[This post requires some knowledge of directed acyclic graphs (DAG) and causal inference. Providing an introduction to the topic goes beyond the scope of this blog though. But you can have a look at a recent paper of mine in which I describe this method in more detail.]
Graphical models of causation, most notably associated with the name of computer scientist Judea Pearl, received a lot of pushback from the grandees of econometrics. Heckman had his famous debate with Pearl, arguing that economics has its own tradition of causal inference going back to Haavelmo and that we don’t need DAGs. Continue reading Econometrics and the “not invented here” syndrome: suggestive evidence from the causal graph literature
In my field of research we’re often running regressions with innovation expenditures or sales with new products aon the left-hand side. Usually we observe many zeros for these variables because firms do not invest at all in R&D and therefore also do not come up with new products. Many researchers then feel inclined to use Tobit models. But frankly, I never understood why. Continue reading Why Tobit models are overused
Some time ago I wrote about a paper by Arthur Lewbel in the Journal of Business & Economic Statistics in which he develops a method to do two-stage least squares regressions without actually having an exclusion restrictions in the model. The approach relies on higher moment restrictions in the error matrix and works well for linear or partly linear models. Back then, I expressed concerns that the estimator does not seem to work when an endogenous regressor is binary though; at least not in the simulations I have carried out.
After a bit of email back-and-forth we were able to settle the debate now. Continue reading Follow-up on “IV regressions without instruments” (technical)
Arthur Lewbel published a very interesting paper back in 2012 in the Journal of Business & Economic Statistics (ungated version here). The paper attracted quite some attention because it lays out a method to do two-stage least squares regressions (in order to identify causal effects) without the need for an outisde instrumental variable. Continue reading IV regressions without instruments (technical)
Nowadays everybody is talking about heterogeneous treatment effects. That is, response to an economic stimulus that varies across individuals in a population. However, so far the discussion was concentrated on the instrumental variable setting where a randomized (natural or administered) experiment affects the treatment status of a so-called complier population. An average of the individual treatment effects can only be estimated for this group of compliers. Instead, for the always and never-takers we cannot say anything. But if individual treatment responses are different for everybody in the population, how can we be sure that what we’re estimating for the compliers is representative for the whole population? Continue reading Econometrics: When Everybody is Different
Because I’m currently sitting in the same lecture room in Strasbourg as Steve Pischke and yet another paper on labor markets is presented, I feel inspired to comment on the newest Angrist and Pischke piece on econometrics education. Furthermore, my own graduation doesn’t lie too much in the past, so I might still be part of the target group for an improved coursework in quantitative methods. Continue reading Successfully Mastering Econometrics