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Point Forecast for Real Equity Returns in 2018

January 3, 2018 2 comments

Point forecasts are evil.

Economists are asked to make point forecasts, and they oblige. But it’s a dumb thing to do, and they know it. Practitioners, who should know better, rely on these point forecasts far more than they should. Because, in economics and especially in markets, there are enormous error bars around any reasonable point forecast, and those error bars are larger the shorter-term the forecast is (if there is any mean-reversion at all). I can no more forecast tomorrow’s change in stock market prices than I can forecast whether I will draw a red card from a deck of cards that you hand me. I can make a reasonable 5-year or 10-year forecast, at least on a compounded annualized basis, but in the short term the noise simply swamps the signal.[1]

Point forecasts are especially humorous when it comes to the various year-end navel-gazing forecasts of stock market returns that we see. These forecasts almost never have fair error bars around the estimate…because, if they did, there would be no real point in publishing them. I will illustrate that – and in the meantime, please realize that this implies the forecast pieces are, for the most part, designed to be marketing pieces and not really science or research. So every sell-side firm will forecast stock market rallies every year without fail. Some buy side firms (Hoisington springs to mind) will predict poor returns, and that usually means they are specializing in something other than stocks. A few respectable firms (GMO, e.g.) will be careful to make only long-term forecasts, over periods of time in which their analysis actually has some reasonable predictive power, and even then they’ll tend to couch their analysis in terms of risks. These are good firms.

So let’s look at why point forecasts of equity returns are useless. The table below shows Enduring’s year-end 10-year forecast for the compounded real return on the S&P 500, based on a model that is similar to what GMO and others use (incorporating current valuation levels and an assumption about how those valuations mean-revert).[2] That’s in the green column labeled “10y model point forecast.” To that forecast, I subtract (to the left) and add (to the right) one standard deviation, based on the year-end spot VIX index for the forecast date.[3] Those columns are pink. Then, to the right of those columns, I present the actual subsequent real total return of the S&P 500 that year, using core CPI to deflate the nominal return; the column the farthest to the right is the “Z-score” and tells how many a priori standard deviations the actual return differed from the “point forecast.” If the volatility estimate is a good one, then roughly 68% of all of the observations should be between -1 and +1 in Z score. And hello, how about that? 14 of the 20 observations fall in the [-1,1] range.

Clearly, 2017 was remarkable in that we were 1.4 standard deviations above the 12/31/2016 forecast of +1.0% real. Sure, that “forecast” is really a forecast of the long-term average real return, but that’s not a bad place to start for a guess about next year’s return, if we must make a point forecast.

This is all preliminary, of course, to the forecast implied by the year-end figures in 2017. The forecast we would make would be that real S&P returns in 2018 have a 2/3 chance of being between -10.9% and +11.1%, with a point forecast (for what that’s worth) of +0.10%. In other words, a rally this year by more than CPI rises is still as likely as heads on a coin flip, even though a forecast of 0.10% real is a truly weak forecast and the weakest implied by this model in a long time.

It is clearly the worst time to be invested in equities since the early 2000s. Even so, there’s a 50-50 chance we see a rally in 2018. That’s not a very good marketing pitch. But it’s better science.[4]


[1] Obligatory Robert Shiller reference: his 1981 paper “Do Stock Prices Move Too Much to be Justified by Subsequent Changes in Dividends” formulated the “excess volatility puzzle,” which essentially says that there’s a lot more noise than signal in the short run.

[2] Forecasts prior to 2009 predate this firm and are arrived at by applying the same methodology to historical data. None of these are discretionary forecasts and none should be taken as implying any sort of recommendation. They may differ from our own discretionary forecasts. They are for illustration only. Buyer beware. Etc.

[3] The spot VIX is an annualized volatility but incorporating much nearer-term option expiries than the 1-year horizon we want. However, since the VIX futures curve generally slopes upward this is biased narrow.

