Wednesday, November 14, 2018

Data dump: JOLTS, CPI

Checking in on the dynamic information equilibrium model forecasts, and everything is pretty much status quo. The JOLTS hires data [1] is showing even fewer signs of a recession than before, but job openings is still on a biased deviation. Based on this model which puts hires as a leading indicator, we should continue to see the unemployment rate fall through February of 2019 (5 months from September 2018), at which point it will be 3.8 ± 0.2 % (90% CL) [2]. Additionally, CPI inflation is well within expected values. And finally, the S&P 500 forecast is still on a negative deviation, but within the norms of market fluctuations. As always, click to enlarge.

JOLTS




CPI inflation (all items)


S&P 500


Footnotes:

[1] The old hires without the 2014 mini-boom is here:


[2] October's 3.7% was on the low end of the CL — it was expected to be 3.9 ± 0.2 % (90% CL), so there might be a bit of mean reversion between now and March (when the February numbers come out).

Unions, inequality, and labor share

I've started writing the first draft of my next book, so I've been trying to gather up all the dynamic information equilibrium model results into economic seismograms [1] to try to provide a complete picture. In the gathering, there have been some unexpected insights — this time about unions and their effect on inequality. Here's the seismogram in the new style that can be displayed on a Kindle [2] (click to enlarge):


This shows the civilian labor force (women), wages, manufacturing employment (as a fraction of total employment), the labor share of output (nominal wages/NGDP), unionization, and income inequality (using Emmanuel Saez's data).

One of the interesting things I noticed was that unionization and inequality show almost exactly the same pattern: each bump up in unionization sees a bump down in inequality a few years later, and the decline of unionization in the 80s is followed by rising inequality in the 90s.

What's also interesting is that the decline in the labor share of output starts happening before unionization declines — i.e. a decline in unions wasn't the predominant way labor lost its share of output. I've talked about my hypothesis for a more likely causal factor before: labor share declined as women entered the workforce because the US pays women less than men. A rough order of magnitude calculation where capital just pockets the extra 30 cents on the dollar they save by hiring a woman gets the expected decline in labor share about right.

...

Update:

The unionization model is discussed here:


And here are the models of inequality and labor share (also here for the latter):

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Footnotes

[1] One of the other things I realized in the process was that it's not seismograph, which is the machine, but seismogram. I went back on the blog and corrected all the references in the posts.

[2] Comments are welcome, but be sure to click to see the higher resolution version. Dark bands are negative shocks (or "bad" shocks), while the lighter bands are positive (or "good" shocks).

Tuesday, November 6, 2018

A workers' history of the United States 1948-2020

On my book blog, I'm starting up the next book tentatively titled A workers' history of the United States 1948-2020 based on some of the dynamic information equilibrium model results and macroeconomic seismograms. Take a look ...


I'll say similar things for half the salary

Jan Hatzius made some macro projections about wages, unemployment, and inflation:
Goldman’s Jan Hatzius wrote Sunday that unemployment should continue to decline to 3% by early 2020, noting the labor market also has room to accommodate more wage growth. Hatzius predicted that average hourly earnings would likely grow in the 3.25% to 3.50% range over the next year. ... For now, Goldman has a baseline forecast of 2.3% for core PCE ...
Well, these are all roughly consistent with Dynamic Information Equilibrium Model (DIEM) forecasts from almost two years ago (early 2017, except for the wage growth which is from the beginning of this year). Hatzius' unemployment forecast is a bit lower (I'm currently guessing there will be a recession that will begin the raise unemployment in the 2020 time frame based on JOLTS data making both of these forecasts effectively "counterfactuals"). His wage forecast is consistent but biased low compared to the DIEM, while his inflation forecast is consistent but biased high compared to the DIEM. 

Of course, there's a hedge:
Hatzius said that the economic outlook is still subject to change from a number of geopolitical factors, such as the U.S. midterm elections on Tuesday [today] ...
The DIEM forecasts will generally only change if there is a recession, but as we haven't seen any real impact on JOLTS hires (see here) we should continue to see the unemployment rate fall through January of 2019 (5 months from August 2018) and wage growth increasing through July 2019 (11 months from August 2018).

