Whenever somebody mentions “The Simpsons” it always stirs up feelings of nostalgia in me. The characters, uproarious gags, zingy one-liners, and edgy animation all contributed towards making, arguably, the greatest TV ever. However, it’s easy to forget that as a TV show “The Simpsons” is still ongoing—in its twenty-fourth season no less. For me, and […]
There was a very informative post last week showing how the R package stargazer is used to generate nice LaTeX tables from a number of R objects. This package looks very useful. However, I would like to extol the virtues of another R package that converts model objects in R into LaTeX code: texreg. For […]
Adjusting standard errors for clustering can be a very important part of any statistical analysis. For example, duplicating a data set will reduce the standard errors dramatically despite there being no new information. I have previously dealt with this topic with reference to the linear regression model. However, in many cases one would like to […]
A number of news outlets have reported a peculiar quirk that arose during Friday’s Champions League draw. Apparently, the sport’s European governing body, UEFA, ran a trial run the day before the main event, and the schedule chosen during this event was identical to that of the actual draw on Friday. Given this strange coincidence, […]
Estimating a least squares linear regression model with fixed effects is a common task in applied econometrics, especially with panel data. For example, one might have a panel of countries and want to control for fixed country factors. In this case the researcher will effectively include this fixed identifier as a factor variable, and then proceed to […]
Measuring the effect of a binary treatment on a measured outcome is one of the most common tasks in applied statistics. Examples of these applications abound, like the effect of smoking on health, or the effect of low birth weight on cognitive development. In an ideal world we would like to be able to assign […]
A commenter on my previous blog entry has drawn my attention to an R function called hetglm() that estimates heteroskedastic probit models. This function is contained in the glmx package. The glmx package is not available on CRAN yet, but thankfully can be downloaded here. The hetglm() function has a number of computational advantages compared with […]
Specification testing is an important part of econometric practice. However, from what I can see, few researchers perform heteroskedasticity tests after estimating probit/logit models. This is not a trivial point. Heteroskedasticity in these models can represent a major violation of the probit/logit specification, both of which assume homoskedastic errors. Thankfully, tests for heteroskedasticity in these […]
The recently released BMR package, short for Bayesian Macroeconometrics with R, provides a comprehensive set of powerful routines that estimate Bayesian Vector Autoregression (VAR) and Dynamic Stochastic General Equilibrium (DSGE) models in R. The procedure of estimating both Bayesian VAR and DSGE models can represent a great computational burden. However, BMR removes a lot of […]
Dealing with endogeneity in a binary dependent variable model requires more consideration than the simpler continuous dependent variable case. For some, the best approach to this problem is to use the same methodology used in the continuous case, i.e. 2 stage least squares. Thus, the equation of interest becomes a linear probability model (LPM). The […]