[4] And, I should hasten point out: it does have implications for portfolio allocations. With Jan-2019 TIPS yielding 0.10% real – identical to the equity point forecast but with essentially zero risk around that point – any decent portfolio allocation algorithm will favor low-risk real bonds over stocks more than usual (even though TIPS pay on headline CPI, and not the core CPI I am using in the table).

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Higher Wages: Good for You, Not Good for Stocks

November 27, 2017 2 comments

The documentation of the endless march of asset markets higher has become passé; the illustration of the markets’ overvaluation redundant and tiresome. After years in which these same arguments have been made, without any discernable correction, the sober voices of warning have been discredited and discounted. The defenders of higher valuations have grown more numerous, more vocal, and more bulletproof.

I recently commented in a forum on cryptocurrencies…something to the effect that while I see blockchain as being a useful technology – although one which, like all technologies, will be superseded someday – I don’t expect that cryptocurrency in any of its current forms will survive because they don’t offer anything particularly useful compared to traditional money, and moreover have a considerable trust hurdle to overcome due to the numerous errors, scandals, and betrayals that have plagued the industry periodically since MtGox. Whatever you say about ‘traditional’ money, no one worries that it will vanish from your bank account tomorrow due to some accident. I don’t see anything particularly controversial about that statement, although reasonable people can disagree with my conclusion that cryptocurrency will never gain widespread acceptance. However, the reaction was aggressive and unabashed bashing of my right to have an opinion. I hadn’t even uttered an opinion about whether the valuation of bitcoin is a bubble (it obviously is – certainly there’s no sign of the stability you’d want in a currency!), and yet I almost felt the need to run for my life. The bitcoin folks make the gold nuts look like Caine in the TV show “Kung Fu”: the epitome of calm reasonableness.

But, again, chronicling the various instances of bubble-like behavior has also become passé. It will all make sense after it’s over, when the crowd recovers its senses “slowly, and one by one” as Mackay had it about 170 years ago.

Today though I want to address a quantitative error that I hope is hard to argue with. It has become de rigeur throughout this…let’s call it the recent stages of an extended bull market…to list all of the reasons that a continued rally makes sense. I always find this fascinating because such enumeration is almost never conducted with reference to whether these things are already “in the price.” On the weekend money shows I heard several pundits opine that the stock market’s rally was likely to continue because “growth is pretty good, at around 3%; interest rates are relatively low; inflation is relatively low; government has become more business-friendly, and wages seem to be going up again.” As I say, it seems to me that most of this should already be in the market price of most securities, and not a cause for further advance. But one of those items is in fact a bearish item.

Make no mistake, wages going up is a great thing. And it’s nice to hear that people are finally starting to note that wages are rising (I pointed this out in April of 2016, citing the Atlanta Fed’s macroblog article on the topic, here. But not everyone reads this column, sadly). The chart below shows the Atlanta Fed’s Wage Growth Tracker, against Median CPI.

So wages are going up for continuously-employed persons, and this is good news for workers. But it’s bad news for corporate earnings. Corporate margins have been very high for a very long time (see chart, source Bloomberg), and that’s partly because a large pool of available labor was keeping a lid on wages while weak global demand was helping to hold down commodity input prices.

Higher wages are, in fact, a negative for stocks.

The argument for why higher wages seem like they ought to be a positive for stocks goes through consumption. If workers are earning more money, the thinking goes, then they can buy more stuff from companies. But this obviously doesn’t make a lot of sense – unless the worker is spending more than 100% of his additional wages in consumption (which can happen if a worker changes his/her savings pattern). If a worker earns $10, and spends $9 buying goods, then business revenues rise by less than wage expenditures and business profits fall, all else being equal.

This shows up in the Kalecki profits equation, which says that corporate profits equal Investment minus Household Savings minus Government Savings minus Foreign Savings plus Dividends. (Look up Kalecki Profit Equation on Wikipedia for a further explanation.) Rearranging, Kalecki profits equal Investment, minus Government Savings (that is, surplus…so currently the deficit contributes to profits), minus Foreign Savings, plus (Dividends minus Household Savings). So, if workers save some of their new, higher earnings then corporate profits decline. The chart below shows how the Kalecki decomposition of profits tends to track pretty well with reported business profits (source: Bloomberg).

