This lecture is in two parts.
Part 1: Summarizes the critical calculations required for inference for a sample of IID Normal Data. This is both for conventional inference and for Bayesian Inference. It is shown that conventional inference is a special case of the Bayesian inference when we use and un-informative prior.
Part 2: Explain how this theory is applied to real data sets. The KEY is to search for data structures which can reasonably be modeled as IID Normals. Most economic time series DO NOT fit this assumption directly. How do we recognize match and mis-match and how we can use transformations of the data to achieve a better match?
BE L16p2 Applying Normal IID Models to real data - How to apply Bayesian Inference for IID Normals to real data sets
BE L16p1: Inference for Normal IID Data with Normal-Gamma Priors - Apr 7, 2016 12:04 PM -- Calculations for the Diffuse Prior