Here are the graphs — click to enlarge:




Sunday, November 4, 2018

Construction hiring, the Great Recession, and the ARRA

In the previous post, I talked about a drop in construction hiring (a JOLTS subcategory) as a leading factor in the Great Recession. Compared to the rest of the macroeconomic observables, construction hires is first to fall. Here's the model for the full data set, which includes an additional bump that seems very likely due to the fiscal stimulus of the American Recovery and Reinvestment Act (ARRA, aka the "Obama Stimulus"):


Interestingly, the NBER recession (light orange bands) cuts off right when the boost in construction hiring begins. None of the other JOLTS series show this bump at this time. Some, like health care job openings (and unemployment), show a positive bump starting in 2014 along with the ACA, aka "Obamacare". Overall JOLTS hires (i.e. across industries) shows a bump in 2014 as well (the 2014 "mini-boom").

Saturday, November 3, 2018

An information equilibrium history of the Great Recession

I mentioned at the beginning of this year on my book website that I was thinking about writing another book about the macro history of the US as told through dynamic information equilibrium and the resulting economic seismograms. I've been collecting the various models on this blog to put them together into graphics that tell at least one version of history. Previously, I've given evidence that women entering the workforce leads nearly every other measure of growth and inflation in the 70s and 80s. Lately, I've been working on the Great Recession. Here's the seismogram (click to enlarge):


Red-orange indicates negative (i.e. bad) shocks, while blue indicates positive (i.e. good) shocks (rising unemployment is "bad", but rising income is "good"). The labels are identified in footnote [1].

While much of the focus of commentary about the recession was on the Lehman collapse and the Fed meetings immediately preceding it (along with the fall in the stock markets as measured by the S&P 500), these actually come in the middle of the recession process . The first thing that happens by far is the drop in hires in construction (labeled "HIR 2300" based on the JOLTS code) in mid-2006. Around that time, Paul Krugman (e.g.) was talking about a housing bubble deflating (he had been forecasting it earlier in mid-2005) [2]. The shock to housing starts (HS) doesn't come until later (though the shock to starts occurs over a longer period, you can see that hires begin to decline just before housing starts begin to decline).  The drop in construction hires also comes right before the halt in the Fed rate increases that had started in 2004.

Before the NBER-defined recession gets underway, there's a drop in conceptions (per this NBER working paper) that's roughly coincident with (but genuinely followed by) two Fed conference calls in 2007 about the financial markets reeling in the collapsing housing bubble (the negative shock to the Case Shiller index) as well as the first Fed rate cut. The rest of the stuff that is associated with a recession in the media (stock market drops, GDP declining, unemployment rate rising) all come much later during the NBER-defined recession.

Personal income (PI) continues to climb ahead of its typical pace through most of 2007, and wage growth continues to increase (i.e. accelerate) almost until the NBER recession end.

While I've heard many stories about excessive debt being a cause behind the Great Recession, most of the negative shocks to debt measures come later (i.e. debt became a problem because of the recession). Although not shown in this graph, consumer credit takes a hit only as the NBER recession is ending. This is not to say that debt levels didn't contribute to the size of the recession (i.e. making it worse), but rather that they didn't contribute to its timing (i.e causality).

Any causality analysis would put construction hires at the beginning of the story, but oddly the shock to construction job openings comes along with the rest recession — barely leading the shock to job openings of all kinds. In fact, there's a surge in openings around the same time. It's the largest difference in timing for all the JOLTS sectors. That is to say jobs were still being advertised in 2006 (until 2008), just fewer were being hired. This doesn't indicate a pessimism about the housing market (which seems like it would show a fall in openings), but rather a labor shortage of some kind. Were employers unwilling to raise wages? Unemployment had reached its lowest level since before the 2001 recession, so maybe there was a genuine shortage of workers.

Was it xenophobia?