Now, profit margins have been high over the last year despite the rise in wages (not because of it) because the personal savings rate has been declining (see chart, source Bloomberg).

If wages continue to grow, and workers start to save more of their earnings (paying off credit cards perhaps?), then it means that labor is taking a larger portion of the pie compared to the historically-large portion that has been going to capital. This is good for workers. It is not good for stocks.

Categories: Stock Market, Theory, Wages

Velocity and Rates and the Vicious Cycle Possibility

November 1, 2017 3 comments

There was a potentially important development in inflation recently, but one that was generally overlooked.

Perhaps it was mostly overlooked because it is way too early to say that a trend is developed that could cause an adverse inflation occurrence. But I think that the main reason it was overlooked is that monetary velocity is not very well understood. In particular, most people seem to think that money velocity – definitionally, the number of times a unit of money is transacted in a year, on average – is somehow tied to nervousness about the economy. So, when money velocity fell in the global financial crisis, many observers attributed that to savers stuffing dollar bills in the proverbial mattress.

There may be some role for investor/consumer uncertainty in the modeling of velocity, but at best it is a secondary or tertiary cause. The main cause of changes in velocity is simple: when the cost of holding money is high, we work hard to hold less of it, and when the cost of holding money is low, we don’t mind holding more of it. Friedman first noticed this, so it isn’t a new discovery. In monetarist speak, velocity is the “inverse of the demand for real cash balances,” and the demand for such balances depends of course on the relative cost of cash balances relative to other investments.

The chart below shows the power of this relationship. I’m using the 5-year constant maturity treasury rate, but there are obviously other investments that would get thrown into this relationship. But it’s easy to envision the effect here. When interest rates are at 6%, then money does not sit idle for very long or accumulate without limit in your bank account – you will invest those monies in, say, a 5-year CD or Treasury Note, rather than earn basically nothing in a checking account. But when interest rates are at 1%, the urge to do so isn’t as much.

What you can see in the chart is that interest rate moves tend to precede movements in money velocity, which is what we would expect from a causal relationship such as this. So the reason that money velocity plunged in the GFC wasn’t because people were scared; it was mostly because interest rates fell, taking away the incentive to invest longer-term.

Changes in money velocity, of course, tend to cause changes in inflation. MVºPQ, after all, and Q tends to be mostly exogenously determined by aggregate fiscal variables, industrial policy (what’s that?) and the like. Changes in M and changes in V tend to be reflected mainly in P over time.

Also, interest rates are affected by inflation, or more properly by the expectations of inflation. And expectations about inflation tend to follow inflation.

So, the history of the 1980s’ declining inflation can be read like this, without too much of a stretch: declining money growth under Volcker caused declining inflation initially. The decline in inflation tended to cause interest rates to decline. Declining interest rates tended to cause declines in money velocity. Declining money velocity tended to cause declines in inflation…and we were in a virtuous cycle that extended, and extended, and extended, until we were close to zero in interest rates and inflation, and money velocity was as low as it has ever been.

Now, you can see from this chart that interest rates bottomed in 2013, but really have not appreciably risen above the lows, and so money velocity hasn’t reversed its slide although since the beginning of last year the trajectory has been slowing (I suspect some nonlinearity/stickiness of this relationship near zero). But the GDP report from last Friday, combined with recent money growth and increase in the price deflator, implied that money velocity actually rose slightly.

It has nudged higher before, but not by very much. And this is why I am reluctant to make a huge deal about this being the start of something, except that this is the first time since 2008 that there has been a reasonable expectation that interest rates might continue to rise because the central bank wishes it so.

And so I don’t think it’s wrong to consider the “what if” of the next cycle. Normalization of interest rates implies normalization of velocity, and there’s just no way to get appreciably higher velocity without higher inflation. Higher inflation would probably produce higher interest rates, because however much your expectations about inflation are “anchored” they are likely to become unanchored if inflation of 3%-4% starts to print. Higher interest rates could lead to higher velocity, and we have a cocktail for the opposite of the 1980s virtuous interest rate cycle.

