A Time-Varying Conditional Parameter Distributed Lag Model with an Application to Crude Oil Market

This paper proposes a new time-varying parameter distributed lag(DL)model.In contrast to the existing methods,which assume parameters to be random walks or regime shifts,our method allows time-varying coefficients of lagged explanatory variables to be conditional on past information.Furthermore,a test for constant-parameter DL model is introduced.The model is then applied to examine time-varying causal effect of inventory on crude oil price and forecast weekly crude oil price.Time-varying causal effect of US commercial crude oil inventory on crude oil price return is presented.In particular,the causal effect of inventory is occasionally positive,which is contrary to some previous research.It’s also shown that the proposed model yields the best in and out-of-sample performances compared to seven alternative models including RW,ARMA,VAR,DL,autoregressive-distributed lag(ADL),time-varying parameter ADL(TVP-ADL)and DCB(dynamic conditional beta)models.

Vertical Price Transmission in the China's Layer Industry Chain:an Application of FDL Approach

This paper examines the vertical price relationship between upstream and downstream products in China＇s layer industry chain by estimating elasticity coefficients of the price transmission. We use cointegration tests, error correction models and finite distributed lag models to analyze vertical price shifts between corn, layer feed, egg-laying chicken and egg prices. With monthly data from 1994 to 2010, our results show that various prices in China＇s layer industry chain are well integrated. In addition, our results indicate that both long-run and short-run price relationships between upstream and downstream products in layer industry chain exist and the long-run equilibrium, to a certain extent, may eliminate the short-run dynamics price deviation. We also found that the influence of corn and feed prices on egg prices is still more remarkable than egg-laying chicken prices. Our findings imply that governments should pay more attention to corn and layer feed prices in order to stabilize egg prices under the conditions of market integration.

Determinants of Commercial Bank Financial Performance:Empirical Evidence From Ethiopia

The study examines factors that determine the financial performance of commercial banks in Ethiopia by using time series data over the period 2004-2019 on the sample of seven banks using secondary data.Moreover,the autoregressive distributed lag model was used.Under this study,both internal and external factors were included as the determinants of bank performance which was measured by loan-to-deposit ratio.The internal factors used in this study include capital adequacy ratio,non-performing loan and loan growth while the external factors are real GDP growth and inflation.Based on the results,specific variables except non-performing loan capital adequacy and loan growth affect banks performance significantly in the long run.In the short run,in addition to those two variables,non-performing loan also affects bank performance.Real GDP growth has negative significant effect on the banks performance in both long and short run.Inflation has insignificant effects on bank performances in both long and short run.

Spatiotemporal Distribution and Epidemiological Characteristics of Hospital Admissions for Carbon Monoxide Poisoning in Guangdong,China,2013-2020

Objective This study aimed to determine the spatiotemporal distribution and epidemiological characteristics of hospital admissions for carbon monoxide poisoning(COP)in Guangdong,China,from 2013 to 2020.Methods Data on age-and sex-specific numbers of hospital admissions due to COP in Guangdong(2013-2020)were collected.Daily temperatures were downloaded through the China Meteorological Data Sharing Service System.We analyzed temporal trends through time series decomposition and used spatial autocorrelation analysis to detect spatial clustering.The distributed lag nonlinear model was used to quantify the effects of temperature.Results There were 48,854 COP admissions over the study period.The sex ratio(male to female)was1:1.74.The concentration ratios(M)ranged from 0.73-0.82.The highest risk occurred in January(season index=3.59).Most cases were concentrated in the northern mountainous areas of Guangdong with high-high clustering.COP in the study region showed significant spatial autocorrelation,and the global Moran’s I value of average annual hospital admission rates for COP was 0.447(P<0.05).Low temperatures were associated with high hospital admission rates for COP,with a lag lasting 7 days.With a lag of 0 days,the effects of low temperatures[5th(12℃)]on COP were 2.24-3.81,as compared with the reference temperature[median(24℃)].Conclusion COP in Guangdong province showed significant temporal and spatial heterogeneity.Low temperature was associated with a high risk of COP,and the influence had a lag lasting 7 days.

