Forecasting performance of mixed data sampling (MIDAS) regressions, autoregressive distributed lag (ADL) model and hybrid of GARCH-MIDAS model: a comparative study

This paper considers the Comparison of forecasting performance between Mixed Data sampling (MIDAS) Regressions model, Autoregressive distributed lag (ARDL) Model and hybrid of GARCH-MIDAS. The data employed for this study was secondary type in nature for all the variables and it is obtained from the publications of Central Bank of Nigerian bulletin, National Bureau of Statistics and World Bank Statistics Database dated, January, 2005 to Dec, 2019. The result of unit root test shows that all variables are stationary at level and after first differences at 5% level of significant. From the results we found that F-statistics 1.895554 is inside the regions defined as the lower and upper bound (3.62 and 4.16) at 5% level of significant, this implies that there’s no long-run relationship between dependent variable (NSE) and independent Variable (CC). using forecasting evaluations with shows that that GARCH-MIDAS has a least value of RMSE and MAPE than ARDL and MIDAS model (1823.531 and 3.976542) is least than for MIDAS and Ardl models (2372.846, 4.765421 and 2134.732, 5.952348). Finally, we can conclude that GARCH- MIDAS model outperform MIDAS and ARDL models of Nigeria Stock Exchange

On Exponentiated Skewed Student t Error Distribution on Some Heteroscedastic Models: Evidence of Nigeria Stock Exchange

In this paper, a new error innovation distribution was proposed in estimating some heteroscedasticity models. A new error innovation distribution was proposed called Exponentiated skewed student t distribution (ESSTD) and compared with the existing error distributions with an empirical dataset using daily returns on Nigeria Stock Exchange (NSE) index return from 30/08/2007 to 30/08/2017.The data shows stationarity at level without difference data and the ADF statistic shows evidence of stationarity, there is presence of ARCH effect. The estimate of the GARCH models and its extension shows a significant probability at 1%, 5% and 10% confident intervals forthe new error distribution and the existing distributions. The AIC and RMSE shows that the new error distributions outperformed in terms of fitness and forecasting evaluation with the smallest AIC and RMSE values respectively

Variable Selection with Convex and Non-Convex Penalized Likelihood Models Using Rainfall Data

Accurate estimate of rainfall is very important for effective use of water resources and optimal planning of water structure in a day-to-day activity of life. Variable selection is an important aspect in penalization for the estimation of accurate outcome. Traditional variable selection such as stepwise and subset selections are usually used which can be computationally expensive and ignore stochastic errors in the variable selection process. Penalized likelihood methods are applied to select the important variables which can be used for accurate predictions. In this study, penalized likelihood approach is applied to select variables and estimate coefficients simultaneously. Some of penalized penalty functions were used to produce sparse solutions. From the results obtained the penalty functions produce the important variables that influence the total rainfall. Lasso model produces Four (4) important variables, Elastic net produces Two (2) important variables while SCAD produces only One (1) variable as important. This indicates that Lasso model is more complex than SCAD model. The results also show that SCAD penalty function out performed Ridge, Lasso and Elastic net. Based on the RMSE criteria, Ridge regression performed less compared to the other models