An Investigative Study of Employed Population Forecast Approaches in Nigeria
DOI:
https://doi.org/10.59573/emsj.8(6).2024.13Ключевые слова:
Population, Forecast, ARIMA model, Linear Regression, Log-Linear Regression, Exponential Regression, Cohort Component, ProjectionАннотация
Inarguably, the choice of the appropriate method to use for country population projection still remains one major factor to consider in production of precise population forecast of country. This study aims at providing an investigative study of population forecast approaches of the yearend population in Nigeria using the National Population Commission population projection method (i.e. the cohort component) as based comparison. Precisely this study empirically examined the performances of five population projection approaches namely ARIMA, Linear Regression, Log-Linear Regression, Exponential Regression and Cohort Component methods in modelling and forecasting the total Nigeria population using the nation population data spanning from 2006 to 2022. The empirical results established ARIMA(1,2,0) with lowest AIC and returned the model as the most parsimonious ARIMA model. Also, the empirical results revealed , and as the estimated Linear Regression, Log-Linear Regression and Exponential Regression model respectively. Similarly, a Cohort Component model was fitted for the total population. Results from four accuracy measure criteria i.e., RMSE, MSE, MAE and MAPE criteria established that the estimated Exponential Regression with the minimum accuracy values across the four (4) evaluation criteria. This study concludes that Exponential Regression based population projection approach outperformed the ARIMA, Linear Regression, Log-Linear Regression and Cohort Component methods, thus Exponential Regression based population projection approach is more robust and efficient to project the Nigeria total population for the examined period. This study recommended Exponential Regression based extrapolation approach to be employed over the conventional Cohort Component approach for modelling and predicting total population of a country over time.
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