This speculation isn’t destiny, and a lot depends on whether interest rates start to move higher and by how much. But there is already starting to be some concern about inflation and the FRBNY’s “Underlying Inflation Gauge” has recently gone to new post-crisis highs (see Chart above, source Bloomberg), so I don’t think it is unreasonable to consider and prepare. Because the best case for the next inflation uptick is that it rises a bit and falls back. But there are elements in place that support a much worse case, and that is a feedback loop through interest rates and velocity. The chances of that outcome are considerably higher than zero.


Note: these articles are now first released on my private Twitter feed, which you can subscribe to for only $10 per month here. Subscribers also get my real-time tweet analysis on the monthly CPI report, which are not on my public feed, and I am working on adding a free chart package to the mix as well.

The Limits to Trusting the Robots

October 20, 2017 1 comment

After another day on Thursday of stocks starting to look mildly tired – but only mildly – only to rally back to a new closing high, it hardly seems unusual any more. I have to keep pinching myself, reminding myself that this is historically abnormal. Actually, very abnormal. If the S&P 500 Total Return Index ends this month with a gain, it will be the second time in history that has happened. The other time was in 1936, as stocks bounced back from a deep bear market (at the end of those 12 months, in March 1936, stocks were still 54% off the 1929 highs). A rally this month would also mean that stocks have gained for 19 out of the last 20 months, the longest streak with just one miss since…1936 again.

But we aren’t rebounding from ‘oversold.’ This seems to be a different situation.

What is going on is confounding the wise and the foolish alike. Every dip is bought; the measures of market constancy (noted above, for example) are at all-time highs and the measures of market volatility such as the VIX are at all-time lows. It is de rigeur at this point to sneer “what could go wrong?” and you may assume I have indeed so sneered. But I also am curious about whether there is some kind of feedback loop at work that could cause this to go on far longer than it “should.”

To be sure, it shouldn’t. By many measures, equities are at or near all time measures of richness. The ones that are not at all-time highs are still in the top decile. Buying equities (or for that matter, bonds) at these levels ought to be a recipe for a capitalistic disaster. And yet, value guys are getting carried out left and right.

Does the elimination (with extreme prejudice) of value traders have any implications?

There has been lots of research about market composition: models, for example, that examine how “noise” and “signal” traders come together to create markets that exhibit the sorts of characteristics that normal markets do. Studies of what proportion of “speculators” you need, compared to “hedgers,” to make markets efficient or to cause them to have bubbles form.

So my question is, what if the combination of “buy the dip” micro-time-frame value guys, combine with the “risk parity” guys, represents a stable system?

Suppose equity volatility starts to rise. Then the risk-parity guys will start to sell equities, which will push prices lower and tend to push volatility higher. But then the short-term value guys step in to ‘buy the dip.’ To be clear, these are not traditional value investors, but rather more like the “speculators” in the hedger/speculator formulation of the market. These are people who buy something that has gone down, because it has gone down and is therefore cheaper, as opposed to the people who sell something that has gone down, because the fact that it has gone down means that it is more likely to go down further. In options-land, the folks buying the dip are pursuing a short-volatility strategy while the folks selling are pursuing a long-volatility strategy.[1]

Once the market has been stabilized by the buy-the-dip folks, who might be for example hedging a long options position (say, volatility arbitrage guys who are long actual options and short the VIX), then volatility starts to decline again, bringing the risk-parity guys back into equities and, along with the indexed long-only money that is seeking beta regardless of price, pushing the market higher. Whereupon the buy-the-dip guys get out with their scalped profit but leaving prices higher, and volatility lower, than it started (this last condition is necessary because otherwise it ends up being a zero-sum game. If prices keep going higher and implied volatility lower, it need not be zero-sum, which means both sides are being rewarded, which means that we would see more and more risk-parity guys – which we do – and more and more delta-hedging-buy-the-dip guys – which we do).

