Bivariate Time Series Analysis of Nigeria Gross Domestics Product and Communication Sector (Published)
The need to establish a relation between Gross Domestic Product and Communication sector was the major focus of this research. This research work investigated the contribution of Communication sector to the Gross Domestic product of Nigeria Economy. With the aid of ACF and PACF, ARIMA (1 1 1) was suggested for both variables. Alternative multivariate time series models used for the analysis were ARIMAV, MARDL and MARDL-MA models. The research has established interaction and interdependence between the two macroeconomic variables, and has also revealed that each of the variable has contributed significantly to each other at first time lag. The error variances of the bivariate time series model were derived for GDP and Communication sector. When comparing the three models for the two economic variables, ARIMAV model for Gross Domestic Product has the least error variance of making it the best model, while MARDL model for communication sector produced the least error variance of 0.0723, thereby indicating that MARDL model outperformed ARIMAV and MARDL-MA models for communication sector. Hence, this research has brought to focus the fact that performance of a model over another is predicated upon the nature of the economic data. That means there is no fixed multivariate time series model for a given macroeconomic data due to the dynamic nature of the time series.
Keywords: ARIMAV, MARDL, MARDL-MA, Partial autocorrelation, autocorrelation
Evaluation of Some Estimators Performance on Linear Models with Heteroscedasticity and Serial Autocorrelation (Published)
In many, if not most, econometric applications, economic data arises from time-series or cross-sectional studies which typically exhibit some form of autocorrelation and/or heteroskedasticity. If the covariance structure were known, it could be taken into account in a (parametric) model, but more often than not the form of autocorrelation and heteroskedasticity is unknown. In such cases, model parameters can typically still be estimated consistently using the usual estimating functions, but for valid inference in such models a consistent covariance matrix estimate is essential. In this study, the strength of some methods of estimating classical linear regression model with both negative and positive autocorrelation in the presence of heteroscedasticity were investigated. The Ordinary Least Square (OLS) estimator, Heteroskedasticity and Autocorrelation (HAC) estimators which includes Cluster-Robust Standard Errors estimators, Newey-West standard errors and Feasible Generalized Least Squares Estimator (FGLS) were considered in this study. Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. The result revealed the superiority of the Newey-West standard errors over others using root mean squared error (RMSE) of the parameter estimates and relative efficiency (RR) as assessment criteria among others over various considerations for the distribution of the serial correlation and heteroskedasticity.
Keywords: Heteroscedasticity, Panel Data, Robust Regression., autocorrelation, ordinary least squares estimation