This makes no sense on any reasonable scale (see, for example, section 5 of this little paper from 1998, but the general principle has been well-known forever, I’m sure), indeed the very concept of a utility function for money becomes, like a rainbow, impossible to see if you try to get too close to it—but economists continue to use it as their default model. Maybe it’s just my field of econ, but most of the papers I read, and definitely the one’s I see getting cited most frequently, are making pretty heavy use of structural modeling. I don’t think either of these statements is in general true. "Economics PhD vs. Statistics PhD." It’s a statement about the population quantities measured in different policy regimes. Economics vs Managerial Economics . What most people do is put extra structure on the problem to see if that helps. According to the Bureau of Labor Statistics, finance and economics, are expected to have a job growth higher than the average for all occupations between 2012 and 2022. This is probably of little interest to anyone but me, but I am following up here so my comments earlier don’t mislead anyone reading this thread. The Wikipedia article provides a good summary. So I don’t think I need to demonstrate how mistaken your belief is, since we seem to be pretty much in agreement on this point, that these ideas are extremely useful and flexible. To further my example, suppose I am interested in estimating the effect of class size on student achievement. …it becomes instantly clear that you don’t much care about whether the things you’re saying are right or not. Of course I don’t think you’re only “allowed” to use least squares etc. „Social Values and Social Structure“. Any game (which is what 3 people making a decision on a movie is, if, say they use majority rule) has to be modeled as decision-making under uncertainty. Not that this makes statisticians inherently better; indeed, one can make lots of good arguments in favor of reduced-form models. Then Arrow’s theorem applies to an individual. But to me that something that needs to be demonstrated, otherwise the point stands. (2013) to consider the relationship between growth and debt in developed countries. Study.com, 28 May 2019 published. Hodrick and Prescott, Sims, and Stock and Watson all do that in different ways. There are in fact many utility functions which can handle exactly the sort of almost-paradox you describe, and which capture both “fearing uncertainty” and “risk aversion” as distinct concepts (the former is known as `ambiguity aversion’). In other words, do they act rationally, where rational is another technical term but probably closer to the lay definition than ‘risk aversion’. For that you need theory as well as multilevel models. But this problem does not arise in contexts in which we observe and model individual-level outcomes, which includes almost all of applied microeconometrics (the bulk of economic research). I know IO methods have a presence there, so I’m shocked by his post. Journal of Economic Geography. I may still handle handle it parametrically with a variety of (econometric jargon) fixed and random effects, but it’s not the object of inference. In the U.S. the Supreme Court imposes a preference, or public decision making would be indeterminate. Assuming a representative agent means modeling X as a result of maximizing utility subject to income Y. This can help people to determine the average rent or mortgage across the county. Economics is far ahead of finance in this regard with an average growth of 20%. Similarly, utility theory was real science—in particular, real psychophysics—for the 1940s. This paper actually does little to refute your Ptolemaic analogy; indeed Friedman says: “Viewed as a body of substantive hypotheses, theory is to be judged by by its predictive power for the class of phenomena which it is intended to ‘explain. Economics is social science that is concerned with the production of goods and services, distribution and consumption of those goods and services, and transfer of wealth between entities within a country or across regions. Lots to think about here (also related to this earlier discussion). I’m sorry, Andrew, but this makes no sense whatsoever. It’s at the core of animal learning. Econometricians are typically much less interested in documenting variation which is nuisance *relative to the causal question at hand*. I was responding to your statement that economists should not use more HLMs. Personally I’m interested in the causes, as opposed to the correlates, of the variability. site, this assumption amounts to assuming away the possibility of unexplained site-level treatment . I don’t know exactly how bears do it, but we humans indeed go to the toilet frequently. If you’re a business major, you’re familiar with the role statistics plays in your field. After Are you familiar with this literature? Adding complexity to the estimation process can make it difficult to get these back. In some contexts it is appropriate despite its limitations, in others, it is not—it’s “refuted” in much the same sense as, say, any static model is “refuted” by the existence of time. There are those, like Sims, who’d rather let the data speak for themselves