Results for “model this” 2739 found
My criticism of the Diamond-Dybvig model
I think I forgot to mention this when they won the Nobel Prize. In their model of bank runs, there are multiple equilibria and people can run on the bank simply if they expect other depositors will run on the bank too. The combination of liquid liabilities and illiquid assets then means the bank cannot meet all their claims. That is logically consistent, but I think not realistic. Virtually all the bank runs I know of stem from insolvency, not rumors, noise, and multiple equilibria. Didn’t Hugh Rockoff do some papers showing that the “free banking era” bank runs were pretty rational in the sense that the depositors targeted the right institutions? Or did someone other than Rockoff do this work? In any case, I’ve long thought that bank run models based on insolvency are more useful than bank run models based on rumors and multiple equilibria.
Modeling persistent storefront vacancies
Have you ever wondered why there are so many empty storefronts in Manhattan, and why they may stay empty for many months or even years? Erica Moszkowski and Daniel Stackman are working on this question:
Why do retail vacancies persist for more than a year in some of the world’s highest-rent retail districts? To explain why retail vacancies last so long (16 months on average), we construct and estimate a dynamic, two-sided model of storefront leasing in New York City. The model incorporates key features of the commercial real estate industry: tenant heterogeneity, long lease lengths, high move-in costs, search frictions, and aggregate uncertainty in downstream retail demand. Consistent with the market norm in New York City, we assume that landlords cannot evict tenants unilaterally before lease expiration. However, tenants can exit leases early at a low cost, and often do: nearly 55% of tenants with ten-year leases exit within five years. We estimate the model parameters using high-frequency data on storefront occupancy covering the near-universe of retail storefronts in Manhattan, combined with micro data on commercial leases. Move-in costs and heterogeneous tenant quality give rise to heterogeneity in match surplus, which generates option value for vacant landlords. Both features are necessary to explain longrun vacancy rates and the length of vacancy spells: in a counterfactual exercise, eliminating either move-in costs or tenant heterogeneity results in vacancy rates of close to zero. We then use the estimated model to quantify the impact of a retail vacancy tax on long-run vacancy rates, average rents, and social welfare. Vacancies would have to generate negative externalities of $29.68 per square foot per quarter (about half of average rents) to justify a 1% vacancy tax on assessed property values.
Erica is on the job market from Harvard, Daniel from NYU. And they have another paper relevant to the same set of questions:
We identify a little-known contracting feature between retail landlord and their bankers that generates vacancies in the downstream market for retail space. Specifically, widespread covenants in commercial mortgage agreements impose rent floors for any new leases landlords may sign with tenants, short-circuiting the price mechanism in times of low demand for retail space.
I am pleased to see people working on the questions that puzzle me.
A Big and Embarrassing Challenge to DSGE Models
Dynamic stochastic general equilibrium (DSGE) models are the leading models in macroeconomics. The earlier DSGE models were Real Business Cycle models and they were criticized by Keynesian economists like Solow, Summers and Krugman because of their non-Keynesian assumptions and conclusions but as DSGE models incorporated more and more Keynesian elements this critique began to lose its bite and many young macroeconomists began to feel that the old guard just weren’t up to the new techniques. Critiques of the assumptions remain but the typical answer has been to change assumption and incorporate more realistic institutions into the model. Thus, most new work today is done using a variant of this type of model by macroeconomists of all political stripes and schools.
Now along comes two statisticians, Daniel J. McDonald and the acerbic Cosma Rohilla Shalizi. McDonald and Shalizi subject the now standard Smet-Wouters DSGE model to some very basic statistical tests. First, they simulate the model and then ask how well can the model predict its own simulation? That is, when we know the true model of the economy how well can the DSGE discover the true parameters? [The authors suggest such tests haven’t been done before but that doesn’t seem correct, e.g. Table 1 here. Updated, AT] Not well at all.
If we take our estimated model and simulate several centuries of data from it, all in the stationary regime, and then re-estimate the model from the simulation, the results are disturbing. Forecasting error remains dismal and shrinks very slowly with the size of the data. Much the same is true of parameter estimates, with the important exception that many of the parameter estimates seem to be stuck around values which differ from the ones used to generate the data. These ill-behaved parameters include not just shock variances and autocorrelations, but also the “deep” ones whose presence is supposed to distinguish a micro-founded DSGE from mere time-series analysis or reduced-form regressions. All this happens in simulations where the model specification is correct, where the parameters are constant, and where the estimation can make use of centuries of stationary data, far more than will ever be available for the actual macroeconomy.
