Empirical Analysis of Agricultural Growth and Unemployment in Nigeria

Unemployment which has been identified as the major cause of poverty is a worldwide economic problem. Poverty alleviation has been a great concern to developing countries. The economic burden of unemployment on a society necessitates this study. Consequently, this study analyses the Nigerian agricultural growth rate, its contributions, and examines the linkage and dimension of agricultural growth and unemployment rates. Collected time series data were analyzed with the aid of t – test, Duncan Multiple Range test, Granger Causality test and regression analysis. Results showed that Nigerian agricultural growth rate has an inverse relationship with unemployment and re – establish the Cobweb supply theory. In addition, increase in agricultural growth decrease unemployment and thus can alleviate poverty. Consequently, recommending polices to alleviate poverty should focus on increasing agricultural growth.Cobweb supply theory, Granger Causality test, Nigeria, Unemployment, Agricultural and Food Policy, Community/Rural/Urban Development, Environmental Economics and Policy, Farm Management, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, International Relations/Trade, Marketing, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,

DETERMINANTS OF INFLATION IN NIGERIA: A CO- INTEGRATION APPROACH

Inflation is undeniable one of most leading and dynamics macroeconomics issues confronting almost all economies of the world. Its dynamism has made it an imperative issue to be considered. Hence the study examines the factors affecting inflation in Nigeria. Time series data were employed for the study. The data was sourced from the Central Bank of Nigeria and National Bureau of Statistics. Descriptive statistics and cointegration analysis were the analytical tools used. It was observed that there were variations in the trend pattern of inflation rate. Some of the variables considered were significant in determining inflation in Nigeria. The previous total export was found to have a negative impact on current inflation while the previous total import exerts a positive effect likewise the food price index. It has thus been recommended that policies that will set the interest rate to a level at which it will encourage investment and increase in production level could be institutionalized, importation should be reduced in Nigeria such that it will not encourage change of consumer taste resulting to inflating prices, exchange rate system should be maintained at a level that will not impose threat on the Nigeria economy and the domestic consumption of petroleum product should be focused, not only exportation.Financial Economics,

Analysis of determinants of maize price variations in Nigeria (1978 - 2014)

Skyrocketing prices of food staples such as maize can lead to inefficient agricultural production and definitely have detrimental effects on the economic, social, and political growth of any country. Most studies on maize in Nigeria are focused on the increasing consumption or competitiveness, very few address the determinants of maize price change as a panacea for the increase of productivity. Filling this gap requires a study on the various factors that contribute to the variations in the price of maize. In this study, secondary data were used. The study used descriptive statistics tools to analyze the pattern of price variations and changes in the production of maize over a period of 36 years in Nigeria. Also, various factors affecting price variation of maize were examined. It was recommended that the positive and significant impact of country’s population to maize price change should serve as an impulse to encourage investment in agricultural sector of Nigeria in order to ensure food security in the country. Also, the government should use the inflation measures to regulate prices of maize in the country

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

Prediction Using Estimators of Linear Regression Model with Aurocorrelated Error Terms and Correlated Stochastic Uniform Regressors

Prediction remains one of the fundamental reasons for regression analysis. However, the Classical Linear Regression Model is formulated under some assumptions which are not always satisfied especially in business, economic and social sciences leading to the development of many estimators. This work, therefore, attempts to examine the performances of the Ordinary Least Square estimator (OLS), Cochrane-Orcutt estimator (COR), Maximum Likelihood estimator (ML) and the estimators based on Principal Component analysis (PC) in prediction of linear regression model under the violations of assumption of non – stochastic regressors, independent regressors and error terms. With stochastic uniform variables as regressors, Monte - Carlo experiments were conducted over the levels of autocorrelation, correlation between regressors (multicollinearity -) and sample sizes, and best estimators for prediction purposes are identified using the goodness of fit statistics of the estimators. Results show that the performances of COR and ML at each level of multicollinearity over the levels of autocorrelation have a convex – like pattern while that of OLS, PR1 and PR2 are concave – like. Also, as the level of multicollinearity increases the estimators especially the COR and ML estimators perform much better at all the levels of autocorrelation. Furthermore, results show that except when the sample size is small (n=10), the performances of the COR and ML estimators are generally best and almost the same, even though at low level of autocorrelation the PC estimator either performs better than or competes with the best estimator when  and . When the sample size is small (n =10), the COR estimator is best except when the autocorrelation level is low and  or. At these instances, the PR2 estimator is best. Moreover, at low level of autocorrelation in all the sample sizes, the OLS estimator competes with the best estimator in all the levels of multicollinearity. .Keywords: Prediction, Estimators, Linear Regression Model, Autocorrelation, Multicollinearity                     

Two Stage Robust Ridge Method in a Linear Regression Model

Two Stage Robust Ridge Estimators based on robust estimators M, MM, S, LTS are examined in the presence of autocorrelation, multicollinearity and outliers as alternative to Ordinary Least Square Estimator (OLS). The estimator based on S estimator performs better. Mean square error was used as a criterion for examining the performances of these estimators

Effect of correlations and equation identification status on estimators of a system of simultaneous equation model

