I just love this piece by Chris Sims: "Bayesian Methods in Applied Econometrics, or, Why Econometrics Should Always and Everywhere Be Bayesian", from 2007.
In addition to the solid content, there are some great take-away snippets, such as:
"Bayesian inference is hard in the sense that thinking is hard."
"(People) want to characterize uncertainty about parameter values, given the sample that has actually been observed."
"Good frequentist practice has a Bayesian interpretation."
In addition to the solid content, there are some great take-away snippets, such as:
And Sims' conclusion: "Lose your inhibitions: Put probabilities on parameters without embarrassment."
I can live with that!
© 2013, David E. Giles
"A 95% confidence interval contains the true parameter value with probability .95 only before one has seen the data. After the data has been seen, the probability is zero or one."
ReplyDeleteI am a bit puzzled by the language here, but is this just another way of stating that 95 out of a 100 times that a confidence interval is constructed it will contain the true parameter? If so, I don't find this distinction between pre and post sample probability to be particularly helpful. Mostly because I can only construct a confidence interval or calculate a p-value after I have seen the data, but perhaps also because I do not have particularly strong background in probability theory and statistics.