile device-based location tracking data,we analyze the factors that contribute to these behavior changes,focusing on stimulus check issuance and state-wide reopening.Our finding suggests that stimulus payment has a significant immediate effect of boosting spending,but it typically does not reverse a downward trend.State-wide reopening had a small effect on spending.Foot traffic increased gradually after stimulus check issuance,but only increased slightly after reopening,which also coincided or preceded several policy changes and confounding events(e.g.,protests)in the US.We also find differences in the reaction to these policies in different regions in the US.Our results may be used to inform future economic recovery policies and their potential consumer response.