Representing homoscedasticity and no autocorrelation in matrix form - part 1 @SpartacanUsuals
Representing homoscedasticity and no autocorrelation in matrix form - part 1 @SpartacanUsuals
Uploaded January 2014 | Updated September 2026, 2 hours ago
This video explains how homoscedastic errors with no autocorrelation can be represented in matrix form.

Check out oxbridge-tutor.co.uk/graduate-econometrics-course for course materials, and information regarding updates on each of the courses. Check out ben-lambert.com/econometrics-course-problem-sets-and-data for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: ben-lambert.com/bayesian Accompanying this series, there will be a book: amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Representing homoscedasticity and no autocorrelation in matrix form - part 1Moving Average processes - Stationary and Weakly DependentEstimating the error variance in matrix form - part 2Conclusions and references for grammar of graphicsBLUE estimators in presence of heteroscedasticity - GLS - part 1Random walk not weakly dependentExplaining the difference between confidence and credible intervalsGLS - example in matrix formWhat is the difference between independent and dependent sampling algorithms?Evaluating model fit through AIC, DIC, WAIC and LOO-CVWhat is a posterior predictive check and why is it useful?Factor Analysis - model representation - part 3 (matrix form)
Ben Lambert |

Representing homoscedasticity and no autocorrelation in matrix form - part 1

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