I am going to offer a speculative answer that I do not think I have ever seen offered as a possible reason for the Great Recession: xenophobia. There were a series of protests from March of 2006 to against anti-immigrant legislation being introduced (some of which passed, and in various jurisdictions E-verify was mandated in 2006 to prevent employers from hiring undocumented workers). The shock to construction hires begins right around the same time as those March protests, and every year since 2004 saw a decrease in immigration from Mexico:


The linked article doesn't get this causality right:
Immigration from Mexico dropped after the U.S. housing market (and construction employment) collapsed in 2006. By 2007, gross inflows from Mexico dipped to 280,000; they continued to fall to 150,000 in 2009 and were even lower in 2010.
According to their data, immigration started dropping before 2006 (the peak is in 2004), but given noise in the data and the annual temporal resolution the best we can say is that construction employment and immigration from Mexico dropped approximately concurrently.

I have written before on how much of an effect a drop of 2 million people in the labor force due to immigration restrictions would cause — about 1 trillion dollars in NGDP. Assuming a linear trend past 2007 in the increase in just undocumented immigrants (using Pew data), by 2009 there were 1.8 million fewer undocumented immigrants (11.3 million) than would be expected by the trend (13.1 million). While there would need to be more detail added (accounting for the decline in documented immigration as well as fraction of those two populations in the labor force), this gives us an order of magnitude that is not trivial compared to the size of the Great Recession.

Again, this is speculative. However it is not implausible that the anti-immigrant sentiment of the mid-2000s ended the "housing bubble". Employers continued to look for workers in construction, but suddenly were unable to hire as many starting in 2006 due to declining immigration. The worst hit states in the housing crash were California, Arizona, Nevada, and Florida — the first three being major destinations for documented and undocumented immigrants from Mexico. Since even undocumented immigrants spend money at the same grocery stores you do, sales decline. Declining construction hires is followed by fewer housing starts, and when a new family can't find a bigger house with more rooms they'll not only delay having children but opt to hold off on that house. Housing prices decline from their peak, but by now the general economic outlook is mediocre enough that the Fed starts to lower interest rates in 2007. Pessimism sets in along with the rest of the recession and a financial crisis that goes global. 

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Update 6 November 2018

A correspondent sent me a link to some work by Kevin Erdmann about how there was actually an under-supply of housing going into the 2008 recession. Now Erdmann is writing for Mercatus which generally means there is some kind of motivated reasoning. Here, that reasoning is an attempt to say there was no housing bubble because there was a "fundamental" reason (short supply). But then, there was a limited supply of tulip bulbs as well. If there was no housing bubble, then it's arguable that the Fed had unnecessarily tight monetary policy (i.e. the desired conclusion in this case). Seeing as monetary policy tends to lag other measures, it's probably not the cause (but may e.g. contribute to the broader conditions).

I also want to emphasize that it is almost entirely unlikely the shock to construction hires was the only causal factor. I see it more as a trigger or a straw that broke the camel's back — in an environment of higher interest rates and general pressure from policymakers to cool the housing market, a sudden shock to labor supply makes that "cooling" suddenly look worse in a way that could change one's outlook. In the information equilibrium approach, it's sudden coordinated action (e.g. panicking) causing agents to cluster in the state space that causes recessions. Sometimes that coordinating signal is the Fed, but it could easily be shock to labor supply due to an unwarranted immigration freak out.

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Footnotes:

[1] The labels are:

HIR 2300: JOLTS hires, construction (JTS2300HIR)
HS: Housing Starts (HOUST)
C/F: Conceptions/fertility
Case Shiller: Case Shiller housing price index (also here)
HIR: JOLTS hires
HIR-ext: Extended JOLTS hires data
JOR 2300: JOLTS job openings, construction (JTS2300JOR)
Debt growth: Growth of debt (All Sectors; Debt Securities and Loans; Liability, Level)
JOR: JOLTS Job opening rate
JOR Barnichon: Job openings in data from Barnichon (2010) [pdf]
QUR: JOLTS quits
SP500: S&P 500
U: U3 unemployment rate
PI: Personal income
PCE: Personal consumption expenditures
NGDP: Nominal Gross Domestic Product
W: Wage growth (Atlanta Fed)
Debt to GDP: Ratio of previous debt measure to NGDP
CLF: Civilian labor force (CLF16OV)



The arrows on the top of the diagram indicate the two Fed meetings (black arrows) prior to the Lehman collapse (red arrow). The arrows on the bottom of the diagram show the first Fed rate increase since the 2001 recession, the beginning of the period of steady rates (mid-2006 to mid-2007) as well as the first rate cut going into the 2008 recession.