Nexus between selected macroeconomic variables and carbon emission in Kenya

Agriculture is not only influenced by climate change,but it is also one of the significant contributors of CO_(2) emission.Understanding CO_(2) emission and macroeconomic variables is critical to solving the challenges and threats faced by Kenya’s agriculture and environment.This study used the Environmental Kuznets Curve(EKC)hypothesis and the autoregressive distribution lag model(ADLM)to analyze the relationships of CO_(2) emission with agricultural output,government direct investment,trade openness,and inflation rate in Kenya from 1983 to 2019.The study found that there exists a positive(direct)relationship between CO_(2) emission and foreign direct investment in the long run in Kenya.Additionally,CO_(2) emission and trade openness have a negative(indirect)and statistically significant relationship after the error correction term adjustment in the long run.Moreover,the relationship between CO_(2) emission and agricultural output is positive(direct)and statistically significant in the long run.There is a positive(direct)and statistically insignificant relationship between CO_(2) emission and inflation rate in the short run.Notably,the EKC hypothesis indicated that the Kenya’s economy is still on the environmental degradation trade-off through the gradual increase of both CO_(2) emission and agricultural output.Our results are important to Kenya’s economy because the derived insights will assist in relevant departments to formulate sustainable strategies to minimize environmental degradation.

A Panel Data Analysis of the Impact of Chinese Foreign Direct Investment (FDI), Remittances and Foreign Aid on Human Capital Growth and Brain Drain in Africa

The main purpose of this research was to analyze the impact the Chinese foreign direct investment (FDI), remittances, and foreign aid have had to human capital growth (HCG) and brain drain. The study data were collected from five African countries (Nigeria, Kenya, Ghana, South Africa, and Morocco) from 2009 to 2018. Secondary sources were used in data collection, then autoregressive distributed lag (ARDL) modeling was used in the analysis. Before modelling was done, co-integration tests and panel unit were applied. The results revealed that Chinese FDI, remittances, and foreign aid had a significant and positive impact on HCG in the long but not the short-run. Besides, remittances, Chinese FDI, and foreign aid demonstrated significant negative impacts on brain drain in the long term, not in the short term. This study makes important practical and theoretical contributions about the roles of Chinese FDI, remittances, and foreign aid in the reduction of brain drain and the growth of human capital.

Light at night and lung cancer risk:A worldwide interdisciplinary and time-series study

Background:Light at night(LAN)has become a concern in interdisciplinary research in recent years.This global interdisciplinary study aimed to explore the exposure-lag-response association between LAN exposure and lung cancer incidence.Methods:LAN data were obtained from the Defense Meteorological Satellite Program’s Operational Linescan System.Data of lung cancer incidence,socio-demographic index,and smoking prevalence of populations in 201 countries/territories from 1992 to 2018 were collected from the Global Burden of Disease Study.Spearman correlation tests and population-weighted linear regression analysis were used to evaluate the correlation between LAN exposure and lung cancer incidence.A distributed lag nonlinear model(DLNM)was used to assess the exposure-lag effects of LAN exposure on lung cancer incidence.Results:The Spearman correlation coefficients were 0.286-0.355 and the population-weighted linear regression correlation coefficients were 0.361-0.527.After adjustment for socio-demographic index and smoking preva-lence,the Spearman correlation coefficients were 0.264-0.357 and the population-weighted linear regression correlation coefficients were 0.346-0.497.In the DLNM,the maximum relative risk was 1.04(1.02-1.06)at LAN exposure of 8.6 with a 2.6-year lag time.After adjustment for socio-demographic index and smoking prevalence,the maximum relative risk was 1.05(1.02-1.07)at LAN exposure of 8.6 with a 2.4-year lag time.Conclusion:High LAN exposure was associated with increased lung cancer incidence,and this effect had a specific lag period.Compared with traditional individual-level studies,this group-level study provides a novel paradigm of effective,efficient,and scalable screening for risk factors.

Photoperiodic diapause in a subtropical population of Aedes albopictus in Guangzhou,China:optimized fieldlaboratory-based study and statistical models for comprehensive characterization

