Friday, July 6, 2018

Economic growth in India


Via DM, I was asked about the path of India's GDP in the dynamic information equilibrium model (DIEM). The result is in the graph above. I had to cobble together some annual data from the Reserve Bank of India's statistics page along side the quarterly data available on FRED. What is interesting is that India shows a different pattern of DIEM "shocks" from Anglophone and European countries. The first shock is a negative one centered in 1952, spanning the years 1949 to 1955; the likely "cause" is India's independence in 1947. Some may want to attribute this to India adopting a socialist system, but its first five year plan doesn't happen until 1951 — halfway through this shock. Plus, the following period shows roughly constant growth. At any rate, more study is needed.

The DIEM description of the data shows a period of "equilibrium" growth between 1960 and 1980 that would match up with Raj Krishna's "Hindu rate of [real] growth" of about 3.5%.  The 9.9% nominal GDP per annum would have to have inflation of about 6.4% during that period (which is about right: log CPI from 0.9 in 1960 to 2.2 in 1980 would be 0.065 = 6.5%) to produce 3.5% real growth.

The period from 1980 to 1995 was a large positive shock. It could be attributed to the sixth five year plan and its economic liberalization, however in most of the rest of the economies I've looked at the major factor is demographic. This could be e.g. people leaving agriculture for industry. It could also be the surge in deficit spending around the same time (slightly before). Again, I have to look more closely at other time series to understand what is happening here.

Finally, there's another surge that occurs from 2006 to 2012 (with the global financial crisis appearing as a blip in the middle). This neatly corresponds to the eleventh five year plan (2007-2012), and overlaps with the construction of the "Golden Quadrilateral" road system.

Since 2012, India appears to be back on its equilibrium growth path of 9.9% per year (nominal GDP), which is expected to continue into the future.

I always like looking at the data for other countries than the US — laziness and the ease of accessing FRED data are big reasons for most of the models being tested on US data. Additionally, the political economy of the US tends to bring up more US-centric questions.

I'm also not very well informed about a lot of the political economy and economic history of other countries. This is both good and bad. It's good because it means I don't go modeling the data with a preconceived economic history; it's bad because I don't necessarily have decent intuitive explanations for what the models uncover. I'd be appreciative for any information about the economic history of India beyond my rudimentary knowledge above in comments.

Unemployment up to 4.0% (but still consistent with forecast)

The dynamic information equilibrium model (DIEM) forecast (the model is detailed in my paper) is still going strong since it was made back in January of 2017 (1.5 years). Last month's data was outside the 90% confidence intervals, but this month has us back up to 4.0% as a bit of mean reversion [1]. Here's the comparison with the FRBSF forecasts as well as the Fed's (annual average) forecasts:



The color-coded arrows in the second graph show when the Fed forecasts were made.

Note that if there is going to be a recession in 2019 (via JOLTS data indicator, via yield curve indicator), we'd expect the path of the unemployment rate to follow something like the September 2017 forecast from the FRBSF and the Fed — the unemployment rate will be significantly above the DIEM forecast error allowing e.g. this "recession detection" algorithm to posit a recession.

These kinds of forecasts always fill me with conflict. A recession is a terrible hardship for many people. However, I'm also excited to see if the model works. I tend to rationalize it by saying that forecasting recessions is a bit like forecasting earthquakes or volcanic eruptions — it can help people prepare — but we should always remember that economic time series are metrics for real world hardship.

...

Footnotes:

[1] I heard the news on NPR, and I found the "very serious" talk by economic journalists and economists about the "meaning" of the jump as if something had changed or that it was due to various factors ... hilarious. Even without the dynamic equilibrium model, this increase was well within the random fluctuations/measurement error.

Tuesday, July 3, 2018

Why hate on beauty?