Obviously this sort of thing happens. My question though is, what if these different activities tend to offset in a convergent rather than divergent way, so that the system is stable? If this is what is happening then traditional value has no meaning, and equities can ascend arbitrary heights of valuation and implied volatility can decline arbitrarily low.

Options traders see this sort of stability in micro all the time. If there is lots of open interest in options around, say, the 110 strike on the bond contract, and the Street (or, more generally, the sophisticated and leveraged delta-hedgers) is long those options, then what tends to happen is that if the bond contract happens to be near 110 when expiry nears it will often oscillate around that strike in ever-declining swings. If I am long 110 straddles and the market rallies to 110-04, suddenly because of my gamma position I find myself long the market since my calls are in the money and my puts are not. If I sell my delta at 110-04, then I have locked in a small profit that helps to offset the large time decay that is going to make my options lose all of their remaining time value in a short while.[2] So, if the active traders are all long options at this strike, what happens is that when the bond goes to 110-04, all of the active folks sell to try and scalp their time decay, pushing the bond back down. When it goes to 99-28, they all buy. Then, the next time up, the bond gets to 110-03 and the folks who missed delta-hedging the last time say “okay, this time I will get this hedge off” and sell, so the oscillation is smaller. Sometimes it gets really hard to have any chance of covering time decay at all because this process results in the market stabilizing right at 110-00 right up until expiration. And that stabilization happens because of the traders hedging long-volatility positions in a low-volatility environment.

But for the options trader, that process has an end – options expiration. In the market process I am describing where risk-parity flows are being offset by buy-the-dip traders…is there an end, or can that process continue ad infinitum or at least, “much longer than you think it can?”

Spoiler alert: it already has continued much longer than I thought it could.

There is, however, a limit. These oscillations have to reach some de minimus level or it isn’t worth it to the buy-the-dip guys to buy the dip, and it isn’t worth reallocation of risk-parity strategies. This level is much lower now than it has been in the past, thanks to the spread of automated trading systems (i.e., robots) that make the delta-hedging process (or its analog in this system) so efficient that it requires less actual volatility to be profitable. But there is a limit. And the limit is reach two ways, in fact, because the minimum oscillation needed is a function of the capital to be deployed in the hedging process. I can hedge a 1-lot with a 2 penny oscillation in a stock. But I can’t get in and out of a million shares that way. So, as the amount of capital deployed in these strategies goes up, it actually raises the potential floor for volatility, below which these strategies aren’t profitable (at least in the long run). However, there could still be an equilibrium in which the capital deployed in these strategies, the volatility, and the market drift are all balanced, and that equilibrium could well be at still-lower volatility and still-higher market prices and still-larger allocations to risk-parity etc.

It seems like a good question to ask, the day after the 30th anniversary of the first time that the robots went crazy, “how does this stable system break down?” And, as a related question, “is the system self-stabilizing when perturbed, or does it de-stabilize?”

Some systems are self-stabilizing with small perturbations and destabilizing with larger perturbations. Think of a marble rolling around in a bowl. A small push up the side of the bowl will result in the marble eventually returning to the bottom of the bowl; a large push will result in the marble leaving the bowl entirely. I think we are in that sort of system. We have seen mild events, such as the shock of Brexit or Trump’s electoral victory, result in mild volatility that eventually dampened and left stocks at a higher level. I wonder if, as more money is employed in risk parity, the same size perturbation might eventually be divergent – as volatility rises, risk parity sells, and if the amount of dip-buyers is too small relative to the risk parity sellers, then the dip-buyers don’t stabilize the rout and eventually become sellers themselves.

If that’s the secret…if it’s the ratio of risk-parity money to dip-buyer money that matters in order to keep this a stable, symbiotic relationship, then there are two ways that the system can lose stability.

