Workblog

It takes way more time to cover this stuff in detail here than I’m willing to spend on it, but here are a few relevant links to stuff I’m working on/with at the moment:

i. Regression discontinuity design.

ii. Survival analysis (duration models).

iii. Kolmogorov–Smirnov test.

iv. Chow test.

v. Bootstrapping.

vi. Education and health: Evaluating Theories and Evidence, by Cutler & Muney.

vii. Education, Health and Mortality: Evidence from a Social Experiment, by Meghir, Palme & Simeonova.

 

Stuff

i. Econometric methods for causal evaluation of education policies and practices: a non-technical guide. This one is ‘work-related’; in one of my courses I’m writing a paper and this working paper is one (of many) of the sources I’m planning on using. Most of the papers I work with are unfortunately not freely available online, which is part of why I haven’t linked to them here on the blog.

I should note that there are no equations in this paper, so you should focus on the words ‘a non-technical guide’ rather than the words ‘econometric methods’ in the title – I think this is a very readable paper for the non-expert as well. I should of course also note that I have worked with most of these methods in a lot more detail, and that without the math it’s very hard to understand the details and really know what’s going on e.g. when applying such methods – or related methods such as IV methods on panel data, a topic which was covered in another class just a few weeks ago but which is not covered in this paper.

This is a place to start if you want to know something about applied econometric methods, particularly if you want to know how they’re used in the field of educational economics, and especially if you don’t have a strong background in stats or math. It should be noted that some of the methods covered see wide-spread use in other areas of economics as well; IV is widely used, and the difference-in-differences estimator have seen a lot of applications in health economics.

…

ii. Regulating the Way to Obesity: Unintended Consequences of Limiting Sugary Drink Sizes. The law of unintended consequences strikes again.

You could argue with some of the assumptions made here (e.g. that prices (/oz) remain constant) but I’m not sure the findings are that sensitive to that assumption, and without an explicit model of the pricing mechanism at work it’s mostly guesswork anyway.

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iii. A discussion about the neurobiology of memory. Razib Khan posted a short part of the video recently, so I decided to watch it today. A few relevant wikipedia links: Memory, Dead reckoning, Hebbian theory, Caenorhabditis elegans. I’m skeptical, but I agree with one commenter who put it this way: “I know darn well I’m too ignorant to decide whether Randy is possibly right, or almost certainly wrong — yet I found this interesting all the way through.” I also agree with another commenter who mentioned that it’d have been useful for Gallistel to go into details about the differences between short term and long term memory and how these differences relate to the problem at hand.

…

iv. Plos-One: Low Levels of Empathic Concern Predict Utilitarian Moral Judgment.

“An extensive body of prior research indicates an association between emotion and moral judgment. In the present study, we characterized the predictive power of specific aspects of emotional processing (e.g., empathic concern versus personal distress) for different kinds of moral responders (e.g., utilitarian versus non-utilitarian). Across three large independent participant samples, using three distinct pairs of moral scenarios, we observed a highly specific and consistent pattern of effects. First, moral judgment was uniquely associated with a measure of empathy but unrelated to any of the demographic or cultural variables tested, including age, gender, education, as well as differences in “moral knowledge” and religiosity. Second, within the complex domain of empathy, utilitarian judgment was consistently predicted only by empathic concern, an emotional component of empathic responding. In particular, participants who consistently delivered utilitarian responses for both personal and impersonal dilemmas showed significantly reduced empathic concern, relative to participants who delivered non-utilitarian responses for one or both dilemmas. By contrast, participants who consistently delivered non-utilitarian responses on both dilemmas did not score especially high on empathic concern or any other aspect of empathic responding.”

In case you were wondering, the difference hasn’t got anything to do with a difference in the ability to ‘see things from the other guy’s point of view’: “the current study demonstrates that utilitarian responders may be as capable at perspective taking as non-utilitarian responders. As such, utilitarian moral judgment appears to be specifically associated with a diminished affective reactivity to the emotions of others (empathic concern) that is independent of one’s ability for perspective taking”.

On a small sidenote, I’m not really sure I get the authors at all – one of the questions they ask in the paper’s last part is whether ‘utilitarians are simply antisocial?’ This is such a stupid way to frame this I don’t even know how to begin to respond; I mean, utilitarians make better decisions that save more lives, and that’s consistent with them being antisocial? I should think the ‘social’ thing to do would be to save as many lives as possible. Dead people aren’t very social, and when your actions cause more people to die they also decrease the scope for future social interaction.

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v. Lastly, some Khan Academy videos:

(Relevant links: Compliance, Preload).

(This one may be very hard to understand if you haven’t covered this stuff before, but I figured I might as well post it here. If you don’t know e.g. what myosin and actin is you probably won’t get much out of this video. If you don’t watch it, this part of what’s covered is probably the most important part to take away from it.)