Background:Aedes albopictus is among the 100 most invasive species worldwide and poses a major risk to public health.Photoperiodic diapause provides a crucial ecological basis for the adaptation of this species to adverse environments.Ae.albopictus is the vital vector transmitting dengue virus in Guangzhou,but its diapause activities herein remain obscure.Methods:In the laboratory,yeast powder and food slurry were compared for a proper diapause determination method,and the critical photoperiod(CPP)was tested at illumination times of 11,11.5,12,12.5,13,and 13.5 h.A 4-parameter logistic(4PL)regression model was selected to estimate the CPP.In the field,the seasonal dynamics of the Ae.albopictus population,egg diapause,and hatching of overwintering eggs were investigated monthly,weekly,and daily,respectively.A distributed lag non-linear model(DLNM)was used to assess the associations of diapause with meteorological factors.Results:In the laboratory,both the wild population and the Foshan strain of Ae.albopictus were induced to diapause at an incidence greater than 80%,and no significant difference(P>0.1)was observed between the two methods for identifying diapause.The CPP of this population was estimated to be 12.312 h of light.In the field,all of the indexes of the wild population were at the lowest levels from December to February,and the Route Index was the first to increase in March.Diapause incidence displayed pronounced seasonal dynamics.It was estimated that the day lengths of 12.111 h at week2016,43 and 12.373 h at week2017,41 contributed to diapause in 50%of the eggs.Day length was estimated to be the main meteorological factor related to diapause.Conclusions:Photoperiodic diapause of Ae.albopictus in Guangzhou of China was confirmed and comprehensively elucidated in both the laboratory and the field.Diapause eggs are the main form for overwintering and begin to hatch in large quantities in March in Guangzhou.Furthermore,this study also established an optimized investigation system and statistical models for the study of Ae.albopictus diapause.These findings will contribute to the prevention and control of Ae.albopictus and mosquito-borne diseases.

Total and cause-specific mortality attributable to cold spells in China:A multicity and multicounty study

Climate change has resulted in an increase in the frequency and intensity of extreme cold weather events,but few multicity or multicounty researches have explored the association between cold spells and mortality risk and burden.We collected daily data on climate,sociodemo-graphic factors and mortality in 18 cities/counties across 11 geographical regions for the period of November to March 2014-2018.A distributed lag nonlinear model was used to examine the association between cold spells and mortality after adjustment for confounding factors.Twelve definitions of cold spells were used.Multi-meta regression analysis was applied to pool the impacts over different regions.Cold spells were significantly associated with all-cause mortality at lag 0-21(CRR:1.38,95%CI:1.21,1.57).In addition to respiratory diseases and cir-culatory system diseases,digestive,endocrine and nervous system diseases and injury were also affected by cold spells.The magnitude of the impacts of cold spells on mortality varied among the diseases investigated,with the highest risk estimate found for influenza and pneumonia(CRR:2.00,95%CI:1.45,2.76)and the lowest estimate found for injury(CRR:1.26,95%CI:1.09,1.46).The fraction of all-cause mortality attributable to cold spells was 2.31%(95%CI:0.90%,3.46%).Among the regional differences,the attributable burden of all-cause mortality was higher in rural areas and subtropical monsoon climate zone,with attributable fractions of 2.85%(95%CI:1.23%,4.11%)and 3.36%(95%CI:0.55%,5.35%),respectively.Cold spells increased mortality from a range of diseases.Women,older adults and residents of rural areas and subtropical monsoon climate zone were more vulnerable to cold spells impacts.The findings may help to formulate preventive strategies and early warning response plans to reduce mortality burden of extreme cold events.

An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa,Canada

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19,especially during times of limited availability in testing.Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions.The association is likely nonlinear and time-varying.This project employs a distributed lag nonlinear model(DLNM)(Gasparrini et al.,2010)to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa,Canada.We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concen-trations to COVID-19 hospitalizations.The expected reduction in hospitalization is adjusted for vaccination efforts.A correlation analysis of the data verifies that COVID-19 hospital-izations are highly correlated with wastewater viral signals with a time-varying rela-tionship.Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospi-talizations with wastewater viral signals.

The effects of the China–US trade war during 2018–2019 on the Chinese economy:an initial assessment

The China–US trade war during 2018–2019 has attracted attentions from academics,policy makers,businesses and investors around the world.Unlike previous researches which are mainly based on hypothetical scenarios,this study looks at the real effects of the China–US trade war on the Chinese economy.Based on either weekly or monthly data during January 2018–December 2019 including creatively using the Google Trends data to measure the severity of the trade war,this study examines the effects of the China–US trade war on Chinese Renminbi,China–US bilateral trade and stock markets.

Impact of meteorological factors on the incidence of childhood hand,foot,and mouth disease(HFMD)analyzed by DLNMs-based time series approach

