This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The new methods are based on the row, column and overall averages of time series data arranged in a Buys-Ballot table with m rows and s columns. The methods assume that 1) only one value is missing at a time, 2) the trending curve may be linear, quadratic or exponential and 3) the decomposition method is either Additive or Multiplicative. The performances of the methods are assessed by comparing accuracy measures (MAE, MAPE and RMSE) computed from the deviations of estimates of the missing values from the actual values used in simulation. Results show that, under the stated assumptions, estimates from the new method based on full decomposition of a series is the best (in terms of the accuracy measures) when compared with other two new and the existing methods.
This study modeled the US Dollar and Nigerian Naira exchange rates during COVID-19 pandemic period using a classical statistical method – Autoregressive Integrated Moving Average (ARIMA) – and two machine learning methods – Artificial Neural Network (ANN) and Random Forest (RF). The data were divided into two sets namely: the training set and the test set. The training set was used to obtain the parameters of the model, and the performance of the estimated model was validated on the test set that served as new data. Though the ARIMA and random forest performed slightly better than the neural network in the training set, their performance in the test set was poor. The neural network with 5 nodes in the input layer, 5 nodes in the hidden layer and 1 node in the output layer (ANN (5,5,1)) performed better on the new data set (test set) and is chosen as the best model to forecast for future USD to NGN exchange rate. The information from the high-performance model (ANN (5, 5, 1)) for modeling the USD to NGN exchange rate will assist econometric trading of the currencies and offer both speculative and precautionary assistance to individuals, households, firms and nations who use the currencies locally and for international trade.
Marriage to first birth interval is used as a measure of the fertility rate of married women and it is known to be influenced by some factors. In this paper, we studied the effect of various socio-demographic and cultural factors on the interval between marriage and first birth in Nigeria using the 2018 Nigerian Demographic and Health Survey data. The analysis was done using the accelerated failure time (AFT) model and the Kaplan-Meier plot. The result showed that on the average, the interval of marriage to first birth is 23.90 months. It means that it takes a Nigerian couple about 2 years (24 months) to give birth to their first child. The results showed that women who married early have longer first birth interval than those that married late. Muslims have longer first birth interval than Christians. Women from North East, North West and South South have longer first birth interval than women in North Central while those from South West have shorter first birth interval than those from North Central. Residence has no statistical significant effect at 0.05 alpha level on the marriage to first birth interval. Women with primary and secondary education have shorter first birth interval than women with no education while the husband's education has no statistical significant effect at 0.05 alpha level on the first birth interval. Women with early first sexual intercourse have shorter first birth interval than those with late first sexual while women that have ever used traditional and modern contraceptive method have shorter first birth interval than women that have not used any contraceptive method. There should be targeted orientation on sexual and reproductive matters to those with higher first birth interval so that desirable conditions would be yielded by controlling these groups.
KeywordsAccelerated failure time model • First birth interval • Kaplan-Meier plot • Cox model • Socio-demographic and cultural factors
COVID-19 has remained and continued to be a severe pandemic threatening the present and future health stability of all the countries, the West African Countries inclusive. The challenge to avert the threat by modeling the reported cases in each of these West African Countries becomes needful for future planning and a K. Ayinde (B) •
Multicollinearity has remained a major problem in regression analysis and should be sustainably addressed. Problems associated with multicollinearity are worse when it occurs at high level among regressors. This review revealed that studies on the subject have focused on developing estimators regardless of effect of differences in levels of multicollinearity among regressors. Studies have considered single-estimator and combined-estimator approaches without sustainable solution to multicollinearity problems. The possible influence of partitioning the regressors according to multicollinearity levels and extracting from each group to develop estimators that will estimate the parameters of a linear regression model when multicollinearity occurs is a new econometrics idea and therefore requires attention. The results of new studies should be compared with existing methods namely principal components estimator, partial least squares estimator, ridge regression estimator and the ordinary least square estimators using wide range of criteria by ranking their performances at each level of multicollinearity parameter and sample size. Based on a recent clue in literature, it is possible to develop innovative estimator that will sustainably solve the problem of multicollinearity through partitioning and extraction of explanatory variables approaches and identify situations where the innovative estimator will produce most efficient result of the model parameters. The new estimator should be applied to real data and popularized for use.
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