It’s been a long time since I checked out the Brit Cruise information theory playlist, and I was happy to learn that he’s updated it and added some more stuff. I like the way he combines historical stuff with a ‘how does it actually work, and how did people realize that’s how it works’ approach – learning how people figured out stuff is to me sometimes just as fascinating as learning what they figured out:

(Relevant wikipedia links: Leyden jar, Electrostatic generator, Semaphore line. Cruise’ play with the cat and the amber may look funny, but there’s a point to it: “The Greek word for amber is ηλεκτρον (“elektron”) and is the origin of the word “electricity”.” – from the first link).

(Relevant wikipedia links: Galvanometer, Morse code)

What I’m currently working on…

I haven’t really work-blogged anything substantial this semester so far and I’ve felt a bit guilty about that. Today on my way home from lectures I decided that one thing I could do, which wouldn’t take a lot of work on my part, was to just upload my notes taken during a lecture.

The stuff uploaded below is one and a half hour (2 lectures, each lasting 45 minutes) of my life, roughly. It wasn’t the complete lecture as the lecturer also briefly went through an example of how to do the specific maximum likelihood estimation and how to perform the Smith-Blundell procedure on a data set in a statistical program called Stata. On the other hand it’s more than 2 hours of my life because I also had to prepare for the lecture…

I know that people who’re not super familiar with mathematical models generally tend to assume that ‘the level of complexity’ dealt with in mathematical expressions is somehow positively correlated with (‘and thus causally linked to…’) the ‘amount of algebra’ (‘long equations with lots of terms are more complicated and involves more advanced math than short equations with few terms’). In general that’s not how it works. The stuff covered during the lecture was corner solution response models with neglected heterogeneity and endogenous variables; it may look simple as there’s a lot of of ‘a+b type stuff’, but you need to think hard to get things right and even simple-looking steps may cause problems when you’re preparing for exams in a course like this. Non-linear models with unobserved variables isn’t what you start out with when you learn statistics, but on the other hand this was hardly the most technical lecture I’ve had so I figured it sort of made sense to upload this; I added quite a few comments to the equations written on the blackboard which should make stuff easier to follow.

Anyway I figured at least one or two of you might find it interesting to ‘have a look inside the classroom’ (you can click the images to view them in a higher resolution):

Back to work…

I’ve not had lectures for the last two weeks, but tomorrow the new semester starts.

Like last semester I’ll try to ‘work-blog’ some stuff along the way – hopefully I’ll do it more often than I did, but it’s hard to say if that’s realistic at this point.

I bought the only book I’m required to acquire this semester earlier today:

…and having had a brief look at it I’m already starting to wonder if it was even a good idea to take that course. I’ve been told it’s a very useful course, but I have a nagging suspicion that it may also be quite hard. Here are some of the reasons (click to view in a higher resolution):

I don’t think it’s particularly likely that I’ll cover stuff from that particular course in work-blogs, for perhaps obvious reasons. One problem is the math, wordpress doesn’t handle math very well. Another problem is that most readers would be unlikely to benefit much from such posts unless I were to spend a lot more time on them than I’d like to do. But it’s not my only course this semester. We’ll see how it goes.

“So you see, it’s really quite simple…”

“…it’s just a matter of estimating the hazard functions…”

Or something like that. The words in the post title the instructor actually said, but I believe his voice sort of trailed off as he finished the sentence. All the stuff above is from today’s lecture notes, click to enlarge. The quote is from the last part of the lecture, after he’d gone through that stuff.

In the last slide, it should “of course” be ‘Oaxaca Blinder decomposition’, rather than ‘Oaxaca-Bilder’.

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…)

What I’m working on (ctd.)

Or a sample that’s arguably closer than yesterday’s to the kind of stuff I’m actually working with. The pics are from my textbook. Click to view in higher res.

In a couple of months, I’ll probably say that (‘stuff like this’) looks worse than it is. Some of it is quite a bit simpler than it looks, but in general I don’t feel that way right now. Even though we made some progress today there’s still a long way to go.

Stopped working half an hour ago, basically because I couldn’t think straight anymore, not because I wouldn’t like to keep working. On my way to bed. We’re in time trouble and I probably won’t do anything but work and sleep until Friday (not that I’ve been doing all that much else so far); anyway, don’t expect any updates until Friday evening or some time Saturday.

Rewriting history

Megan, apparently now also blogging at instapundit, made me aware of this. Creepy…

An excerpt:

One of the great benefits of experimental research is that, in principle, we can repeat the experiment and generate a fresh set of data. While this is impossible for many questions in social science, at a minimum one would hope that we could replicate our original results using the same dataset. As many students in Gov 2001 can tell you, however, social science often fails to clear even that low bar.

Of course, even this type of replication is impossible if someone else has changed the dataset since the original analysis was conducted. But that would never happen, right?

Guess again…