The first is that risk parity strategies can attract too much money. Risk parity is a liquidity-consumer, as they tend to be sellers when volatility is rising and buyers when volatility is falling. Moreover, they tend to be sellers of all assets when correlations are rising, and buyers of all assets when correlations are falling. And while total risk-parity fund flows are hard to track, there is little doubt that money is flowing to these strategies. For example one such fund, the Columbia Adaptive Risk Allocation Fund (CRAZX), has seen fairly dramatic increases in total assets over the last year or so (see chart, source Bloomberg. Hat tip to Peter Tchir whose Forbes article in May suggested this metric).

The second way that ratio can lose stability is that the money allocated to buy-the-dip strategies declines. This is even harder to track, but I suspect it is related to two things: the frequency and size of reasonable dips to buy, and the value of buying the dip (if you buy the dip, and the market keeps going down, then you probably don’t think you did well). Here are two charts, with the data sourced from Bloomberg (Enduring Intellectual Properties calculations).

The former chart suggests that dip-buyers may be getting bored as there are fewer dips to buy (90% of the time over the last 180 days, the S&P 500 has been within 2% of its high). The latter chart suggests that the return to buying the dip has been low recently, but in general has been reasonably stable. This is essentially a measure of realized volatility. In principle, though, forward expectations about the range should be highly correlated to current implied volatility so the low level of the VIX implies that buying the dip shouldn’t give a large return to the upside. So in this last chart, I am trying to combine these two items into one index to give an overall view of the attractiveness of dip buying. This is the VIX, minus the 10th percentile of dips to buy.

I don’t know if this number by itself means a whole lot, but it does seem generally correct: the combination of fewer dips and lower volatility means dip-buying should become less popular.

But if dip-buying becomes less popular, and risk-parity implies more selling on dips…well, that is how you can get instability.

[1] This is not inconsistent with how risk parity is described in this excellent paper by Artemis Capital Management (h/t JN) – risk parity itself is a short volatility strategy; to hedge the delta of a risk parity strategy you sell when markets are going down and buy when markets are going up, replicating a synthetic long volatility position to offset.

[2] If this is making your eyes glaze over, skip ahead. It’s hard to explain this dynamic briefly unless I assume some level of options knowledge in the reader. But I know many of my readers don’t have that requisite knowledge. For those who do, I think this may resonate however so I’m plunging forward.

The Mystery of Why There’s A Mystery

October 10, 2017 Leave a comment

We have an interesting week ahead, at least for an inflation guy.

Of course, the CPI statistics (released this Friday) are always interesting but with all of the chatter about the “mystery” of inflation, it should draw more than the usual level of attention. That’s especially true since the mystery will cease to be a mystery fairly soon as even flawed indicators of inflation’s central tendency, such as the core CPI, turn back higher. This is not particularly good news for many pundits, who have declared the mystery to be solved with some explanation that implies inflation will stay low.

  • “Amazon effect”
  • Globalization
  • “competition”
  • Etc

The first of these I have addressed previously back in June (“The Internet Has Not Killed, and Will Not Kill, Inflation”). The second is a real effect, but it is a real effect whose effect peaked in the early 1990s and has been waning since then. I wrote something in our quarterly in Q4 last year, which is partly summarized here.

The “competition” objection is a weird one. It seems to posit that competition was pretty lame until recently, which is pretty strange. One argument along these lines is in this article by Steve Wunsch, who considers the increase in airline fees “stark evidence of a deflationary spiral in those ticket prices caused by antitrust-induced competition.” This is odd, since airlines were deregulated in 1978 and have in recent years become less competitive if anything with the mergers of Delta/Northwest in 2009, United/Continental in 2010, Southwest/AirTran in 2011, and US Airways/American Airlines in 2013. A flaccid antitrust response from the Justice Department has allowed quasi-monopolies to develop in some travel hubs, which has tended to push fares higher rather than lower. The chart below shows the relationship between Jet Fuel prices and the CPI for airfares (both seasonally adjusted) for the 20 years ended in 2014, along with the most-recent point from last month.

