I’m a bit obsessive with words. May be I should have used learning in the title, rather than teaching code. Or perhaps remembering code. You know? Code where one actually has very clear idea of what is going on; for … Continue reading →

What the title of this post is supposed to mean is: "Estimating a simple aggregate consumption function using Bayesian regression analysis".In a recent post I mentioned my long-standing interest in Bayesian Econometrics. When I teach this material I usually include a simple application that involves estimating a consumption function using U.S. time-series data. I used to have...

In a previous post, we discussed ideas generated by a Timely Portfolio post about Linear Models on Stock. I wanted to see if there was a relationship between the window length of the running mean of the linear regression slope estimate and the running mean of the correlation between fitted and observed values. The parameters

The common approach to estimating a binary dependent variable regression model is to use either the logit or probit model. Both are forms of generalized linear models (GLMs), which can be seen as modified linear regressions that allow the dependent variable to originate from non-normal distributions. The coefficients in a linear regression model are marginal