I've seeing the media surrounding the release of Sabine Hossenfelder's new book Lost in Math: How Beauty Leads Physics Astray and I think we now have a physics example of the econ critique trope. I tweeted about it the other day:
I'd almost put this as directly analogous to an econ-critical economist saying economists are too enamored with beautiful theories when cursory inspection of your average DSGE model would generate almost any adjective except beautiful.
At the time I had read Hossenfelder's blog post about "beauty" in physics, but I just found the link to Andrew Gelman's blog about it and it's seems to have picked up sufficient steam that I really think I need to say that Hossenfelder is mis-characterizing physics — in much the same way econ-critical economists tend to mis-characterize economics in their criticism by playing on public perceptions of the field.

Hossenfelder defines beauty in physics as "simplicity, naturalness, and elegance" and proceeds to discuss each in turn; I will do the same.

Simplicity

I think Hossenfelder is playing on the prejudices of a general audience here. If people know any "simple" theoretical physics models, they probably are aware of Maxwell's equations or Einstein's general relativity. Actually, Andrew Gelman gives the list the typical member of the target audience might give:
Newton’s laws, relativity theory, quantum mechanics, classical electromagnetism [i.e. Maxwell's equations], the second law of thermodynamics, the ideal gas law: these all do seem beautiful to me.
People have tattoos of some of these equations! I knew someone (not a physicist) who had a Schrodinger equation tattoo. They make several Maxwell's equations T-shirts. I always thought they should write ℒ = tr F ∧ ★F using differential forms instead of the 19th century vector form people are most familiar with. The thing is that nearly all of those theories were known by the first half of the 20th century. The most recent one on the list is quantum mechanics, and quantum mechanics is over 100 years old (the Schrodinger equation itself turned 90 a couple years ago). These are not recent theories. Yet, I think Hossenfelder is playing on the fact that her audience has these examples on the tip of their tongue (availability heuristic).

Of course, missing from that list is quantum field theory, the content-less method for maintaining [pdf] "analyticity, unitarity, cluster decomposition, and symmetry". But quantum Yang-Mills theory and examples of it like QED and QCD have a kind of beautiful simplicity. At least when you write them down as a Lagrangian (they're both described by the classical Yang-Mills Lagrangian in the previous paragraph, but QCD has additional non-commutative matrix indices). Computing 600 diagrams to get a few more decimal places in the calculation of the magnetic moment isn't really "simple", and there's nothing "simple" about non-perturbative QCD for which one of the major approaches (lattice QCD) has all the beauty and simplicity of your undergrad implementation of Runge-Kutta integration.

The underlying jab, of course, is at string theory. Hossenfelder studies quantum gravity, for which there are few candidate theories that make sense. String theory requires six or seven additional unobserved dimensions, and loop quantum gravity violates Einstein's special relativity (Lorentz invariance). I personally like Verlinde's wisecrack [1] — his paper about entropic gravity that in a sense says quantum gravity doesn't exist.

But as anyone who has actually studied string theory would know (the UW introduced its first string theory class while I was there) the string theories aren't exactly "simple" — much like how the simplicity of the Yang-Mills Lagrangian hides complex non-perturbative physics, string theories are incredibly complicated to actually write down and perform calculations with. Sometimes a "simple" idea comes up (T-duality, AdS/CFT correspondence), but the preponderance of papers in purportedly simple string theory look like this [pdf]:


This is not simple in any way that would be considered "beautiful" (I'm not knocking this paper!), so obviously beauty as "simplicity" is not always a driving factor in research. And most string theory looks like this! It makes me wonder if Hossenfelder is playing on the fact that very few people reading her book have ever actually done a calculation with a Virasoro algebra — even among physicists. 

Naturalness

Hossenfelder's technical description of naturalness is fine (dimensionless parameters being of order 1), but the direction of inferences from naturalness is wrong. A lack of naturalness is usually a sign of a puzzle, but if a theory describes empirical data well enough no one rejects the theory. An example: QCD. The QCD Lagrangian, from a theoretical perspective (based on Weinberg's paper I used as a citation for the content-less-ness of quantum field theory above), should have another term that allows QCD to violate CP symmetry (charge-parity symmetry, the conjugate of time-reversal symmetry). This is called the strong CP problem. For some reason, the coefficient of that term, if it's not zero, is really small. Unnaturally small. It's small enough that the axion was proposed as a possible solution. A similar consideration happens in general relativity which should have a cosmological constant; however that constant is unnaturally small (at least from the scale we think should set it — which likely means it should be some other scale). 