Now that is bad enough but I suppose one might argue that this is telling us something important about the world. Maybe the model is fine, it’s just a sad fact that we can’t uncover the true parameters even when we know the true model. Maybe but it gets worse. Much worse.
McDonald and Shalizi then swap variables and feed the model wages as if it were output and consumption as if it were wages and so forth. Now this should surely distort the model completely and produce nonsense. Right?
If we randomly re-label the macroeconomic time series and feed them into the DSGE, the results are no more comforting. Much of the time we get a model which predicts the (permuted) data better than the model predicts the unpermuted data. Even if one disdains forecasting as end in itself, it is hard to see how this is at all compatible with a model capturing something — anything — essential about the structure of the economy. Perhaps even more disturbing, many of the parameters of the model are essentially unchanged under permutation, including “deep” parameters supposedly representing tastes, technologies and institutions.
Oh boy. Imagine if you were trying to predict the motion of the planets but you accidentally substituted the mass of Jupiter for Venus and discovered that your model predicted better than the one fed the correct data. I have nothing against these models in principle and I will be interested in what the macroeconomists have to say, as this isn’t my field, but I can’t see any reason why this should happen in a good model. Embarrassing.
Addendum: Note that the statistical failure of the DSGE models does not imply that the reduced-form, toy models that say Paul Krugman favors are any better than DSGE in terms of “forecasting” or “predictions”–the two classes of models simply don’t compete on that level–but it does imply that the greater “rigor” of the DSGE models isn’t buying us anything and the rigor may be impeding understanding–rigor mortis as we used to say.
Addendum 2: Note that I said challenge. It goes without saying but I will say it anyway, the authors could have made mistakes. It should be easy to test these strategies in other DSGE models.
Further evidence on role models
Leveraging the Tennessee STAR class size experiment, we show that Black students randomly assigned to at least one Black teacher in grades K–3 are 9 percentage points (13 percent) more likely to graduate from high school and 6 percentage points (19 percent) more likely to enroll in college compared to their Black schoolmates who are not. Black teachers have no significant long-run effects on White students. Postsecondary education results are driven by two-year colleges and concentrated among disadvantaged males. North Carolina administrative data yield similar findings, and analyses of mechanisms suggest role model effects may be one potential channel.
That is from a new AER paper by Seth Gershenson, Cassandra M. D. Hart, Joshua Hyman, Constance A. Lindsay and Nicholas W. Papageorge, “The Long-Run Impacts of Same-Race Teachers.” Here are various ungated versions. Just to be clear, I don’t consider this a justification for any particular set of policies. I do see it as extra reason for the successful to be visible and to work hard!
The Diamond and Dybvig model
The Diamond and Dybvig model was first outlined in a seminal paper from Douglas W. Diamond and Philip H. Dybvig in 1983 in a famous Journal of Political Economy piece, “Bank Runs, Deposit Insurance, and Liquidity.” You can think of this model as our most fundamental understanding, in modeled form, of how financial intermediation works. It is a foundation for how economists think about deposit insurance and also the lender of last resort functions of the Fed.
Here is a 2007 exposition of the model by Diamond. You can start with the basic insight that bank assets often are illiquid, yet depositors wish to be liquid. If you are a depositor, and you owned 1/2000 of a loan to the local Chinese restaurant, you could not very readily write a check or make a credit card transaction based upon that loan. The loan would be costly to sell and the bid-ask spread would be high.
Now enter banks. Banks hold and make the loans and bear the risk of fluctuations in those asset values. At the same time, banks issue liquid demand deposits to their customers. The customers have liquidity, and the banks hold the assets. Obviously for this to work, the banks will (on average) earn more on their loans than they are paying out on deposits. Nonetheless the customers prefer this arrangement because they have transferred the risk and liquidity issues to the bank.
This arrangement works out because (usually) not all the customers wish to withdraw their money from the bank at the same time. Of course we call that a bank run.
