Top all-time · #19447
Dave Giles
Personal blog · davegiles.blogspot.comeconomicsdatamath
Dave Giles's personal blog at davegiles.blogspot.com covers economics, data and math. Since 2015, 4 different Hacker News readers have linked it 4 times. The last post was in 2019, but the archive is still widely read.
Too few sampled comments (2) to say how often HN recommends it.?
Why HN links it
HN readers link Dave Giles's econometrics posts, such as one on whether regression inference needs normally distributed errors, citing King (1980) on elliptically symmetric distributions. Comments only cite the posts.
Summarised by Claude from a sample of 2 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
Econometrics blog with EViews applications Econometrics is fun!
What Hacker News says?
It's actually not even required for that! See http://davegiles.blogspot.com/2011/08/being-normal-is-option... which cites King (1980): > If the error vector in our regression model follows any distribution in the family of Elliptically Symmetric distributions, then any test statistic that is scale-invariant has the same null and alternative distributions as they have when the errors are normally distributed.
Comment linking /2011/08/being-normal-is-optional.html
on “Types of Regression Analysis”Submitted Forecasting from a Regression with a Square Root Dependent Variable as a story · 1 points
HN discussion> “After each time series is transformed (if necessary) to a stationary one, a Granger causality test is performed.“ This is actually methodologically incorrect. You test Granger non-causality by taking a VAR system and you do not difference the data for stationarity! You use the cointegration order to determine how many extra lags of the exogenous variable to use beyond the order p that defines the lags tested agai…
Comment linking /2011/04/testing-for-granger-causality.html
on “Popularity of technology on Stack Overflow and Hacker News: Causality Analysis”Submitted Biased Estimation of Marginal Effects as a story · 1 points
HN discussion