Comparative Analysis of Market Efficiency and Volatility of Energy Prices Before and During COVID-19 Pandemic Periods

The Covid-19 pandemic has affected energy demand and pricing globally due to different lockdown measures embarked on by governments in different economies. As a result, prices of oil and petroleum products dropped drastically at the peak of the pandemic period. The present paper, therefore, investigates the effect of the pandemic on energy markets and compared the levels of market efficiency, volatility, and volatility persistence. Two 5-monthly daily data windows are considered, each for the period before and during the pandemic, and an updated nonlinear fractional integration approach in time series analysis is employed. Having considered prices of Crude oil, Gasoline, Diesel, Heating oil, Kerosene, and Propane from US markets, we find that energy markets are less efficient during the Covid-19 pandemic period, even though with higher volatility but with lesser volatility persistence compared to the period before the pandemic. Thus, volatility shocks last for a shorter period during the 5-month pandemic period than in the 5-month period that precedes the pandemic. It is hoped that the findings of this work will be of interest to oil marketers and administrators in the international oil markets

Bayesian estimation of simultaneous equation model with lagged endogenous variables and first order serially correlated errors

Most simultaneous equation models involve the inclusion of lagged endogenous and/or exogenous variables and sometimes it may be misleading to assume that the errors are normally distributed when in reality they exhibit functional formsthat are not normal especially in practical situations. The classical methods of estimating parameters of simultaneous equation models are usually affected by the presence of autocorrelation among the error terms. Unfortunately, in practice the form of correlation between the pairs of the random deviates is unknown.In this paper classical and Bayesian methods for the estimation of simultaneous equation model withlagged endogenous variables and first order serially correlated errors are considered. The smallsample properties of the methods at different levels of correlation for ρ = 0.2, 0.5 and 0.8are compared.Better parameter estimates were produced by the Bayesian estimator with smaller standard errors compared to the classical method. The standard deviations of the Bayesian estimator are consistently better than those of the OLS estimator for the sample sizes considered. For example, the standard deviations of the Bayesian for b14 (the coefficient of the lagged endogenous variable,y 1t-1) when ρ = 0.2 at N = 10, 15, 20 and 25 were 0.07712781, 0.05433923, 0.03230012 and 0.03177252 respectively while those of OLS were 0.0784732, 0.4718914, 0.05701936 and 1.31422868. However, when ρ = 0.8, the standard deviations were 0.0548055, 0.03860254, 0.02572899 and 0.02126175 for Bayesian and 0.0562190, 0.03882345, 0.053676 and 0.0315632 for OLS. Interestingly, notice that even at high correlation level, the estimates produced by the Bayesian method are closer to the parameter values and the standard deviations decrease as the sample size increases. Hence, the Bayesian estimation method might be a better choice when lagged endogenous variables are included in a simultaneous equation model with auto-correlated disturbances since it appeared to give better results compared to the classical approach.Keywords: Bayesian estimation, Lagged endogenous variables, Simultaneous equations, Monte-Carlo Simulation, First-order autoregressive process

Spatial patterns and determinants of fertility levels among women of childbearing age in Nigeria

Background: Despite aggressive measures to control the population in Nigeria, the population of Nigeria still remains worrisome. Increased birth rates have significantly contributed to Nigeria being referred to as the most populous country in Africa. This study analyses spatial patterns and contributory factors to fertility levels in different states in Nigeria. Method: The 2013 Nigerian Demographic Health Survey (NDHS) data were used to investigate the determinants of fertility levels in Nigeria using the geo-additive model. The fertility levels were considered as count data. Negative Binomial distribution was used to handle overdispersion of the dependent variable. Spatial effects were used to identify the hotspots for high fertility levels. Inference was a fully Bayesian approach. Results were presented within 95% credible Interval (CI). Results: Secondary or higher level of education of the mother, Yoruba ethnicity, Christianity, family planning use, higher wealth index, previous Caesarean birth were all factors associated with lower fertility levels in Nigeria. Age at first birth, staying in rural place of residence, the number of daughters in a household, being gainfully employed, married and living with a partner, community and household effects contribute to the high fertility patterns in Nigeria. The hotspots for high fertility in Nigeria are Kano, Yobe, Benue, Edo and Bayelsa states. Conclusion: State-specific policies need to be developed to address fertility levels in Nigeria. (Full text of the research articles are available online at www.medpharm.tandfonline.com/ojfp) S Afr Fam Pract 2017; DOI: 10.1080/20786190.2017.129269

Spatial pattern and determinants of unmet need of family planning in Nigeria

Background: Nigeria still grapples with low family planning (FP) use and a high fertility rate. This study explores the factors associated with the unmet need for FP and the coldspots of unmet need for FP in Nigeria.Methods: The 2013 Nigerian Demographic Health Survey (NDHS) data was used to investigate the unmet need for FP in Nigeria. A geo-additive model was specified to simultaneously measure the fixed, nonlinear, spatial and random effects inherent in the data. The fixed effect of categorical covariates was modelled using the diffuse prior, the nonlinear effect of continuous variable was modelled using the P-spline with second-order random walk, the spatial effects followed Markov random field priors while the exchangeable normal priors were used for the random effect of the community. The binomial distribution was used to handle the dichotomous nature of the dependent variable.Results: North East (OR: 1.8404, CI: 1.6170, 2.0941), North West (OR: 1.1145, CI: 1.1454, 1.1789), primary education (OR: 1.0441, CI: 0.9680, 1.1286), Hausa (OR: 2.7031, CI: 2.3037, 3.1513), birth interval greater than 12 months (OR: 1.0909, CI: 1.0447, 1.1379), community (OR: 1.6733, CI: 1.5261, 1.7899) and states (OR: 6.0879, CI: 2.5995, 29.6274) significantly increased the unmet need for FP.Conclusion: The unmet need for FP in Nigeria is positively associated with the Northern region, low level of education and birth interval