If a bank run occurs, the bank can reimburse the customers only by selling off a significant percentage of the loans, perhaps all of them. But we’ve already noted those loans are illiquid and they cannot be readily sold off at a good price, especially if the banks is trying to sell them all at the same time.
Note that in this model there are multiple equilibria. In one equilibrium, the customers expect that the other customers have faith in the bank and there is no massive run to withdraw all the deposits. In another equilibrium, everyone expects a bank run and that becomes a self-fulfilling prophecy. After all, if you know the bank will have trouble meeting its commitments, you will try to get your money out sooner rather than later.
In the simplest form of this model, the bank is a mutual, owned by the customers. So there is not an independent shareholder decision to put up capital to limit the chance of the bad outcome. Some economists have seen the Diamond-Dybvig model as limited for this reason, but over time the model has been enriched with a wider variety of assumptions, including by Diamond himself (with Rajan). It has given rise to a whole literature on the microeconomics of financial intermediation, spawning thousands of pieces in a similar theoretical vein.
The model also embodies what is known as a “sequential service constraint.” That is, the initial bank is constrained to follow a “first come, first serve’ approach to serving customers. If we relax the sequential service constraint, it is possible to stop the bank runs by a richer set of contracts. For instance, the bank might reserve the right to limit or suspend or delay convertibility, possibly with a bonus then sent to customers for waiting. Those incentives, or other contracts along similar lines, might be able to stop the bank run.
In this model the bank run does not happen because the bank is insolvent. Rather the bank run happens because of “sunspots” — a run occurs because a run is expected. If the bank is insolvent, simply postponing convertibility will not solve the basic problem.
It is easy enough to see how either deposit insurance or a Fed lender of last resort can improve on the basic outcome. If customers start an incipient run on the bank, the FDIC or Fed simply guarantees the deposits. There is then no reason for the run to continue, and the economy continues to move along in the Pareto-superior manner. Of course either deposit insurance or the Fed can create moral hazard problems for banks — they might take too many risks given these guarantees — and those problems have been studied further in the subsequent literature.
Along related (but quite different!) lines, Diamond (solo) has a 1984 Review of Economic Studies piece “Financial Intermediation and Delegated Monitoring.” This piece models the benefits of financial intermediation in a quite different manner. It is necessary to monitor the quality of loans, and banks have a comparative advantage in doing this, relative to depositors. Furthermore, the bank can monitor loan quality in a diversified fashion, since it holds many loans in its portfolio. Bank monitoring involves lower risk than depositor monitoring, in addition to being lower cost. This piece also has been a major influence on the subsequent literature.
Here is Diamond on google.scholar.com — you can see he is a very focused economist. Here is Dybvig on scholar.google.com, most of his other articles in the area of finance more narrowly, but he won the prize for this work on banking and intermediation. His piece on asset pricing and the term structure of interest rates is well known.
Here is all the Swedish information on the researchers and their work. I haven’t read these yet, but they are usually very well done.
Overall these prize picks were not at all surprising and they have been expected for quite a few years.
The labor market mismatch model
This paper studies the cyclical dynamics of skill mismatch and quantifies its impact on labor productivity. We build a tractable directed search model, in which workers differ in skills along multiple dimensions and sort into jobs with heterogeneous skill requirements. Skill mismatch arises because of information frictions and is prolonged by search frictions. Estimated to the United States, the model replicates salient business cycle properties of mismatch. Job transitions in and out of bottom job rungs, combined with career mobility, are key to account for the empirical fit. The model provides a novel narrative for the scarring effect of unemployment.
That is from the new JPE, by Isaac Baley, Ana Figueiredo, and Robert Ulbricht. Follow the science! Don’t let only the Keynesians tell you what is and is not an accepted macroeconomic theory.
They modeled this
…we demonstrate that individuals who hold very strict norms of honesty are more likely to lie to the maximal extent. Further, countries with a larger fraction of people with very strict civic norms have proportionally more societal-level rule violations. We show that our findings are consistent with a simple behavioral rationale. If perceived norms are so strict that they do not differentiate between small and large violations, then, conditional on a violation occurring, a large violation is individually optimal.
Life at the margin! That is from a new paper by Diego Aycinena, Lucas Rentschler, Benjamin Beranek, and Jonathan F. Schulz.