The highly-explanatory R-squared of 0.81 suggests that there is not much wiggle room in airline pricing. Airfares are, as you would expect under a competitive industry, roughly cost-plus with the main source of variance being jet fuel prices. This is true even though we would expect that spread to vary over time. As Mr. Wunsch would argue, the highly competitive nature of the industry is holding down the non-commodity price pressures in airfares.

The only problem is that if you extend this graph to include the last three years, the R-squared drops about 10 points:

In case it isn’t clear from that chart, the last three years have seen airfares increasingly above what we would expect from the level of jet fuel prices. The next chart makes that clear I hope by plotting the residual (and 12-month moving average to smooth out seasonal issues such as one that evidently happened last month) between the actual CPI-airfare and the level that would be predicted from the 1994-2014 relationship. As you can see, prices have been higher, and increasingly so, than we would have thought, until this last month or two – and I wouldn’t grab a lot of comfort from that yet.

Not only is this not “stark evidence of a deflationary spiral in those ticket prices caused by antitrust-induced competition,” it seems to be stark evidence of inflation in ticket prices caused by a reduction in competition thanks to airline mergers.

In reading these many articles, it always is somewhat striking to me: everybody thinks their answer is “the” answer to the mystery. But most of these authors really don’t sufficiently understand how inflation works, and what the data is showing. This is apparent to those who do understand these nuances, as an author might discuss (as the one mentioned above did) an “aberration” in cell phone inflation as if the experts are stupid for expecting inflation when cell phone services only go down. The author clearly misunderstands what the “aberration” referred to even is; in this case the aberration was an enormous one-month collapse in prices that had never been seen and has not been repeated since. (For those who are curious about the aberration, and why it occurred, and why it is likely a methodology issue rather than sign of spiraling deflation in wireless services you can see my discussion of it here.)

The mystery is simple – the Fed’s models don’t work, and don’t take into account the fact that lower interest rates cause lower money velocity. They rely on a Phillips Curve effect that they think is broken because they don’t understand that the Phillips Curve relates wages and unemployment, not consumer prices and unemployment. They focus on a flawed measure like PCE rather than on something like Median CPI which, coincidentally, is a lot higher and suggests more price pressures. The mystery isn’t why inflation isn’t rising yet – the mystery is why they think there’s a mystery.

Some Further (Minor) Thoughts on the Phillips Curve

September 6, 2017 3 comments

Before I begin, let me say that if you haven’t read yesterday’s article, please do because it represents the important argument: the Phillips Curve doesn’t need rehabilitating, because it is working fine. In fact, I would argue that the Phillips Curve – relating wages to unemployment – is a remarkably accurate economic model prediction. The key chart from that article I reproduce here, but the article (which is brief) is worth reading.

Following my publication of that article, I had a few more thoughts that are worth discussing on this topic.

The first is historical. It’s incredibly frustrating to read article after article incorrectly stating what the Phillips Curve is supposed to relate. Of course one writer learns from another writer until what is incorrect becomes ‘common knowledge.’ I was fortunate in that, 30 years ago, I had excellent Economics professors at Trinity University in San Antonio, and I was reflecting on that fact when I said to myself “I wonder if Samuelson had it right?”

So I dug out my copy of Economics by Samuelson and Nordhaus (the best-selling textbook of all time, I believe, and the de rigeur Intro to Economics textbook for generations of economists). My copy is the 12th Edition, so perhaps they have corrected this since then…but on page 247, there it is – the Phillips Curve illustrated as a “tradeoff between inflation and unemployment.” Maybe that is where this error really propagated – with a Nobel Prize-winning economist making an error in his incredibly widely-read text! Interestingly, the authors don’t reference the original Phillips work, but refer to “writers in the 1960s” who made that connection, so to be fair to Samuelson and Nordhaus they were possibly already repeating an error that had been made even earlier.

