Category: Equitablog

Daniel Kuehn: Yes, Acemoglu and Robinson’s review of Piketty is very strange: “I just happened to get to one of the parts in Piketty that Acemoglu and Robinson quote…

…to show that Piketty doesn’t think institutions matter (from page 365):

The fundamental inequality r > g can explain the very high level of capital inequality observed in the nineteenth century, and thus in a sense the failure of the French revolution…. The formal nature of the regime was of little moment compared with the inequality r > g.

So what is in that ellipses? [Piketty] explains that the revolution didn’t change the course of inequality (relative to monarchical Britain) because the new institutions that were established were much closer to Britain than popular perception in France at the time suggested! It was NOT a big change in institutions, which was why the French revolution did not shift the parameters…. Immediately after this he goes on to discuss changes in institutions in the 20th century that WERE substantial enough to impact inequality…. In other words, the real point of this section is that institutions matter a lot…. And not only did A&R get that wrong–they deliberately removed the portion of the quote where he made the point…. All of the explanations for the empirical changes in the distribution over time are either (1) institutions, or (2) shocks…. Apparently it’s not just Acemoglu and Robinson that missed this memo…. Piketty without institutions in the capital share of income section could probably survive. Piketty without institutions in the inequality section of the book simply wouldn’t exist any more…. This is like saying Milton Friedman wasn’t all that concerned with money!

**Matthew Klein:** This time is different, long-term unemployment edition: “Jae Song and Till von Wachter…

>…the impact of the most recent recession on long-term unemployment was not actually that unusual given the number of jobs lost at the outset. The paper also presents some encouraging evidence that many of those who appear to have given up hope of finding a job could rejoin the labour force if the economy keeps expanding, as well as sobering demonstrations of the permanent costs of being laid off…. Long-term non-employment in the immediate aftermath of the Great Recession was not much worse than it was in the early 1980s for most cohorts, with the notable exception of young men…. As the paper puts it: ‘There is no evidence that the rate of exit from long term nonemployment has slowed during the Great Recession compared to the patterns in all episodes since the 1990s.’ But this is not all good news… ‘five to ten years after the Great Recession the employment population ratio would be predicted to be 1 to 2 percentage points lower than it was before the recession’…

Jonathan Chait: Dreamers Have Destroyed GOP Immigration Strategy: “After the 2012 election, Marco Rubio tried to craft himself…

…as the leader of a pro-immigration-reform Republican Party. That effort has capsized, pulling Rubio’s standing with conservatives down along with it. Now Rubio is refashioning himself as the leader of a restrictionist Republican Party…. The newest iteration of Rubio is the opposite of the figure he and party leaders envisioned last year. The transformation ought to terrify them…. Of course, the 2016 campaign has hardly begun…. The trouble for Republicans is that the political theater created by the Dreamers is not going to stop. They can try their best to control officially sanctioned media debates, but the Dreamers are staging debates without permission, endlessly highlighting the cruelty of the Republican stance. It is a strategy for which the Republicans so far have no answer. The symbolic denouement of Rubio’s immigration debacle may well be an angry old man brandishing his cane at young Dreamers.

Yet More Things You Absolutely Must Read…

• Claire Cain Miller pulls out interesting quotes from the Pew Research Center’s Internet & American Life Project: AI, Robotics, and the Future of Jobs
• The full version of John Maynard Keynes’s The End of Laissez-Faire

Nick Rowe begs for North Atlantic central banks to do what he (and I) regard as their proper job, and writes:

Nick Rowe: Money, Prices, and Coordination Failures “The more interesting cases are…

…where a non-monetary coordination failure has spillover effects, and causes a monetary coordination failure. A worsening of asymmetric information problems in financial markets, which is a coordination problem in its own right, also causes an increased demand for money and a monetary coordination problem. Should we say that the problem in financial markets is the “root cause” of the recession, and one that should be addressed directly, if possible, by something other than monetary policy? No. Monetary policy should take the world as it is, warts and all, and do what it can do. And what it can do is eliminate that excess demand for money, even if it cannot eliminate that original problem that initially caused the excess demand for money. It does not matter, for the monetary authority, whether that increased demand for money was caused by some natural event like the weather, which nobody can change, or whether it was caused by some other problem, which the fiscal authority can and should fix.