But in no way is this lack of naturalness cause to reject general relativity or QCD (which are both wildly empirically successful in other areas), or consider either any less "beautiful". At least I thought the theory was 'beautiful'; my thesis was about a potential approach to non-perturbative QCD that could be measured in nuclear physics experiments. Naturalness as beauty has not led physics astray in its study of QCD or general relativity.

In fact, if you had some new theory of quantum gravity and the only thing in your way is a lack of naturalness in your parameter values that fit empirical data well, then that would be a major breakthrough. I can't imagine any physicist that would reject it. The issue is that there's no theory that predicts new effects that have been (or could be) measured to make any kind of naturalness consideration at all of the parameters that fit that non-existent data.

Elegance

Hossenfelder's definition of elegance seems to be a redundant restatement of the other aspects of beauty ("Elegance is the fuzziest aspect of beauty. ... By no way do I mean to propose [elegance] as a definition of beauty; it is merely a summary of what physicists mean when they say a theory is beautiful." — beautiful via the other two criteria, I guess?)

I'd agree her example of general relativity is elegant. It's also simple from a certain perspective. In fact, going by the effective theory approach, general relativity is the simplest non-trivial curved space-time theory we could write down:

Gμν + Λ gμν = α Tμν

That basically says space-time curvature + cosmological constant = energy-momentum. Of course, there's a big naturalness problem right there in that cosmological constant: Why is it so small? But we don't reject the theory. Einstein thought the Λ = 0 version was more elegant. However, Einstein also thought the equations were so complicated (And they are! The notation above hides so much! [2]) that it would be difficult to find any closed form solutions. (Schwarzchild did find such a solution a few years later.) As with a century of quantum mechanics, time can alter our perspective. Once thought hopelessly complex, people now routinely solve Einstein's field equations and make precision measurements of its novel effects.

Hossenfelder also mentions grand unification as elegant, but grand unification is more a set of circumstantial evidence than a "theory". The charges of the various quantum field theories in the standard model change with energy in such a way that they almost coincide at a huge energy called the Grand Unified Theory (GUT) scale. Adding supersymmetry (which is mentioned as a separate case of elegance) makes them coincide much better, and there are a variety of "grand unified theories" (really, various models). Of course, most of them are untestable at current energies and many predict the same observable things at energies we can reach. I guess it's elegant that the supersymmetry that's required to make string theories consistent also makes the GUT scale work out better! But then, supersymmetry has never been observed. The only real parameter it has is the number of supersymmetries, so you could maybe say N = 50 would an unnatural number of supersymmetries. But then 26 dimensions falls naturally out of bosonic string theory [3], so who's to really say? Having fewer supersymmetries would fall more under Occam's razor (which Hossenfelder mentions) than simplicity and naturalness as metrics of beauty getting in the way.

As mentioned above, sting theory (her other example of "elegance") isn't really "simple" if you're actually trying to work with it. It's really neat that e.g. the string boundary conditions become real dynamical objects ("branes") in string theory, but that weirdness is more why some physicists say that string theory is actually 23rd century alien math we discovered accidentally in the 20th century (that's a vaguely remembered comment that I forget the source of). Essentially, string theories are theories of more than just strings, and we do not fully know how much more yet.

Conclusion

The subtitle of Hossenfelder's book is "How Beauty Leads Physics Astray", and the implicit judgment is on string theory. Much like how hating on DSGE models [4] is popular in pop-econ, it seems hating on string theory is popular in pop-physics. Hossenfelder along with Peter Woit are well-known bloggers who frequently critique string theory (I think there might be a connection between blog popularity and critique). Hossenfelder's thesis is that too many resources are devoted to it. In a world with limited resources, this is definitely good to question. Woit just seems to think that it is too popular in pop-physics for how little he thinks it seems to have accomplished (I think, I'm not sure as his various writings come across as just pessimism rather than criticism; he mostly seems like that cranky grad student that finds the pessimism in any discussion).

