Model implied variance-covariance matrix of indicators (matrix form) - part 1 @SpartacanUsuals
Model implied variance-covariance matrix of indicators (matrix form) - part 1  @SpartacanUsuals
Uploaded February 2014 | Updated September 2026, 1 hour ago
This video provides a derivation of the model-implied variance-covariance matrix of indicator variables, when the matrix notation form for factor analysis models is used. 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
Model implied variance-covariance matrix of indicators (matrix form) - part 1An introduction to the Poisson distribution - 1Effective sample size: representing the cost of dependent samplingMaximum likelihood: Normal error distribution - estimator variance part 1Simultaneous equation models - an introductionAn introduction to numerical integration through Gaussian quadratureBob’s bees: the importance of using multiple bees (chains) to judge MCMC convergenceHow to use rejection sampling to uniformly sample within a cows boundariesMaximum likelihood: Normal error distribution - estimator variance part 3Why is a likelihood not a probability distribution?The variance of GLS estimatorsExample likelihood model: waiting times between beer orders
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Model implied variance-covariance matrix of indicators (matrix form) - part 1

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