Economics as a soft science
What we’re covering right now in class is not something I’ll cover here in detail – it’s very technical stuff. A few excerpts from today’s lecture notes (click to view full size):
Stuff like this is why I actually get a bit annoyed by people who state that their impression is that economics is a relatively ‘soft’ science, and ask questions like ‘the math you guys make use of isn’t all that hard, is it?’ (I’ve been asked this question a few times in the past) It’s actually true that a lot of it isn’t – we spend a lot of time calculating derivatives and finding the signs of those derivatives and similar stuff. And economics is a reasonably heterogenous field, so surely there’s a lot of variation – for example, in Denmark business graduates often call themselves economists too even though a business graduates’ background, in terms of what we’ve learned during our education, would most often be reasonably different from e.g. my own.
What I’ll just say here is that the statistics stuff generally is not easy (if you think it is, you’ve spent way too little time on that stuff*). And yeah, the above excerpt is from what I consider my ‘easy course’ this semester – most of it is not like that, but some of it sure is.
Incidentally I should just comment in advance here, before people start talking about physics envy (mostly related to macro, IMO (and remember again the field heterogeneity; many, perhaps a majority of, economists don’t specialize in that stuff and don’t really know all that much about it…)), that the complexity economists deal with when they work with statistics – which is also economics – is the same kind of complexity that’s dealt with in all other subject areas where people need to analyze data to reach conclusions about what the data can tell us. Much of the complexity is in the data – the complexity relates to the fact that the real world is complex, and if we want to model it right and get results that make sense, we need to think very hard about which tools to use and how we use them. The economists who decide to work with that kind of stuff, more than they absolutely have to in order to get their degrees that is, are economists who are taught how to analyze data and do it the right way, and how what is the right way may depend upon what kind of data you’re working with and the questions you want to answer. This also involves learning what an Epanechnikov kernel is and what it implies that the error terms of a model are m-dependent.
(*…or (Plamus?) way too much time…)