Robustness of Some Estimators of Linear Model with Autocorrelated Error Terms When Stochastic Regressors are Normally Distributed

Performances of estimators of the linear model under different level of autocorrelation (ρ) are known to be affected by different specifications of regressors. The robustness of some methods of parameter estimation of linear model to autocorrelation are examined when stochastic regressors are normally distributed. Monte Carlo experiments were conducted at both low and high replications. Comparison and preference of estimator(s) are based on their performances via bias, absolute bias, variance and more importantly the mean squared error of the estimated parameters of the model. Results show that the performances of the estimators improve with increased replication. In estimating all the parameters of the model, the Ordinary Least Square (OLS) estimator is more efficient than any of the Generalized Least Square (GLS) estimators considered when − 0.25 \u3c ρ ≤ 0.25; and the Maximum Likelihood (ML) and the Hildreth and LU (HILU) estimators are robust

Performances of some estimators of linear model with autocorrelated error terms when regressors are normally distributed

Different specification of regressors and levels of autocorrelation are known to affect the performances of estimators of linear model with autocorrelated error terms. In this paper, we examined the performances of the ordinary least square (OLS) and four feasible generalized least estimators namely; Cochrane Orcut (CORC), Hidreth – Lu (HILU), Maximum Likelihood (ML), Maximum Likelihood Grid (MLGD) when regressors are normally distributed at various levels of autocorrelation and sample size through Monte – Carlo studies. The estimators are compared by examing the finite properties of estimators namely; sum of biases, sum of absolute biases, sum of variances and sum of the mean squared error of the estimated parameter of the model. Results show that when the autocorrelation level is small (&#961=0.4), the MLGD estimator is best except when the sample size is large (n=80) where the CORC estimator is best. When autocorrelation is high (&#961=0.8), the CORC estimator is best except when the sample size is small (n=80) where the ML estimator is best. When autocorrelation is very high (&#961=0.9), the HILU estimator is best except when the sample size is large where the CORC estimator is best. Furthermore, when the autocorrelation level tends to unity (&#961 &#8594 1), the HILU estimator is best in all the sample sizes. IJONAS Vol. 3 (1) 2007: pp.22-2

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

Time Series Model for Predicting the Mean Death Rate of a Disease

This study develops a time series model to estimate the mean death rate of either an emerging disease or re-emerging disease with a bilinear induced model. The estimated death rate converges rapidly to the true parameter value for a given mean death at time t. The derived model could be used in predicting the m-step future death rate value of a given disease. We illustrated the new concept with real life data