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RESEARCH-RELATE CONCEPTS

Difference Between Homoscedasticity and Heteroscedasticity

 

Prof. Ogulu: In fact, Big Data Analytics has now moved on to Industrial Internet of Things (IIoT) and Ecosystemic Data Analytics. What can a developing nation do today to stay economically afloat in a globally connected world? It must resort to Big Data Analytics or better still, the wider Data Science in every sector, in every industry and in every organisation.                        
 
Kenedy Nnaji: Thanks Prof. DJO for reading my paper on Autoregressive Conditional Heteroscedasticity (ARCH/GARCH family of models) and making some intuitive remarks. I want say here that Homoscedasticity and Heteroscedasticity are two sides of a coin. Whereas the error term entering the classical regression model is assumed to be homoscedastic (i.e. its variance is assumed to be constant over time), heteroscedasticity becomes an issue of concern if this assumption is violated. So, the difference between homoscedasticity and heteroscedasticy lies in whether the variance of error term is constant or not. Just as you rightly advised, the two concepts will be thoroughly considered in the upcoming seminar.                        
 
 
 
 
 
 

 
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