Background:Hand,foot,and mouth disease(HFMD)has become an emerging infectious disease in China in the last decade.There has been evidence that meteorological factors can influence the HFMD incidence,and understanding the mechanisms can help prevent and control HFMD.Methods:HFMD incidence data and meteorological data in Minhang District,Shanghai were obtained for the period between 2009 and 2015.Distributed lag non-linear models(DLNMs)were utilized to investigate the impact of meteorological factors on HFMD incidence after adjusting for potential confounders of long time trend,weekdays and holidays.Results:There was a non-linear relationship between temperature and HFMD incidence,the RR of 5th percentile compared to the median is 0.836(95%CI:0.671-1.042)and the RR of 95th percentile is 2.225(95%CI:1.774-2.792),and the effect of temperature varied across age groups.HFMD incidence increased with increasing average relative humidity(%)(RR=1.009,95%CI:1.005-1.015)and wind speed(m/s)(RR=1.197,95%CI:1.118-1.282),and with decreasing daily rainfall(mm)(RR=0.992,95%CI:0.987-0.997)and sunshine hours(h)(RR=0.966,95%CI:0.951-0.980).Conclusions:There were significant relationships between meteorological factors and childhood HFMD incidence in Minhang District,Shanghai.This information can help local health agencies develop strategies for the control and prevention of HFMD under specific climatic conditions.

Ambient Temperature and Years of Life Lost:A National Study in China

Although numerous studies have investigated premature deaths attributable to temperature,effects of temperature on years of life lost(YLL)remain unclear.We estimated the relationship between temperatures and YLL,and quantified the YLL per death caused by temperature in China.We collected daily meteorological and mortality data,and calculated the daily YLL values for 364 locations(2013–2017 in Yunnan,Guangdong,Hunan,Zhejiang,and Jilin provinces,and 2006–2011 in other locations)in China.A time-series design with a distributed lag nonlinear model was first employed to estimate the location-specific associations between temperature and YLL rates(YLL/100,000 population),and a multivariate meta-analysis model was used to pool location-specific associations.Then,YLL per death caused by temperatures was calculated.The temperature and YLL rates consistently showed U-shaped associations.A mean of 1.02(95%confidence interval:0.67,1.37)YLL per death was attributable to temperature.Cold temperature caused 0.98 YLL per death with most from moderate cold(0.84).The mean YLL per death was higher in those with cardiovascular diseases(1.14),males(1.15),younger age categories(1.31 in people aged 65–74 years),and in central China(1.34)than in those with respiratory diseases(0.47),females(0.87),older people(0.85 in people R75 years old),and northern China(0.64)or southern China(1.19).The mortality burden was modified by annual temperature and temperature variability,relative humidity,latitude,longitude,altitude,education attainment,and central heating use.Temperatures caused substantial YLL per death in China,which was modified by demographic and regional characteristics.

Defining region-specific heatwave in China based on a novel concept of “avoidable mortality for each temperature unit decrease”

The distribution of temperature and temperature-health association varied largely across different regions in China,a region-specific definition for heatwave was therefore needed.We collected the data on daily mortality,meteorological factors and air pollution in 84 Chinese cities during 2013–2016,which was divided into seven regions.Based on the association between daily maximum temperature and mortality in each city in a threshold distributed lag non-linear model,where the threshold was defined as the temperature corresponding to the lowest mortality risk,we calculated the number of deaths that could be avoided for 1℃ decrease in maximum temperature under different thresholds,then a random-effect meta-analysis was used to generate regional results,in which the temperature with the highest avoidable mortality number for 1℃ decrease was considered as the most appropriate heatwave definition.We observed an immediate detrimental effect of high temperature within three lag days.Our analysis suggested to use 29.5℃,31.5℃,29.0℃,31.5℃,30.0℃,and 28.5℃ as the heatwave standard for east,north,northeast,central,south,and southwest region,with the avoidable mortality number of 1.54(95%Confidence interval(CI):0.88,2.19),0.55(95%CI:0.16,0.94),0.59(95%CI:0.32,0.86),1.14(95%CI:0.68,1.59),1.22(95%CI:0.54,1.90),and 0.78(95%CI:0.01,1.55),respectively,while the estimated number 0.19(95%CI:−0.02,0.40)in northwest region was not statistically significant.The concept of‘avoidable mortality for 1℃ decrease’was proposed to define the heatwave event,and varied maximum temperature between 28.5 and 31.5℃ was suggested for region-specific heatwave definition in China.

The Relationship between Interest Rates and Inflation:Examining the Fisher Effect in China

This study revisits the Fisher effect using a different empirical method that considers a potential nonlinear relationship between interest rates(treasury bond rates)and inflation in China.The rising uncertainty and asymmetric information in financial markets between bond holders and bond issuers suggest such a potential nonlinear relationship.To this aim,we apply Shin et al.'s(2014)nonlinear autoregressive distributed lag(NARDL)model with asymmetric dynamic multipliers for the sample period 2002M7-2018M4.The empirical findings reveal symmetric and asymmetric partial Fisher effects for all sample bond rates in China.Furthermore,we find that 20-year bond rates experience the lowest partial Fisher effect.