The Hanson Grabby Aliens Model
Robin is more inclined to draw big conclusions from absences than I am but this is a cool video on the Grabby Aliens Model. And here is the Robin Hanson, Daniel Martin, Calvin McCarter, Jonathan Paulson paper forthcoming in Astrophysical Journal.
They modeled this — why women might see fewer STEM ads
Women see fewer advertisements about entering into science and technology professions than men do. But it’s not because companies are preferentially targeting men—rather it appears to result from the economics of ad sales.
Surprisingly, when an advertiser pays for digital ads, including postings for jobs in science, technology, engineering and mathematics (STEM), it is more expensive to get female views than male ones. As a result, ad algorithms designed to get the most bang for one’s buck consequently go for the cheaper eyeballs—men’s. New work illustrating this gap is prompting questions about how that disparity may contribute to the gender gap in science jobs.
…As a result of that optimization, however, men saw the ad 20 percent more often than women did…
Tucker ran $181 worth of advertising via Google, for example, saying she was willing to pay as much as 50 cents per click. It ended up costing 19 cents to show the ad to a man versus 20 cents to show that same ad to a woman. These investments resulted in 38,000 “impressions”—industry-speak for ad views—among men, but only about 29,000 impressions among women.
Similarly, on Twitter it cost $31 to get about 52,000 impressions for men but roughly $46 to get 66,000 impressions for women. And on Instagram it cost $1.74 to get a woman’s eyeballs on the ad but only 95 cents to get a man’s.
Here is the full Scientific American article, via Luke Froeb, and do note those differentials may vary considerably over time. Gender issues aside, I would say this reflects a broader problem with having a very high value of time — it becomes harder to maintain a relatively high proportion of people showing you valuable things you wish to see (as opposed to people bugging you, grifting you, etc.).
Basil Halperin on sticky wage vs. sticky price models
Here is a long post, full of insight and citations, basically arguing that sticky wage models are better for macro than sticky price models. Sticky wage models had been deemphasized because real wages seemed to be acyclical, but sticky prices can’t quite do the work either. The post is hard to summarize, but my reading of it is a little different than what the author intends. My takeaway is “Sticky wages for new hires are the key, and we didn’t have real evidence/modeling for that until 2020, so isn’t this all still up in the air?” I am a big fan of the Hazell and Taska piece, which I consider to be one of the best economics contributions of the last decade, but still…I don’t exactly view it as confirmed and all nailed down. I do believe in nominal stickiness of (many not all) wages, but I still don’t think we have a coherent model matching up the theory and the empirics for how nominal stickiness drives business cycles. I thus despair when I see so many dogmatic pronouncements about labor markets.
For the pointer I thank João Eira.
A simple model of grabby aliens
According to a hard-steps model of advanced life timing, humans seem puzzlingly early. We offer an explanation: an early deadline is set by ‘grabby’ civilizations (GC), who expand rapidly, never die alone, change the appearance of the volumes they control, and who are not born within other GC volumes. If we might soon become grabby, then today is near a sample origin date of such a GC. A selection effect explains why we don’t see them even though they probably control over a third of the universe now. Each parameter in our three parameter model can be estimated to within roughly a factor of four, allowing principled predictions of GC origins, spacing, appearance, and durations till we see or meet them.
That is a new paper from Robin Hanson, Daniel Martin, Calvin McCarter, and Jonathan Paulson. And here is Robin’s associated blog post.
The Supply and Demand Model Predicts Behavior in Thousands of Experiments
It is sometimes said that economics does not predict. In fact, Lin et al. (2020) (SSRN) (including Colin Camerer) find that the classic supply and demand model predicts behavior and outcomes in the double oral auction experiment in thousands of different experiments across the world. The model predicts average prices, final prices, who buys, who sells, and the distribution of gains very well as Vernon Smith first showed in the 1960s.
Indeed, the results from simple competitive buyer-seller trading appear to be as close to a culturally universal, highly reproducible outcome as one is likely to get in social science about collective behavior. This bold claim is limited, of course, by the fact that all these data are high school and college students in classes in “WEIRD” societies (Henrich et al.,2010b). Given this apparent robustness, it would next be useful to establish if emergence of CE in small buyer-seller markets extends to small-scale societies, across the human life cycle, to adult psychopathology and cognitive deficit, and even to other species.
It is true that economic theory is less capable of explaining the process by which prices and quantities reach equilibrium levels. Adam Smith’s theory about how competitive equilibrium is reached (“as if by an invisible hand”) has been improved upon only modestly. The authors, however, are able to test several theories of market processes and find that zero-intelligence theories tend to do better, though not uniformly so, than theories requiring more strategic and forward thinking behavior by market participants. The double-oral auction is powerful because the market is intelligent even when the traders are not.
The authors also find that bargaining behavior in the ultimatum game is reproducible in thousands of experiments. Simple economic theory makes very poor predictions (offer and accept epsilon) in this model but the deviations are well known and reproducible around the world (participants, for example, are more likely to accept and to accept quickly a 50% split than a split at any other level).
The experiments were run using MobLab, the classroom app, and were run without monetary incentives.
Tyler and I use Vernon Smith’s experiments to explain the Supply-Demand model in our textbook, Modern Principles, and it’s always fun to run the same experiment in the classroom. I’ve done this many times and never failed to reach equilibrium!
Economists modify a SIR model with a spatial and also behavioral dimension
We simulate a spatial behavioral model of the diffusion of an infection to understand the role of geographical characteristics: the number and distribution of outbreaks, population size, density, and agents’ movements. We show that several invariance properties of the SIR model with respect to these variables do not hold when agents are placed in a (two dimensional) geographical space. Indeed, local herd immunity plays a fundamental role in changing the dynamics of the infection. We also show that geographical factors affect how behavioral responses affect the epidemics. We derive relevant implications for the estimation of epidemiological models with panel data from several geographical units.
That is from a new paper by Alberto Bisin and Andrea Moro. Here is a good sentence from the accompanying and descriptive tweet storm:
In Spatial-SIR, local herd immunity slows contagion initially in the less dense city, but faster global herd immunity slows it in the denser city later
I think this means West Virginia is in for some hard times fairly soon.
Implications of Heterogeneous SIR Models for Analyses of COVID-19
This paper provides a quick survey of results on the classic SIR model and variants allowing for heterogeneity in contact rates. It notes that calibrating the classic model to data generated by a heterogeneous model can lead to forecasts that are biased in several ways and to understatement of the forecast uncertainty. Among the biases are that we may underestimate how quickly herd immunity might be reached, underestimate differences across regions, and have biased estimates of the impact of endogenous and policy-driven social distancing.
That is the abstract of a new paper by Glenn Ellison, recommended.
Modeling COVID-19 on a network: super-spreaders, testing and containment
These would seem to be some important results:
To model COVID-19 spread, we use an SEIR agent-based model on a graph, which takes into account several important real-life attributes of COVID-19: super-spreaders, realistic epidemiological parameters of the disease, testing and quarantine policies. We find that mass-testing is much less effective than testing the symptomatic and contact tracing, and some blend of these with social distancing is required to achieve suppression. We also find that the fat tail of the degree distribution matters a lot for epidemic growth, and many standard models do not account for this. Additionally, the average reproduction number for individuals, equivalent in many models to R0, is not an upper bound for the effective reproduction number, R. Even with an expectation of less than one new case per person, our model shows that exponential spread is possible. The parameter which closely predicts growth rate is the ratio between 2nd to 1st moments of the degree distribution. We provide mathematical arguments to argue that certain results of our simulations hold in more general settings.
And from the body of the paper:
To create containment, we need to test 30% of the population every day. If we only test 10% of the population every day, we get 34% of the population infected – no containment (blue bars).
As for test and trace:
Even with 100% of contacts traced and tested, still mass-testing of just over 10% of the population daily is required for containment.
The authors are not anti-testing (though relatively skeptical about mass testing compared to some of its adherents), but rather think a combination is required in what is a very tough fight:
Our simulations suggest some social distancing (short of lockdown), testing of symptomatics and contact tracing are the way to go.
That is all from a new paper by Ofir Reich, Guy Shalev, and Tom Kalvari, from Google, Google, and Tel Aviv University, respectively. Here is a related tweetstorm. With this research, I feel we are finally getting somewhere.