Researcher every so often model economic relationship using linear models. Even when non-linear models are used, they hardly consider the possibility of regimes, the transmission from one regime to another and also the duration of stay in a particular regime. These are not captured by linear models. To address this, we applied the Markov-Switching Mean Vector Autoregressive  Model to model and estimate the interdependence between macroeconomic variables (International Trade and Macroeconomic Stability) within the context of the Nigerian economy. Specifically, the study analyzed the trends of total export, total import, exchange rate, and inflation rate in Nigeria within the study period, Estimated MSM-VAR models on the study variables and selected the best model using information criteria. The study empirically investigated the interdependence existing among the study variables, determined the probabilities of transition from one regime to another and the duration of stay in a regime. The study ascertained the relative significance of each random innovation in affecting the study variables. Monthly time series data 246 months spanning through January 2000 to June 2020 was collected form the Central Bank of Nigeria Statistical Bulletin. The study used the Markov-Switching Mean Vector Autoression in the Analysis. The results showed that all variables were stationary at first difference. The study chose 2 regimes and the model selection criteria selected [MS(2)-MVAR(2)]. The variables were largely self-explanatory and very strongly exogenous.

Aims: The aim of this work is to develop suitable ARIMA models which can be sued to forecast daily confirmed/death cases of COVID-19 in Nigeria. This is subject to developing the model, checking them for suitability and carrying out eight months forecast, and making recommendations for the Nigerian Health sector. Study Design:  The study used daily confirmed and death cases of COVID-19 in Nigeria. Methodology: This work covers times series data on the on the daily confirmed/death cases of COVID-19 in Nigeria, obtained from the Nigerian Centre for Disease Control (NDCD) from 21 March 2020 to 5 May 2020, covering a total of 51 data points. This work  is geared towards developing a suitable  Autoregressive Integrated Moving Average (ARIMA) models which can be used to forecast total daily confirmed/death cases of COVID-19 in Nigeria. Two adequate subset ARIMA (2, 2, 1) and AR (1) models for the confirmed/death cases, respectively, is fitted and discussed Results: A forecast of 239 days – from 6th May 2020 to 31 December 2020 was conducted using the fitted models and we observed that the COVID19 data has an upward trend and is best forecasted within a short period. Conclusion: Critical investigation into the rate of spread of COVID-19 pandemic has shown that, that the daily confirmed cases as well as death cases of the disease tends to follow an upward trend. This work aimed at developing a suitable ARIMA models which can be used to fit a most appropriate subsets  to statistically forecast the actual number of confirmed cases as well as death cases of COVID-19 recorded in Nigeria for a period of 8 months.