http://www.economist.com/node/21532266. No one really believes that these models are correct, but the hope is that, if they’re close to correct, than the estimated parameters should be close to stable across policy changes. Mitchell Hartman Jul 9, 2018 A financial professional loosk at his computer screen on the floor of the New York Stock Exchange at the end of … Enter zip: I was about to say this. Andrew, I am sure you are aware — for example, because I just cited you noting! I completely agree that research and practice in econometrics is diverse. The Lucas critique is actually pretty different from the bias/variance tradeoff. The fundamentals of the model are not approximations to something real, they’re just fictions.” Not long ago Judea Pearl said similar on this blog about statisticians, leading you to insist on “a bit of politeness and a bit of respect to people who spend their careers studying reality and just happen not to use your favored methods.” Yet a few weeks later you exhibit an equivalent lack of politeness and respect for people who spend their careers studying reality and just happen not to use your favored methods. Maybe what he meant was that relaxing that axiom (thereby undoing utility theory) would undo the contradiction. But you’re missing the larger point I think. We get more for less, and who does not love a bargain! Most modern economics, including macro, lay a heavy emphasis on micro-foundations, representative agents, and the like. See, for example, the Wikipedia entry on risk aversion, which goes on and on using the long-refuted nonlinear-utility-for-money model and then has one little paragraph on why that model might be wrong. But as I noted that variation would typically be viewed as a nuisance to deal with rather than the estimand of interest. 5. Can you lay this out? Careers involving all three of these concepts are plentiful, and with the... Are you wondering if a major in economics might be the right path for you? Even in a situation such as this where neither of seems to be able to understand what the other is saying, this can be useful in helping us realize where there are problems of communication. As long as the friends are willing to select a movie in an informal way (as opposed to using formal rules), Arrow does not apply. The question is whether they are useful (in some specific cases, and accompanied by appropriate caveats). In your example, multilevel modeling can allow you to estimate that b function in settings of sparse data, for example allowing you to estimate how b varies geographically or over time, in settings where you don’t have a lot of data on each geographic or time unit. Your assumption that I personally ignore that sort of variation because “it’s not worth the effort” is mistaken, for example, I’m currently working on a paper which uses what econometricians refer to as “heterogeneous treatment effects” model and you would refer to as a “multilevel model” specifically to estimate whether a particular causal effect changed over time and how it varies with certain individual-level characteristics. Your post quotes Peter Dorman’s views on microeconomics and (particularly if one follows the link) applied econometrics at length, apparently approvingly. Thread Statistics vs. business statistics Author Date within 1 day 3 days 1 week 2 weeks 1 month 2 months 6 months 1 year of Examples: Monday, today, last week, Mar 26, 3/26/04 external unconfoundedness: that there are no unobservables that moderate the treatment effect and Dorman’s claims about both economic theory and econometric practice are, in view, very much mistaken. . Some of these programs are available in part-time formats, but typically students can complete the program in 4 to 6 years and must complete a dissertation. Therefore, statistics in economics helps in establishing theoretical concepts and models by providing evidence. “But the deeper question is why these models are so appealing to economists but less attractive (yes?) The more important part, in my view, is the view that unstructured models generalize to new settings worse than structured models. From the other side, my approach would be unsatisfactory to the economist because it has no micro-model. That’s the whole point of multilevel modeling, to estimate parameters that vary by using partial pooling. doi:10.1080/0022250X.2011.629067. I do think that modeling behavior as goal-directed is extremely useful in a wide variety of contexts, and that constrained maximization is an extremely useful and flexible approach to modeling goal-directed behavior. in economics, whereas a Ph.D. could lead to a career as a researcher or an academic. Students in these programs must complete a dissertation or doctoral thesis, can usually finish the program in 4 to 5 years, and commonly serve as teaching and/or research assistants throughout the program. Doing the best one can under the circumstances is the intuition behind the formal statement of maximizing utility subject to a constraint. To formalize a little using the canonical example: we observe consumption bundles x_i, incomes y_i, and prices p_i, where i indexes people. In addition I agree with you that modeling behavior as goal-directed is extremely useful in a wide variety of contexts.