I don't really get Hossenfelder's and Woit's impatience. It's been 40 years! they cry. Is the critique of too much emphasis on "beauty" just masking impatience? (Actually, one of the blurbs on the Amazon site says "Sabine Hossenfelder is impatient for new waves of discovery.") Did you think we'd go from the Standard Model to a theory of everything in your lifetime? It was 200 years before we started to upend Newtonian physics with quantum mechanics. Again, string theory is 23rd century alien math. 

The lack of empirical confirmation of anything specific to string theory is actually more a reason not to devote resources to any kind of high energy fundamental physics at all. There are no plans for dramatically larger accelerators than the LHC, so if you think there isn't going to be confirmation or rejection of string theory that means it's unlikely there will be any confirmation of any theory at the same scale.

In the end, criticism like this is the kind I dislike. We should do something different! Ok, what? Um, I don't know. Don't tell us we are on the wrong path, show it by finding a more fruitful path. Hossenfelder addresses this in a separate blog post:
As far as quantum gravity is concerned, string theorist’s main argument seems to be “Well, can you come up with something better?” Then of course if someone answers this question with “Yes” they would never agree that something else might possibly be better. And why would they – there’s no evidence forcing them one way or the other.
This seems an odd retort to the expectation to show something different is useful — a kind of tu quoque where something purportedly better has no evidence either. It's also a bit disingenuous because a string theory calculation did come up with the Hawking-Bekenstein area law. And even if there are some possible issues with that (firewalls), string theory still unifies all the forces and gravity into a single framework. Let's go back to Hossenfelder's own blog:
String theory arguably has empirical evidence speaking for it because it is compatible with the theories that we know, the standard model and general relativity. The problem is though that, for what the evidence is concerned, string theory so far isn’t any better than the existing theories. There isn’t a single piece of data that string theory explains which the standard model or general relativity doesn’t explain.


The reason many theoretical physicists prefer string theory over the existing theories are purely non-empirical. They consider it a better theory because it unifies all known interactions in a common framework and is believed to solve consistency problems in the existing theories, like the black hole information loss problem and the formation of singularities in general relativity. Whether it is actually correct as a unified theory of all interactions is still unknown. And short of a uniqueness proof, no non-empirical argument will change anything about this.
I would say this — being compatible with all the empirical successful theories while unifying them, but just not giving anything extra — is a remarkable feather in string theory's cap. In a sense, Maxwell's equations unified a bunch of known electromagnetic forces into a single framework in this same way. Later it was discovered to have some issues that Einstein solved with his 1905 special relativity paper (note that those issues were in fact the impetus for Einstein's paper and why it's titled "On the Electrodynamics of Moving Bodies").

Also, don't tell me that better path is loop quantum gravity because it violates Lorentz invariance. For all the failings of string theory, at least is doesn't violate one of the most empirically successful theories to come out of physics. In fact, if string theory was languishing and all the resources were going to loop quantum gravity, I'd be totally on-board with Hossenfelder/Woit style criticism of loops and calls for redirection of resources to other areas.

But then, who's to say resources are being misdirected? I don't know about Hossenfelder or Woit, but even when I was studying boring (to quantum gravity people, at least) QCD I frequently wrote down attempts to come up with pieces of a possible final "theory of everything". I speculated about the common coefficients in quantum field theory calculations being related to to Galois groups — something that is currently being studied. I had a wild idea to re-make the idea of smooth manifolds in mathematics as essentially leading approximations to some new underlying space and tried to understand its topology (I think I just re-invented fractional derivatives, though). I was always messing around with possibilities — random ideas that were essentially funded by my nuclear theory research position. I imagine most string theory practitioners do the same thing. Einstein did his work while being "funded" by the patent office. Heck, some string theory concepts like holography may be illuminated by incredibly simple models that seem to have started out as just messing around. I could imagine training in string theory would be decent background for understanding a final theory, whatever it turns out to be.

