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The Paradox of Population Growth in National Development.
41st Inaugural Lecture of the University of Abuja, Nigeria.
(2023-06-01) MUNDI, RHODA
The 41st inaugural lecture of University of Abuja, Nigeria was delivered by Prof. Rhoda Mundi, Professor of Geography. The lecture focuses on the paradox of population growth in national development, drawing a lot from inaugural lecturer’s lifelong research and experiences. The lecture is divided into sections as follows: Introduction, Definition of terms and conceptual clarifications, History of world population growth, The debate on Population growth, Theories on population and development interrelations, Population growth in Nigeria, Reasons for high, population growth, Implication of Nigeria's high population growth, Population growth and development in Nigeria, Population Policies, and Challenges to Nigeria's Development efforts, Recommendations, The lecture conclusions that people are resources, creators and managers of wealth especially when developed through education, training and knowledge transfer and when healthy. Nigeria's large population is as asset however, the high population growth is likely to be detrimental in the short and medium term because of inadequate investment in it particularly through bad governance. If properly developed and kept healthy, the population in the longer run would result in demographic dividends as the expansive young people become productive adults. This is the paradox of population growth in National Development.
SMOOTHING SPLINE TECHNIQUE FOR TIME SERIES DATA WITH AUTOCORRELATION
(Lambert Academic Publishing, 2023-03-10) Adams, Samuel Olorunfemi
Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of smoothing parameter lambda. Most of the existing smoothing methods applied to time series data tend to overfit in the presence of autocorrelated errors. The aim of this study is to propose a smoothing method which is the arithmetic weighted value of Generalized Cross-Validation (GCV) and Unbiased Risk (UBR) methods The objectives of the study were to (i) determine the best-fit smoothing method for the time series observation; (ii) identify the best smoothing method that does not overfit timeseries data when autocorrelation is present in the error term;
(iii)establish the optimum value of the proposed smoothing method; (iv) compare GCV, GML and UBR smoothing methods to the proposed smoothing methods in terms of sample size; and (v)test the results of simulation using real life-data. A hybrid smoothing method of the Generalized Cross-Validation (GCV) and Unbiased Risk (UBR) was developed by adding the weighted values of Generalized CrossValidation (GCV) and Unbiased Risk (UBR). The Proposed Smoothing Method (PSM) was compared with Generalized Maximum Likelihood (GML), GCV and UBR smoothing methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60 and 100), three levels of the autocorrelation (02, 05 and 08), and four degrees of smoothing (1, 2, 3 and 4) Real-life data on Standard international Trade Classification (SITC) export and import price indices in Nigeria between 1970 2018 extracted from CBN 2019 edition were also used. The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that:(i)for a time series observation with autocorrelated errors, Ǥ ሺ ൌͲͲሻ ൌ ͳ ሺ ሻൈሺ ሻ ൌͲͻͳǡ provides the besfit smoothing method for the model: (ii)he PM does not over-fit data at all the autocorrelation levels considered (ͲǤʹ Ǥͷ ǤͺሻǢ (iii) t optium value of the PSM was at the weighted value of 0.04, with the eqtion is given as ሺ ሻ ൌ ሺͲͲͶሻ ! ሾ$ ሺ &' ሻሿ( ሺͲͻሻ *+") (% &' (,) * ሼ% &' ሻሽ- (; (i wh thee is autocorrelation in the error term, PSM performed better than the GCVGML and UBR smoothing methods were considered at all-time series sizes (T =20, 60 d 100); (v) for the real-life data employed in the study, PSM proved to be the most efficit among the GCV, GML, PSM and UBR smoothing methods compare. The study concluded that the PSM method provides the best-fit as a smoothing method, works well atutocorrelation levels (=0.2, 0.5 and 0.8), and does not overfit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autorrelation in the error term and econometrics real-life data. This study can be applied to; non parametric regression, non – parametric forecasting, spatial, survival and econometrics
The Efficiency of the Proposed Smoothing Method over the Classical Cubic Smoothing Spline Regression Model with Autocorrelated Residual
(Science Publications, 2023-03-18) Adams, Samuel Olorunfemi
Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to over fit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.
Librarians Use of Information Literacy Strategic Tools for Teaching Students in Nigeria Universities to Combat Fake News and Misinformation
(2022-09-09) Anyaoku, Ebele N
The research assessed the extent of use of Information literacy tools by librarians in Nigeria for teaching undergraduate students' skills to combat fake news. The research method was document analysis. The research analysed forty-four course materials currently used by Librarians for teaching an information literacy course coded GST 121 (Use of Library, Study Skills and Information Communication Technology. The research also examined library websites of 20 universities in Nigeria to determine availability of fake news content. Findings show there were no module or chapter on fake news and misinformation in the 44 course materials reviewed. Equally, there was no content on fake news on the library websites. However, the course materials included media and information literacy modules which are underlining competencies that are important in guarding against fake news and misinformation. The librarians provided instructional units on online bibliographic databases in more than half (56.8%) of the course materials. There were added units on database literature search (38.6%) and search engine information search (27.6%). Only a small percentage had units on evaluating information (11.4%). It is recommended that librarians should review the GST 121 curriculum and include distinct modules on evaluating information, fact-checking, spotting and combating fake news and misinformation.
Modeling and Forecasting Cryptocurrency Returns and Volatility: An Application of GARCH Models
(Scientific Publications, 2022-11-10) Adams, Samuel Olorunfemi
The future of e-money is crypocurrencies, it is the decentralize digital and virtual currency that is secured by cryptography. It has become increasingly popular in recent years attracting the attention of the individual, investor, media, academia and governments worldwide. This study aims to model and forecast the volatilities and returns of three top cryptocurrencies, namely; Bitcoin, Ethereum and Binance Coin. The data utilized in the study was extracted from the higher market capitalization at 31st December, 2021 and the data for the period starting from 9th November, 2017 to 31st December 2021. The Generalised Autoregressive conditional heteroscedasticity (GARCH) type models with several distributions were fitted to the three cryptocurrencies dataset with their performances assessed using some model criterion tests. The result shows that the mean of all the returns are positive indicating the fact that the price of this three crptocurrencies increase throughout the period of study. The ARCH-LM test shows that there is no ARCH effect in volatility of Bitcoin and Ethereum but present in Binance Coin. The GARCH model was fitted on Binance Coin, the AIC and log L shows that the CGARCH is the best model for Binance Coin. Automatic forecasting was perform based on the selected ARIMA (2,0,1), ARIMA (0,1,2) and the random walk model which has the lowest AIC for ETH-USD, BNB-USD and BTC-USD respectively. This finding could aid investors in determining a cryptocurrency's unique risk-reward characteristics. The study contributes to a better deployment of investor’s resources and prediction of the future prices the three cryptocurrencies.