[2] I'm not tying to make any point here about "who saw the crisis coming" — only citing some news that I remembered from the time for context.

Friday, November 2, 2018

Unemployment: forecasts and reality

The unemployment rate came in unchanged for October at 3.7%, which is still like 0.1 percentage points below the forecast — but it's a forecast from January 2017, so not bad for almost two years.


It looks even better compared to the competition. The FOMC and FRBSF forecasts of the same vintage definitely didn't capture the decline, with the latter being off by about a full percentage point (click to enlarge):



New fair forecast comparisons


I also put together some new (fair) comparisons with projections from the CBO, FRBSF, and FOMC starting in 2018. Note that I actually expect the path of unemployment to follow something like one of the "recession" curves in the CBO forecast graph because of what look like leading signs of a recession in the JOLTS data (which lead unemployment by several months). In the meantime, all we can do is project what the model shows and wait for the signal to appear in unemployment data [1].





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Footnotes:

[1] See this post — here's an example for the previous recession and 2014 positive shock:


Tuesday, October 30, 2018

Comparing my S&P 500 forecast to observations

Disclaimer/disclosure: It is entirely possible I am a crackpot physicist who has deluded himself (always a 'him', amirite?) into believing he has figured out some structure in the stock market. You most likely do not want to wager the change in your pocket on that, much less your life savings. The model presented is for purely academic curiosity purposes, not stock advice. I have my 401(k) invested in an S&P 500 index fund and own a few shares of Boeing stock.
I've been testing a dynamic information equilibrium model (DIEM) forecast of the S&P 500 (a.k.a. hubris) since January 2017, and we're now about 2 months from the original end date of December 31, 2018 [1]. It has worked remarkably well (click to enlarge):


The model is the red line, the red band are the single prediction errors (90% confidence) over the entire model data set (1950-2017), and the blue band is the 90% ARMA(2,1) forecast from the last data point (December 2017). The black line is the post-forecast data.

The current data is well within the "normal" range of fluctuations. I've added a red dashed line to indicate the "recession warning" level. Should the data exceed this threshold, the model would potentially indicate the presence of a shock (usually associated with recessions) [1]. Actually, the S&P 500 seems to have a bit of multi-scale self-similarity (a property of "fractals"), so as you zoom in more and more shocks of ever smaller magnitude and ever shorter duration can be resolved. Whether or not there's a shock associated with a recession depends a bit on the scale of the recession. All that's to say the S&P 500 isn't exactly a good recession indicator on its own (labor market measures are better and tend to be leading indicators).

I've heard questions lately in the business news about whether we are experiencing a market "correction" — a fall on the order of 10%. This probably reflects some genuinely useful heuristic. However that metric is too vague (over one week? two weeks? a month? from what level?) to be of value. The DIEM view could make that more explicit as a 10% deviation from the trend (red model line) is roughly at the bottom of the blue band [3]. It's reasonably close to the "recession warning" line so as to consider the metric as part of a more general concept that separates normal fluctuations from shocks [4]. However, the data has passed the "correction" line several times since 2010 (but not the "recession warning" line).

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Footnotes:

[1] I've extended the forecast another year (assuming no shock, click to enlarge):


[2] Here's the longer term overview with recessions (blue) included (click to enlarge):


[3] More explicitly — actually a 0.1 log-deviation which is approximately 10% (click to enlarge):


The width of the blue band is based on the short run (i.e. no recession) volatility (2010-2017) rather than the long run volatility (1950-2017) which is represented by the red band.

