Economic relationships are often modelled without consideration of a possible regime switch, the transmission from one regime to another and the duration of stay in a particular regime which are not captured by linear models. This study aimed to model and estimate the interdependence existing among Nigeria’s International Trade and Macroeconomic Stability. Specifically, this study sought to estimate and compare the estimated Models, select the best Model and determine the probabilities of stay, the expected duration of stay in a particular regime. The study adopted a quasi-experimental design. Time series data on the study variables from January 2000 to June 2019 were obtained from the Statistical Bulletin of the Central Bank of Nigeria. Models were specified accordingly, the statistical analyses were carried out using the Markov Switching Intercept Vector Autoregressive Models, the pre and post-diagnostic tests were also conducted. The unit root test results showed I (1). VAR lag length selection criteria choose lag 2. The MS-VAR analysis identified two regimes (expansion and contraction), the information criteria selected the Markov-Switching Intercept Autoregressive Heteroschedastic 2 Variance Auto-regression 2 [MSIARH (2) - VAR (2)]. The MS-VAR results in regime 1 showed that lags 1 and 2 of total export significantly affected total export and total import, Lags 1 and 2 of total import had significant effects on exchange rate while lags 1 of exchange rate and lags 1 and 2 of exchange rate had significant effects on inflation rate. In Regime 2, lag 1 of total export and lag 2 of exchange rate had significant effects on total export. Only lag 2 of inflation rate had significant effects on exchange rate while lag 2 of total export and lags 1 and 2 of exchange rate had significant effects on the inflation rate. The results also showed an 89% probability of staying in regime 1 for a duration of 8 months 8 days and 57% probability of staying in regime 2 for 2 months 10 days. It was concluded that the MSIARH (2) - VAR (2). It was recommended that the right-hand side variables should be tested for endogeneity before concluding on single or system equation. It was also recommended that the possibility of regimes should be verified before concluding on linear or nonlinear models.

Communicable diseases are a major health challenge for the world. However, their negative impacts are felt most in Africa. This panel data study investigates the effect of communicable diseases and health expenditure on the economy. Gross Domestic Product (GDP) and current health expenditure are used as proxies for economic performance and health expenditure, respectively. Incidence of Tuberculosis, prevalence of Human Immunodeficiency Virus (HIV), and adults living with HIV (15 years - above) are the health indicators used in the study. Data for a period of ten years: 2007 to 2016 were collected from seven African countries in low and middle-income countries, according to World Health Organization (WHO) income groupings. Low-income countries are Gambia, Sierra Leone, and Togo, while Egypt, Ghana, Nigeria, and South Africa are middle-income countries. The three analytical panel data models; namely: Pooled Ordinary Least Squares Model (POLS), Fixed Effects Model (FEM) and Random Effects Model (REM) were used. Model selection tests were also performed, using the F Ratio Test, the Breush-Pagan Langrange Multiplier Test, and the Hausman Test, to choose the model that best describes the data. The results of the model selection tests show that the FEM is the most appropriate model for the data; therefore, the result of the FEM is used to interpret the impact of communicable diseases on the economy. First, the FEM analysis generally showed that HIV prevalence has a statistically significant negative effect on GDP, which is consistent with the existing literature. On the other hand, the incidence of tuberculosis and adults living with HIV have statistically positive effect. The result also shows that current health expenditure per capita is positively correlated with GDP, which implies that a unit increase in current health expenditure would lead to an increase of 961 units in GDP, based on the data used. Second, an additional analysis conducted in FEM to determine the effect of the variables in each country reveal that adults living with HIV and HIV prevalence have a statistically significant negative effect on economic performance. In conclusion, communicable diseases are an impediment to economic growth. The prevention and control of these diseases is a step in the right direction towards improving economic performance.

This study focuses on Monte Carlo Methods in parameter estimation of production function. The ordinary least square (OLS) method is used to estimate the unknown parameters. The Monte Carlo simulation methods are used for the data generating process. The Cobb-Douglas production model with multiplicative error term is fitted to the data generated. From tables 1.1 to 1.3, the mean square error (MSE) of 1 are 0.007678, 0.001972 and 0.001253 respectively for sample sizes 20, 40 and 80. Our finding showed that the mean square error (MSE) value varies with the sum of the powers of the input variables.

Time series analysis of Nigerian Inflation rate series is done. A seasonal difference and then a non-seasonal one were obtained. The correlogram of the differenced series revealed a seasonal nature. It also revealed a seasonal moving average component and a non-seasonal autoregressive component. A(5, 1, 0) × (0, 1, 1) 12 seasonal model was fitted and shown to be adequate.

A measure known as leaf rectangularity index (LRI) is estimated by means of bootstrap regression. The index, it is envisaged, will assist in discussing the geometry of leaf surfaces, if possible among different plants and across species. This paper considers a survey on previous works on the index and suggests possible areas of applications and collaborative research.