The trick is to keep new students funded and engaged. I don't think the specific projects that get funded are necessarily that important. Can you imagine? A big government agency picking research projects and their choice is what ends up as the final "theory of everything"? Not to go all libertarian on you, but that kind of top-down direction seems unlikely to generate breakthroughs. Breakthroughs often come while you were studying something else [5]. String theory itself started as essentially a side project looking at the details of a simple model of mesons (that was later rejected for the better QCD). Who knew funding that would lead to a string theory-industrial complex that Hossenfelder claims is eating all the resources?

I guess I'm saying: who really knows where innovation comes from? Why is motivation through beauty not a source of innovation? Why is wasting resources on string theory not a source of innovation? Maybe even writing books about wasting resources on string theory is a source of innovation to those that read them.

In the end, any final "theory of everything" that describes all matter, energy, space, and time is going to be beautiful regardless of what it looks like because it describes all matter, energy, space, and time.

...

Footnotes:

[1] This is an insider joke; a reference to "Witten's wisecrack" (as described by Sidney Coleman) that said in natural units, the perturbative expansion of QED in the coupling constant of about e ~ 1/3 was no worse than the large-Nc expansion of QCD with Nc = 3.

[2] Expanding a bit:



[3] It's weird, but the Zeta-regularized sum of natural numbers is ζ(−1) = −1/12, and in order to make a cancellation, you end up with 2 − 1/ζ(−1)  = 26 (if I remember correctly). Also, the Casimir force is attractive because this number is negative.

[4] DSGE models are actually pretty general — just a few canonical elements seem to be included out of inertia (Phillips curves, Euler equation).

[5] Not to say information equilibrium should be hailed as a "breakthrough" (yet!), but it came about from studying compressed sensing.

PCE inflation and checking forecast validity

This post isn't going to be very exciting, but sometimes science is just doing the legwork. PCE price level (inflation) data was released last week — the forecast is doing fine:

Monday, June 25, 2018

Yield curve inversion and a future recession

There was a recent article out on the internet about yield curve inversion. Using the spread between Moody's AAA rate (blue, a decent proxy for the 10-year rate with less noise) and the 3-month secondary market rate (purple) we can see that from the 1950s until today, a low spread has been associated with recessions:


However, in the aftermath of the Great Depression, inversion of this measure wasn't a good indicator. It's only become an indicator since the 1950s. The past few recessions have all been preceded by a closing of this measure, but the degree of closing has gotten smaller since the 80s (actual inversion before the 90s has turned into just entering there error band):


Looking at the recent data and assuming the dynamic equilibrium model is correct along with a linear trend in rate increases, we see that the indicator will enter the error band sometime before 2020:


However, the period of time the spread spends inside that error band ranges from a few months to a year (yield curve inversion is usually described as being an indicator a recession will happen within a year). So unless we have other data, we won't be able to predict the timing of this future recession. We do have other indicators, and this extrapolation is consistent with them.

...

Update 26 June 2018

I've done a better analysis of the estimate of the US recession onset via the yield curve inversion indicator by aggregating several different measures of the spread (collected here). I looked at the median (yellow), average (blue), and a principal component analysis (green). These gave nearly identical results:


Since those were practically identical, I used the mean median for the subsequent calculations. I then extracted the slope of the approach to the three previous recessions (early 1990s, early 2000s, and the "Great Recession" of 2008, dropping the first year after the start of the previous recession) using a linear model, and used that slope to estimate the most likely recession onset (first quarter of the NBER recession) for a future recession (assuming the current decline in spreads will eventually lead to a recession). That value is 2019.7 ± 0.3 (two standard deviation error). This is what the current approach to the recession looks like in that context (the previous three recessions are shown in blue, yellow, and green and labeled by the end of the NBER quarter — i.e. 0.5 is the end of calendar Q2):


The blue band represents the 90% confidence on the single prediction errors of the linear model (dashed line). Since the declaration of an NBER recession typically lags the first indications of a recession in unemployment and JOLTS data, we should be seeing the first signs in those data series in the next 6 months to a year. Since we are already seeing some signs in the JOLTS data, these indicators all seem consistent.