[4] This on volatility regimes is also relevant.

Thursday, October 25, 2018

Keen

Because it comes up from time to time, let me collect my various criticisms of Steve Keen's work in one place with short summaries. I've been critical of his claims and approaches on this blog for years. People keep linking me to his nonsense, so I thought I'd create a reference post for why I think he's talking nonsense.

TL;DR Keen seems to thrive in a niche in heterodox econ where his fans aren't technically savvy enough to realize what he says doesn't make any sense, but he includes enough political polemic, name dropping, and chumminess with other heterodox "schools" that he can construe any attack on his claims as politically motivated or expect those "schools" to rise to his defense. Failing that, he says that people don't understand what he's saying. As I'm practically a Marxist (and even pro-Minksy) and am well-trained in mathematics and model-building, I'm in a pretty good place to push back against this nonsense. I was also what is called a phenomenologist in theoretical physics — I connected theory to data — which gives me particular expertise understanding the connected roles of theory and empirical data. This last element is a major failure in both mainstream and heterodox economics. Keen was trained as an economist, so that's probably why he's so bad at determining whether his models represent any kind of empirical reality — or even a "realistic" starting point. 

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May 2018

Dirk Bezemer wrote a paper where he claims that heterodox economics predicted the global financial crisis. Almost all of his references are quotes either taken out of context or completely fabricated. I stand by my assessment, but have published Bezemer's response to let people judge for themselves. Regardless, Steve Keen's website cites Bezemer's paper. However, the reference for that "prediction" was not a prediction of a global financial crisis, but rather an Australian housing crisis which has never appeared (with Keen in fact losing a bet over it). Bezemer's defense against my charge of fabrication only says that Australia made a policy change but clearly says the prediction was about Australia. As far as I know, Keen has never said in public that he never predicted the global financial crisis — and he should disavow such claims when made by his proponents.

Update: Oh, jeez. Keen himself [pdf] cites Bezemer while simultaneously claiming he has a "model" that predicted the global financial crisis. This is really bad.

September 2017

Keen doesn't understand the second law of thermodynamics and claims that log-linear regression is "false" if it is called a Cobb-Douglas function. He just sounds stupid. I go on to produce evidence that might support one of his claims (because he apparently forgot what supporting evidence looks like).

August 2017

Keen smugly derides an economist using "the word 'complex' while clearly not understanding its modern meaning". He then says "it's maths semantics by the way, not economics. Look it up." However, Keen often refers to his models as "complex dynamical systems" or "complex systems" when they are in fact just called dynamical systems. Look it up. Just because you have a nonlinear set of differential equations doesn't mean you have e.g. a complex adaptive system (Paul Cilliers even says of complex system that "conventional means (e.g. a system of differential equations) not only become[s] impractical, [but] cease[s] to assist in any understanding of the system.").

April 2017

This is just a footnote where I talked about seeing Boom Bust Boom (2015) — I'll just quote it: [The movie] brought on Steve Keen for a second to mention money and debt. However, later on they had someone else say that economics shouldn't be approached like a branch of theoretical physics. If I had to pick an economist who used the most inappropriate physics models, it would be Steve Keen who treats the economy like it's a nonlinear electronic circuit. It's just a very odd juxtaposition.

February 2017

So many of Keen's defenders tell me that his models are just qualitative or toy models, so they won't get the data right. If that's how it is, I wonder how these same people (and Keen himself) can be so down on DSGE models which are actually much better at getting the qualitative behavior right. Regardless, Keen's models are qualitatively accurate in the same way rocks are qualitatively food. This might be an area where Keen and his defenders aren't technically savvy enough to understand what the qualitative features of model outputs actually are. This also appears to be a general failure in economics (here's me saying the similar things about DSGE models, for example).

October 2016

Keen says that Kocherlakota's note represents a defense of his (Keen's) approach, but Kocherlakota's note explicitly says the case where a worse model empirically might be taken more seriously is when that worse model has better microfoundations (microeconomics). Keen's models have no microfoundations (nor even a basis in microeconometrics), and he has often criticized the idea or held microfoundations responsible for DSGE models (e.g. here or citing Solow on DSGE here). Keen doesn't seem pass basic reading comprehension in this case.