In simultaneous equations model, multicollinearity and status of identification of the equations have been observed to influence estimation of the model parameters. The error terms of each equation in the model are also expected to be correlated with each other. This study therefore examined the effect of multicollinearity, correlation between error terms and status of identification of equations on six methods of parameter estimation in a simultaneous equations model using Monte Carlo approach. A two equation model, with one equation exactly identified and the other over identified, was considered. The levels of multicollinearity among the exogeneous variables were specified as r = 0.3, 0.6, 0.8, 0.9 and 0.99 and that of correlation between error terms as l = 0.3, 0.6, and 0.9. A Monte Carlo experiment of 250 trials was carried out at three sample sizes (20, 50 and 100). The six estimation methods; Ordinary Least Squares (OLS), Indirect Least Squares (ILS), Limited Information Maximum Likelihood (LIML), Two Stage Least Squares (2SLS), Full Information Maximum Likelihood (FIML) and Three Stage Least Squares (3SLS); were ranked according to their performances. Finite properties of estimators’ criteria namely bias, absolute bias, variance and mean squared error were used for comparing the methods. An estimator is best at a specified level of multicollinearity, correlation between error terms and sample size if it has minimum total rank over the model parameters and the criteria. Results show that the OLS estimator is best in estimating the parameters of the exactly identified equation at severe level of multicollinearity (r®1) at all sample sizes. At other levels of multicollinearity, the best estimator is FIML or 3SLS except when the correlation between error terms is low (l = 0.3). At this instance, the best estimators are LIML and 2SLS. The parameters of over identification model are best estimated with FIML or 3SLS at all levels of multicollinearity, correlation between error terms and at all sample sizes.

Effect of Multicolinearity and Autocorrelation on Predictive Ability of Some Estimators of Linear Regression Model

Violation of the assumptions of independent regressors and error terms in linear regression model has respectively resulted into the problems of multicollinearity and autocorrelation. Each of these problems separately has significant effect on parameters estimation of the model parameters and hence prediction.  This paper therefore attempts to investigate the joint effect of the existence of multicollinerity and autocorrlation on Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis on prediction of linear regression model through Monte Carlo studies using the adjusted coefficient of determination goodness of fit statistic of each estimator. With correlated normal variables as regressors, it further identifies the best estimator for prediction at various levels of sample sizes (n), multicollinearity  and autocorrlation . Results reveal the pattern of performances of COR and ML at each level of multicollinearity over the levels of autocorrelation to be generally and evidently convex especially when  and while that of OLS and PC is generally concave. Moreover, the COR and ML estimators perform equivalently and better; and their performances become much better as multicollinearity increases. The COR estimator is generally the best estimator for prediction except at high level of multicollinearity and low levels of autocorrelation. At these instances, the PC estimator is either best or competes with the COR estimator. Moreover, when the sample size is small (n=10) and multicollinearity level is not high, the OLS estimator is best at low level of autocorrelation whereas the ML is best at moderate levels of autocorrelation. .Keywords: Prediction, Estimators, Linear Regression Model, Multicollinearity, Autocorrelation

Monte Carlo Study of Some Classification-Based Ridge Parameter Estimators

Ridge estimator in linear regression model requires a ridge parameter, K, of which many have been proposed. In this study, estimators based on Dorugade (2014) and Adnan et al. (2014) were classified into different forms and various types using the idea of Lukman and Ayinde (2015). Some new ridge estimators were proposed. Results shows that the proposed estimators based on Adnan et al. (2014) perform generally better than the existing ones

Effect of Correlations on Type 1 Error Rates of Some Multivariate Normality Tests

Normality assumption of multivariate data is a prerequisite to the use of multivariate statistical data analysis methods before inference could be valid and reliable. Tests developed to validate this assumption including Doornik-Harsen (DH), Shapiro-Francia (SF), Mardia Skewness (MS), Mardia Skewness for small sample (MSS) and Kurtosis (MK), Skewness (S) and Kurtosis(K), Shapiro-Wilk(SW), Royston (R), Desgagne-Micheaux (DM), Henze-Zirkler (HZ), Energy (E), Gel-Gastwirth (GG) and Bontemps-Meddahi (BM) tests often result into different conclusions. These differences can be misleading. Consequently, this paper examined the effect of correlations on the Type 1 error rates of multivariate tests of normality. Monte Carlo experiments were conducted one thousand (1000) times taking into consideration the dimensions, correlations and sample sizes of the multivariate data. A test is affected by correlation if its estimated Type 1 error rate changes as correlation changes. A test is considered good if its estimated error rate approximates the true error rate and best if the number of times it approximates the estimated error rate when counted over the levels of correlations, sample sizes and levels of significance is the highest, the mode. Results show that Type 1 error rates of DH, SF, SW, R, DM, GG and BM tests are affected by correlations and are relatively not good; where as the Type 1 error rates of HZ, MS, MK, MSS, S, K and E tests are not only unaffected by correlations but are also relatively good. Consequently, MS, R, MSS, HZ and E tests have good Type 1 error rates but that of E and HZ tests are best. They are therefore recommended for practitioners