Note that the above analysis in this update is "model agnostic" in the sense that it just relies on the empirical regularity of a trend towards yield curve inversion between recessions, but no specific model of how yield curve inversion works or which way causality goes. It does imply a certain inevitability of a recession. Since the mean spread rose to about 3 percentage points after each recession, and the slope is -0.36 percentage point per year, this implies about 8.3 years between recessions — which is what a Poisson process estimate says based simply on the frequency of recessions (λ ~ 0.126/y, or an inter-arrival time of 7.9 years as mentioned here).

...

Update 3 July 2018

Apparently I mislabeled the variables medianData and meanData in my code, switching them up. Anyway, the above result uses the median, not the mean (average). I also added post-forecast daily data in red, which is more rapidly updated than the monthly data time series.

Tuesday, June 19, 2018

Q: When is bad methodology fine?

A: When you clearly say it's bad methodology.

For example, I am currently playing around with housing starts data and the dynamic information equilibrium model (DIEM). It really only looks like the data from about 1990 on can be described by the model (which interestingly matches up with a similar situation with the ratio of consumption to investment).

However, I noticed something in the data -- if you delete the leading edges of recessions, the DIEM works further back. It's possible that a step response is involved; here's the log-linear transform of the data:


It's totally bad methodology to just willy-nilly delete segments of data by eye, and I wouldn't create a forecast with this model result that I'd take seriously. I won't even transform back to the original data representation to help prevent this graph from being used for other purposes. But sometimes I notice prima facie interesting things, and as this blog effectively operates as my lab notebook [1] I try to document them. They could turn out to be nothing! Why? Bad methodology!

Footnotes


[1] There's apparently an "open notebook" movement that I guess I've been a part of since 2013.

Monday, June 18, 2018

[Insert new approach to economics] isn't empirical


Going beyond a sub-tweet into an entire sub-blog, my least favorite genre of econ writing is the "economics should try [insert new approach to economics]" that is entirely supported by only two pillars: subjective analogies and that the writer himself (nearly always a him) studies [insert new approach to economics]. The gaping hole in these pieces is whether or not [insert new approach to economics] actually describes any real world empirical data [1]. The galling thing about this is that a common (although tired and largely over-simplified) criticism of economics is that it "isn't empirical". The worst offenders are the ones hypocritically doing both [2].

Sometimes calls for economics theories — fundamentally about social phenomena — to be more empirical are taken as calls to make economics more like physics and remove any messy human imprecision. This is not what I am talking about. Darwin's book [3] doesn't have math or numerical data for the most part, but still represents an empirical study conducted through observations. The theory not only presents and organizes the data, but explains many observed things — including things Darwin did not study himself. It also made concrete predictions (e.g. transitional forms). But describing how an aye-aye's middle finger elongated to help get grubs out of wood is not going to be "precise" in the physics sense where you'd be able to put the model in a computer and "predict" the finger based on a set of inputs.

I am not so pessimistic to think that it is impossible to toss a bunch of observations of interest rates and prices into a computer and accurately forecast future GDP, but I am open to the possibility that a successful economic theory may well be more narrative — like Darwin's book.

But as I mentioned, Darwin's book is empirical in the sense that it is based on observations. Not just recording observations, but explaining observations. So when I read things about how economics should use [insert new approach to economics], I am looking for the observable things that [insert new approach to economics] explains. Since the vast majority of macroeconomic data is numerical, the easy way is for [insert new approach to economics] to explain some of that data. Even just a couple time series. We can argue about whether those time series are measuring real things (like here), but at least show something.

Without those explanations of observations, a purported new approach to economics is just an assertion, an untested hypothesis. That [insert new approach to economics] was studied in (usually) the author's own field [4] is not in any way a valid test of its applicability to economics. Quantum mechanics works on electrons, therefore it should work for human brains. Um, really? Evolution works as a theory of how living organisms change over time, therefore it will work for macroeconomies. Okay ... want to show me some macro data it explains?