October 2016

This post was an elaboration on a post by Roger Farmer about how we can't really tell the difference empirically between nonlinear models and linear models with stochastic shocks. As Keen's models don't look anything like the empirical data (even qualitatively, see above), this is kind of moot. So really, Keen's insistence on nonlinear dynamical systems is based on nothing — you can't tell the difference between it and other approaches, and it doesn't have the benefit of being a good model of the empirical data in the first place.

February 2016

This one really pissed me off. Keen is referencing mathematics lots of other people don't really understand in a way that's not really appropriate to the economic system to claim that mainstream economists are full of it and that capitalism is mathematically proven (ha!) to be unstable. It pissed me off because either Keen understands the math and is basically deceiving people who don't know any better, or doesn't understand it and engaging in a bit of, um, rectally disseminated speech. Keen's claim is like saying all buildings are unstable and will collapse soon by asserting a definition of "building" that can only be a literal house of cards. Either he knows what he's doing and is a snake oil salesman, or doesn't know and should really just go back to school. 

December 2015

I started off my earliest blog post referencing Keen by saying "I'm not sure I understand the allure of Steve Keen." Keen's models are equivalent to nonlinear circuits in electronics, and as a person who has built a couple over my lifetime I am aware just how easy it is for noise or small deviations in e.g. resistor specs to completely ruin the chaotic limit cycles (the things that Keen likens to the business cycle). These circuits oscillate in only narrow ranges of component values (i.e. model parameters). And that fine-tuning problem comes before you even get to issues of the Lucas critique (why should parameters stay in the finely-tuned areas of phase space where the system exhibits a chaotic or nonlinear result for several decades). Instead of thinking of chaotic limit cycles as the result of a rickety, jury-rigged, non-deterministic underlying system (the image most people probably have in their mind when they think of chaos), they're actually the result of a finely-tuned deterministic system. It's more like the Newtonian clockwork universe (which, remember, includes the chaotic three-body problem) than the stochastic uncertainty of real economic systems.

...

I also wanted to note another author — J.W. Mason, a professor at CUNY, a fellow at the lefty Roosevelt Institute, and writes for Jacobin — that I think captured the essence so succinctly that I've referenced it multiple times. I'll just quote from it because I don't think I could possibly do better. 

J.W. Mason, April 2012
... if your idea is just that there is some important connection between A and B and C, the equation A = B + C is not a good way of saying it. 
Honestly, it sometimes feels as though Steve Keen read a bunch of Minsky and Schumpeter and realized that the pace of credit creation plays a big part in the evolution of GDP. So he decided to theorize that relationship by writing, credit squiggly GDP. And when you try to find out what exactly is meant by squiggly, what you get are speeches about how orthodox economics ignores the role of the banking system. 
Keen is taken seriously by serious people. He’s presenting this paper at the big INET conference in Berlin next week. It’s not OK that he writes in a way that makes it impossible to understand or evaluate his ideas. For better or worse, we in the world of unconventional economics cannot rely on the usual professional gatekeepers. So we have a special duty to police each other’s work, not of course for ideology, but for meeting basic standards of logic and evidence. There are very important arguments in Schumpeter, Minsky, etc. about the role of the financial system in capitalism, which mainstream economics has downplayed, just as Keen says. And he may well have something important to add to those arguments. But until he writes in a language spoken by people other than himself, there’s no way to know.
Whereas J.W. Mason points out that Keen's prose is opaque, I am pointing out in the list above that the modeling strategies and mathematics (i.e. the equations) are largely unjustified or inappropriate — not math errors per se (maybe, I haven't checked), but using math without tight connections to the claims about the system. Bad math plus opaque language is not a recipe for progress.

