There are no April Fool's tricks in the following list of suggestions. 😐
- Antoine, B. and E. Renault, 2017. On the relevance of weaker instruments. Econometric Reviews, online.
- Golan, A. and A. Ullah, 2017. Interval estimation: An information theoretic approach. Econometric Reviews, online.
- Hendry, D. F. and P. C. B. Phillips, 2017. John Denis Sargan at the London School of Economics. Cowles Foundation Discussion Paper No. 2082, Yale University.
- Ozonur, D., H. T. K. Akdur, & H. Bayrak, 2016. Comparisons of tests of distributional assumption in Poisson regression model. Communications in Statistics - Simulation and Computation, online.
- Vijayamohanan, P. N., 2017. How do you interpret your regression coefficients? MPRA Paper No. 76867.
- Witmer, J., 2017. Bayes and MCMC for undergraduates. American Statistician, online.
I went through the last piece and some of its references included the speech of Sims on bayesian methods. Having been formed only in frequentist methods during my undergraduate years, I am wondering how much of a gain there is to learn about bayesian statistics -- I mean, at least to a point it would be of service for serious work or better yet improve my hability to analyze data or sell my services later on.
ReplyDeleteThere is a cost to learning it: what's the gain?
The gain is that it's the most rapidly growing area in econometrics now that there are no real computational barriers. Also, much of the "large data" techniques that are being used are either explicitly or implicitly Bayesian in nature.
DeleteDear Prof,
ReplyDeleteCan you write a blog on the issue of misclassification in binary variables. In particular how misclassification (misreporting treatment status) bias parameter estimates of interest? Also an extension of parameter identification when the misclassified binary variable as well as misclassification (probability of misreporting treatment status) is endogenous would be great
Many thanks
David