The econoblogosphere is full of this stuff, and I'm sure it's fun to talk about for some of you. Talking philosophy in a bar can be amusing. Story-boarding is much easier than actually making a movie. It all reminds me of the meetings I've gone into at my real job where the agenda is to find a solution to a specific problem, but everyone in the room just wants to talk about the approach or other generalities. When I was younger, I used to think this was because the people didn't know what they were talking about and they were afraid if they made any concrete statements we'd find out. I'm now a bit more laid back, but I still roll my eyes. I'm interested in trying to figure things out. That's why I became a scientist. 

That's also why my blog, papers, and presentations have been focused on explaining empirical data. The econoblogosphere doesn't need yet another white male spewing hot air  about "money" or saying "economists should try" [insert new approach to economics] because of analogies or "logic". Economists should try information theory because it provides a pretty accurate model of several labor force measures.

If you think your pet theory [insert new approach to economics] is so cool, do some actual work [5] and show how it improves our understanding of micro- or macro-economic phenomena we observe. Rational agents and methodological individualism may be flawed constructs, but they've almost certainly produced results that have been compared to more empirical economic data than your precious [insert new approach to economics].

...


Footnotes

[1] I understand this last phrase is redundant; it's written for emphasis.

[2] Actually, the worst offenders are the people that claim their model explains empirical data when it doesn't.

[3] I am not saying Darwin was the complete and last word on evolution — much research has been done since then. I am using it as a common example where the empirical "data" isn't numeric as a way of saying both "physics isn't the only good science example" as well as "social sciences can be empirical without numbers".

[4] That author almost invariably has only a limited knowledge of economics as well. That's because the typical place this kind of "you should be using [insert new approach to economics]" comes from seeing macro struggle with empirical validity and assuming everything economists must have learned must be garbage. The reason macro struggles with empirical validity is that it's difficult — every macro theory will struggle with empirical validity to some degree. Even [insert new approach to economics]. I've had a few models built with the information equilibrium framework that I've rejected.

[5] I wanted to add "you lazy feckless windbag" here, and really just a lot of invective almost everywhere. You're not helping, and you're just feeding an "evidence-free" approach to macroeconomic policy (because that's what your half-baked ideas about how economies work become).

Thursday, June 14, 2018

Wage growth showing signs of a downward shock

The latest wage growth data from the Atlanta Fed came out a few days ago, and like the JOLTS data is showing possible signs of a recession (or at least the undoing of the prior upward shock that might have been associated with a post-Lilly Ledbetter decline in the gender pay gap). Here's the latest data on the original forecast:


Also, I looked into the Employment Cost Index (ECI) which is another measure of wages and compensation. First the original model of the log derivative:


And since I was comparing with this picture from Ernie Tedeschi:


... I reconstructed the year-over-year model adding the extra data from the previous graph by digitizing it (for some reason, it wasn't on FRED):


Because this data is noisier and less frequently updated (only quarterly, roughly a month after the quarter ends), we can't really see any sign of a recession yet even if there was one.

...

PS Note that I use the Atlanta Fed's raw data, not the 3-month moving average they display on the website linked above.

Wednesday, June 13, 2018

Explaining recessions with definitions?

Nick Rowe is an excellent educator, and always has really nice "parables" to explain some point; this recent one is about the excess demand for money (the medium of exchange) causing recessions.

But it's a just-so story. The only way agents can satisfy their demand for goods is through monetary exchange of money earned through monetary exchange, and satisfying that demand is defined as equilibrium. Therefore non-equilibrium (i.e. recession) is effectively defined as insufficient monetary exchange (i.e. excess demand for money).

Actually, I could use the same exact system (with the same effects) but have a recession defined completely differently: agents huddling in a particular corner of state space.