My second point is artistic. In yesterday’s article, I said “The Phillips Curve…simply says that when labor is in short supply, its price goes up. In other words: labor, like everything else, is traded in the context of supply and demand,…” But students of economics will note that the Phillips Curve seems to obfuscate this relationship, because it is sloping the wrong way for a supply curve – which should slope up and to the right rather than down and to the right. This can be remedied by expressing the x-axis of the Phillips Curve differently – making it the quantity of labor demanded rather than the quantity of labor not demanded…which is what the unemployment rate is. So the plot of wage inflation as a function of the Employment Rate (as opposed to the Unemployment Rate) has the expected shape of a supply curve. More labor is supplied when the prices rise.

Again, this is nuance and not a really important point unless you want your economics to be pretty.

My third point, though, is important. One member of the bow-tied fraternity of Ph.D. economists told me through a friend that “the Phillips Curve has evolved to the relationship between Unemployment and general prices, not simply wages.” I am skeptical of any “evolution” that causes the offspring to be worse-adapted to the environment, but moreover I would argue that whoever led this “evolution” (and as I said above, it looks like it happened in the 1960s) didn’t really understand the way the economy (and in particular, business) works.

There is every reason to think that wages should be tied to available labor supply because one is the price of the other. That’s Microeconomics 101. But if unemployment is going to be a good indicator of generalized price inflation too, then it means that prices in the economy are essentially set as the price of the labor input plus a spread for profit. That is not at all how prices are set. Picture the businessperson deciding how to set prices. According to the “evolved Phillips Curve” understanding, this business owner looks at the wages he/she is paying and then sets the price of the product. But that’s crazy. A business owner considers labor as one input, as well as all of the other inputs, improvements in productivity in producing this good or service in question, competitive pressures, and the general state of the national and local economy. It would be incredible if all of these factors canceled out except for wage inflation, wouldn’t it? So in short, while I would expect that unemployment might have some explanatory power for inflation, I wouldn’t expect that explanatory power to be very strong. And, in fact, it isn’t. (But this isn’t new – it never has had any power.)

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The Phillips Curve is Working Just Fine, Thanks

September 5, 2017 4 comments

I must say that it is discouraging how often I have to write about the Phillips Curve.

The Phillips Curve is a very simple idea and a very powerful model. It simply says that when labor is in short supply, its price goes up. In other words: labor, like everything else, is traded in the context of supply and demand, and the price is sensitive to the balance of supply and demand.

Somewhere along the line, people decided that what Phillips really meant was that low unemployment caused consumer price inflation. It turns out that doesn’t really work (see chart, source BLS, showing unemployment versus CPI since 1997).

Accordingly, since the Phillips Curve is “broken,” lots of work has been done to resurrect it by “augmenting” it with expectations. This also does not work, although if you add enough variables to any model you will eventually get a decent fit.

And so here we are, with Federal Reserve officials and blue-chip economists alike bemoaning that the Fed has “only one model, and it’s broken,” when it never really worked in the first place. (Incidentally, the monetary model that relates money and velocity (via interest rates) to the price level works quite well, but apparently they haven’t gotten around to rediscovering monetarism at the Fed).

But the problem is not in our stars, but in ourselves. There is nothing wrong with the Phillips Curve. The title of William Phillips’ original paper is “The Relation between Unemployment and the Rate of Change of Money Wage Rates in the United Kingdom, 1861-1957.” Note that there is nothing in that title about consumer inflation! Here is the actual Phillips Curve in the US over the last 20 years, relating the Unemployment Rate to wages 9 months later.

The trendline here is a simple power function and actually resembles the shape of Phillips’ original curve. The R-squared of 0.91, I think, sufficiently rehabilitates Phillips. Don’t you?

I haven’t done anything tricky here. The Atlanta Fed Wage Growth Tracker is a relevant measure of wages which tracks the change in the wages of continuously-employed persons, and so avoids composition effects such as the fact that when unemployment drops, lower-quality workers (who earn lower wages) are the last to be hired. The 9-month lag is a reasonable response time for employers to respond to labor conditions when they are changing rapidly such as in 2009…but even with no lag, the R-squared is still 0.73 or so, despite the rapid changes in the Unemployment Rate in 2008-09.

So let Phillips rest in peace with his considerable contribution in place. Blame the lack of inflation on someone else.

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