Wednesday, October 24, 2018

New Zealand's 2% inflation target

Paul Volcker has an article on Bloomberg about the 2% inflation target. Now I don't have any particular problem with arguing that central banks should focus on more than a numerical inflation target (the main idea of the rest of the article), but Volcker tells a brief story that is part of the whole "central banks controlling inflation" narrative that doesn't appear to be well-supported by the data.

Here's Volcker:
I think I know the origin [of the 2% inflation target]. It’s not a matter of theory or of deep empirical studies. Just a very practical decision in a far-away place. 
New Zealand is a small country, known among other things for excellent trout fishing. So, as I left the Federal Reserve in 1987, I happily accepted an invitation to visit. It turns out I was there, in one respect, under false pretenses. Getting off the plane in Auckland, I learned the fishing season was closed. I could have left my fly rods at home. 
In other respects, the visit was fascinating. New Zealand economic policy was undergoing radical change. Years of high inflation, slow growth, and increasing foreign debt culminated in a sharp swing toward support for free markets and a strong attack on inflation led by the traditionally left-wing Labour Party. 
The changes included narrowing the central bank’s focus to a single goal: bringing the inflation rate down to a predetermined target. The new government set an annual inflation rate of zero to 2 percent as the central bank’s key objective. The simplicity of the target was seen as part of its appeal — no excuses, no hedging about, one policy, one instrument. Within a year or so the inflation rate fell to about 2 percent.
The issue is that — using the dynamic information equilibrium model [DIEM] — inflation was already headed in that direction and it and the price level could have been forecast through 2018 (!) reasonably well back in 1983 (!) using only data available at the time (click to enlarge):


The forecast was made using data before 1983. The dashed red line is the post-1983 model and the green is the post-1983 data. That conference Volcker attended was in 1987, and the inflation target wasn't adopted until 1989.

The main feature of the data is the large shock centered at 1978.7, much like similar shocks in the UK and the US (which by the way didn't adopt inflation targets, and which also saw their inflation rate fall to some approximately constant level by the 1990s). The source of these shocks lasting from the 1960s to the 1990s seems to be demographic (women entering the workforce) in most Anglophone countries [1], so I wouldn't be surprised if it was demographic in New Zealand as well (unfortunately little good data going back far enough exists).

So Volcker's story is a bit like the fire brigade showing up after almost everyone has left the building and congratulating themselves for their good job saving lives. This is similar to the problematic causality around the 1980s recessions — often associated with the Volcker Fed. I'm not saying he's nefariously claiming credit for things — the interpretation is not completely implausible, and in fact most economists (even recent Nobel prize winners) subscribe to it. It's just difficult to square with the data. If you could forecast inflation today from 1983, it's difficult (but not impossible) to conclude events in 1987 had little impact.

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Update 29 October 2018

Nick Rowe in comments here mentions Canada's target:
How to test the effect of money on inflation? One example: in 1992(?) [ed. 1991] the Bank of Canada said it was going to use monetary policy to bring inflation down to 2%, and keep it there. And that is (roughly) what happened. Either the Bank of Canada got very lucky, or else monetary policy worked in (roughly) the way the Bank of Canada thought it worked.
We can actually play the same game as we played above to show that a forecast from 1985 gets the present day price level (CPI) to within about 1.4% (102.7 predicted versus 104.1 actual) over the course of 33 years (click to enlarge):


Nick says that unless the model is accurate, the Bank of Canada must have been "very lucky" to get "about 2%" right. But there are two issues: 1) what is "about 2%" (the actual dynamic equilibrium appears closer to 1.7% with 1.6% estimated from pre-1985 data so "about 2%" can mean up to a 50 basis point error), and 2) the data before the 70s surge in inflation was "about 2%". I don't have access to the Bank of Canada deliberations, but it seems unlikely that the choice of the 2% target was made without any consideration of this data before 1970. In fact, that's exactly the data the dynamic information equilibrium model keys in on to obtain the 1.6% estimate.

Since you can then forecast 2018's CPI using data from before 1985, it is hard to argue that setting the target in 1991 must have had an effect.

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Footnotes:

[1] Data for several countries, with the US, Canada and UK showing the demographic shift (click to enlarge):