When Nick says the agents get an excess demand for money, I'd say agents decide to occupy a particular corner of the available opportunity set (state space) that involves holding (not spending) as much money, cutting off a segment of it. Agents no longer fully explore the state space and entropy is no longer maximized. This generalizes to exactly the same generalization Nick makes:
It is not an excessive desire to accumulate assets that causes recessions; it is an excessive demand for one particular asset (the medium of exchange) relative to other assets. It's about the composition of their portfolios of assets, not about the total size of that portfolio.
There is a maximum entropy distribution (subject to some constraints) over assets (portfolio), and deviations from it represent a loss of (information) entropy. I've suggested before that this kind of correlation in state space is a possible description of a recession. Notably, I don't define this as a recession. I just look at the consequences and note that it describes a recession without getting into the details of why agents have decided to correlate in a corner of state space. 

Here's an example where one agent on the Wicksellian circle decides to have an excess demand for "money" (you can imagine goods flowing in the opposite direction):


The key point here is that we've abstracted what Nick defines as an excess demand for money (observable only as a recession) as a correlation in state space. Nick defines a specific correlation in state space as an excess demand for money, whereas we leave it open.

That's because looking at the data, it's hard to say exactly what it is humans are doing as an economy heads into a recession. Job Openings take a hit (as well as hires) before unemployment begins to rise. Of course, that could be defined as firms having an excess demand for money (i.e. not spending it on employees). However, that doesn't add much information unless somehow giving firms money would cause them to put out more job openings. Does it? That seems like an empirical question, not one answered by a logical parable. And of course you could characterize a decline in conceptions as an excess demand for money (i.e. not spending it on a baby). 

But now we have a question of why the excess demand for money shows up first in conceptions and then later in hires and job openings (with the latter coming in different orders in different recessions). Why does this excess demand for money show up last in wages firms pay employees? Firms first hold back on hiring, and then hold back on raises (actually it's wage acceleration) — but both could be characterized as an "excess demand for money" by firms. Nick's definition of a recession is now seriously lacking in explanatory power for the details. Is demand for some kinds of money (i.e. reduced spending on certain things) different from other kinds? The correlation in state space framework doesn't necessarily restrict exactly how the firms correlate: they could first correlate in hiring decisions and then later in wage decisions. Different parts of state space are going to be different and there's no reason to expect them to behave in the same way.

In Nick's example (assuming that world actually existed for a moment), what if data showed the recession first showed up as a decline in banana production, and then later apples and cherries? As constructed, the model could only explain a recession where the onset of the recession was the same across each fruit. You could add in ad hoc delays to the production of each good (i.e. apples take longer to grow than bananas, so banana production is more pro-cyclical).

I've noted this before, but I see this as a general problem with people who study macro — mainstream to heterodox, econophysics to complexity: defining a recession. Your conceptual framework should not define what a recession is. A recession is one of the main subjects of study of the field of macro. Defining what a recession is assumes the answer. Now in some cases assuming the answer and trying to work out what the theory has to look like to produce that answer is a useful theoretical tool. But it's a useful theoretical tool for finding the answer, not explaining the answer.

In the previous link, I came up with what I thought was a good analogy to assuming what a recession is in order to explain it:
If I said I was a doctor studying Alzheimer's and my conceptual framework included a tenet that Alzheimer's disease was defined by amyloid plaque build-up (rather than, say, the stereotypical symptom of memory loss) and lo and behold I put up some micrographs of amyloid plaque build-up in a neuron and said that caused Alzheimer's ... exactly what is my conceptual framework helping me understand?
Nick defines a recession as the excess demand for money, and lo and behold his parable shows that an excess demand for money produces a recession!

But recessions in data are defined by a bunch of people squinting at it (NBER) or heuristics like two consecutive quarters of negative GDP growth. If a recession is an excess demand for money, why does it only last two quarters? That's a joke, but you can see what I'm getting at. The excess demand for money explanation basically shifts the question to why people have excess demand for money for short periods where it manifests in reduced spending in different amounts on different things at different times. That is to say: it's no explanation. 

Tuesday, June 12, 2018

CPI inflation forecast from 2017 still going strong

I made a forecast of CPI (all items) that I've been tracking since 2017, and it's still doing fine with the latest data (showing the ending of "lowflation" in the wake of the Great Recession due to a drop in labor force participation):



...

Update

I'll throw in this S&P 500 forecast comparison with the latest data for absolutely free: