Jimbo Brand

Wrote a second blog post! This time it's about using LLMs as part of an ensemble imputation method, relying on their "knowledge" of the world to provide additional prediction signal. Seems to work well, especially for bigger models!

jamesbrandecon.github.io/blog/posts_h...

P.S. I also tried to make plotting easier. Here, for example, are estimates from a model with two product characteristics and correlated preferences. More to come, including docs to make it easier to dig through the "problem" objects to extract results.

As with FRACDemand (which is now registered!), the coolest thing here is that it's fast. less than a minute to define the problem, estimate it, and calculate price elasticities for 500 markets with 20 products each

GitHub - jamesbrandecon/FKRBDemand.jl Contribute to jamesbrandecon/FKRBDemand.jl development by creating an account on GitHub.

Have a day off so I made some small updates to FKRBDemand.jl (new name!). Hopefully it's now easier to use the Fox-Kim-Ryan-Bajari method to estimate a random coefficient model with market-level data (2-step approach described here: www.jamesbrandecon.com/blog/0jxkvfr...)

github.com/jamesbrandec...

Congratulations, Jeff!

Nice, thanks for explaining! Agreed, it seems useful to help see how I'm using memory while building something (which I still wish I understood better)

Is the goal just that GC can catch when to run? Or is there another performance benefit I don't understand? Seems like a lot of additional code so I'm assuming there's more value

This point you've been on has stuck with me as an industry guy. How do firms succeed while being terrible at stats reasoning? Because some big ideas are robust to that bad logic and (like VCs?) you get a lot of shots and only need a few to pay off. That changes how to think of data work for the firm

Finally, we put this all in a Julia package! Trying to take seriously that these complex methods cost a lot of time and effort to implement, we tried to make it as easy as possible to use our methods for estimation, inference, and some basic counterfactuals 6/6

Third, we apply our method to real demand from 12 categories of products at a large retail food chain. Our estimates agree with our simulations — constraints matter, and imposing them generates more reasonable elasticities and counterfactuals (below) 5/

Second, we show in simulations that our method improves upon the previous state of the art for this model, both because we can fully enforce constraints and because doing so gives us more accurate estimates of price elasticities. 4/

This two-step approach not only helps us impose new constraints -- it also guarantees that our constraints are satisfied *everywhere*, which we show is often not true of the best previous approach 3/

First, we develop a new quasi-Bayes approach to solve this (hard!) problem. We show that imposing constraints on nonparametric demand curves is better suited to MCMC sampling approaches than frequentist optimization, and develop a two-step process for sampling in our setting 2/

New paper with @adam-n-smith.bsky.social
papers.ssrn.com/sol3/papers....

Our paper develops a new approach for estimating demand nonparametrically while imposing economic constraints and comes with a new package, NPDemand.jl! Some things we do in the paper 1/

The paper looks really nice, but Prop 2 (no ME) feels stronger than is useful in practice... do we hold ourselves to that standard for any other measurement tool? Or am I being unfair/missing something

Ditto, my brain hurts enough switching between the languages themselves. Adding UI as another thing to switch is a pain

Wow, as an increasingly frequent R user I wish I'd known this was possible months ago -- thanks for figuring this out in public

Been (finally) reading through it in detail recently -- great paper!

Agreed (and I get this argument from ML people a lot), but funny enough we'll never know the counterfactual there either! I could walk around in the dark and get where I'm going but it'd still be nice to have the lights on

My experience is that the former often dominates, and is the default even in settings where academics would lean toward the latter (when estimating the value in the world is statistically hard) but folks are surprisingly open to understanding the issues with "ML everything" when demonstrated

There's definitely some of "just do whatever works" in my experience but there are also a ton of people (often PhDs) trying to do things as right as possible under constraints, including experimentation and causal inference

Agreed, I wondered how reallocating regular up to premium is being counted. New planes with 50% premium seems high! Curious to see how premium margins decline (seems like they have to?) as lower WTP customers are forced into it just by prevalence

Awesome find. Silly question -- what does "incremental" here really mean? Net new seats mostly come from new planes/routes (I assume) and I don't see how 85% can be premium. Any idea?

My group at Microsoft is hiring again! This time looking for junior candidates specifically wanting to study advertising. Academic friends (econ or business school especially), please let me know if you have PhD students we should look at!

Listing here: jobs.careers.microsoft.com/global/en/jo...

Oh yeah? Well where else can you get a "taco" with a hot dog in it?

If y_i are just means, doesn’t going full or empirical Bayes make sense? Feels like that kind of structure

What do you mean exactly? There are a handful of studies looking at productivity of different forms. Usual finding is that lower-skill users are more likely to benefit, though not always. One paper with a few studies www.microsoft.com/en-us/resear...

Sorry, I’m not familiar with any guides! The particulars are an odd mix of very general (simulate the DGP you care about and do your estimation many times) and DGP specific (e.g. realistic IV strength in nonlinear models)

Isn’t the norm to do it via simulation? For most non-RCTs I’ve seen its hard to do anything else

A one-file implementation of Fox, Kim, Ryan, Bajari (2011)'s linear estimator for the mixed logit de... A one-file implementation of Fox, Kim, Ryan, Bajari (2011)'s linear estimator for the mixed logit demand model, in the style of FRAC.jl and NPDemand.jl. - FKRB.jl

In case non-twitterers are interested: A demand estimation approach that I've always liked but never tried is Fox-Kim-Ryan-Bajari 2011. Wanted to see how easy it'd be to package + whether it would perform better than FRAC.jl, so I wrote a quick gist. Works well! gist.github.com/jamesbrandec...