Governance is itself a coordination problem…. An autonomous central bank targets inflation, or NGDP, just like Hayek’s user of tin targets his own profits or utility. And just as Hayek’s user of tin does not need to know why the price of tin has fallen, so the central bank should not need to know why inflation, or NGDP, has fallen below target. The whole point of having a good monetary policy target, just like the point of having a market system, is that the central bank can do what’s right without knowing everything about the economy, and do what’s right whether or not the rest of the government does what’s right in their jobs…

Not that I believe that what I am about to say has escaped him, but I think I should remind Nick Rowe that I do not believe his arguments have been found convincing by monetary policymakers. Nowhere in the North Atlantic is there a central bank committed to supplying money today and in the future on a path so that planned economy-wide spending equals projected economy-wide income at the NAIRU level of employment. Nobody at the Jackson Lake Lodge in Grand Teton National Park last weekend set out his point of view (although my (imperfect) visualization of the Cosmic All postdicts that Christina Romer came close).

Why not? My visualization of the Cosmic All tells me that:

1. Some denied that there was, today, any large shortfall of today’s level of employment from today’s NAIRU level.

2. Some claimed that there would be “immaculate deanchoring”: the measures necessary to convince workers, managers, savers, and investors of a higher nominal GDP path would–even though employment today is well short of the NAIRU level–immaculately de-anchor inflation expectations so that 100% of the rise would show up as higher inflation and 0% as higher real production and employment.

3. Some claimed that supplying enough money to satisfy the demand for money at a NAIRU-consistent level of employment would generate an unwise increase in the risks of financial instability.

The first of these simply flies in the face of everything we know. Consider what has happened to employment-to-population ratios for prime-aged males and females since 1980:

To make sense of this first would require that factors reducing the ability of employers to make a good match with 25-54 year-old males have accelerated from reducing the employment share of the 25-54 male population by 1%-point/decade in the 1980s and 1990s to a rate of 2%-points/decade between 2000 and 2007 and then a rate of 7%-points/decade between 2007 and 2014–without any similar acceleration in disemployment effects among 25-54 year-old women comparing 2000-2007 to 2007-2014. That makes no sense to me whatsoever. Moreover, the fact that the employment rate among 25-54 year-old females today is no higher than at the global recession trough in 2009 is, I think, a very powerful piece of evidence that there is still a lot of slack in the labor market: prime-aged females should, given the collapse in male employment, have a stronger demand for jobs today than they did back in 2007.

Thus let me give three cheers for Binyamin Applebaum’s assessment of David and Haltiwanger:

Binyamin Applebaum: On the Decline in Labor Force Participation: “Davis and Haltiwanger attribute [the decline in participation]… to… aging… decline in the creation of new companies… [rising] cost of training… partly because the share of all workers who require government licenses has grown… legal changes that have made it more difficult to fire employees… insurance as a reason that employees may stay put. In [their] view… the recession just made a bad situation worse…. But… [how] to reconcile the assertion that these trends were the dominant factors with the reality that the employment rate rose in the years before the recession[?]… [This,] like others of its genre… requires belief in a big coincidence… that the economy crashed at the moment that it was already beginning a [not so] gradual descent.

I think that hits the nail on the head.

To make sense of this second would require that the best analogue for the U.S. today be the only even semi-large economy that has ever been even mentioned as a possible candidate for “immaculate deanchoring”: France at the election of Mitterand. And the claim that even France then was such an example is strongly contested. I can see “immaculate deanchoring” being a thing in small very open economies, but not in large ones–especially not in large ones that issue reserve currencies.

And I continue to fail to understand this third point–largely because I have not yet found anybody who will spell it out to me in any depth or at any length. How, exactly, is taking risk off of the private sector’s balance sheet via quantitative easing supposed to increase risk? Every time I try to think about this issue, I find myself led back to this argument I made quite a while ago: In brief, while reductions in risk premia that spring from irrational exuberance or other increases in the supply of risk-bearing capacity by private agents who have no clue what they are doing can drive bubbles and provoke crises by increasing the amount of risk in financial markets, it is very hard to see how reductions in risk premia that spring from decreases in the demand for private-sector risk-bearing capacity that are driven by asset purchases by a solvent government can do the same, for such reduce the amount of risk in financial markets by transferring it onto taxpayers:

What Are the Risks of Quantitative Easing, Really?

In the financial market there is a demand for risk-bearing capacity by firms and others who want to borrow but who cannot guarantee that they will be able to repay. The higher is the price of risk–the greater the risk premium interest rate spread over short-term Treasuries they must pay–the less they will borrow.

In the financial market there is also a supply of risk-bearing capacity by savers and financial intermediaries who want to lend, and are willing to accept and bear some risk in return from getting more than the short-term Treasury rate. The higher is the price of risk—-the greater the risk premium interest rate spread over short-term Treasuries they must pay–the more they will be willing to lend.

When the Federal Reserve undertakes quantitative easing, it enters the market and takes some risk off the table, buying up some of the risky assets issued by the U.S. government and its tame mortgage GSEs and selling safe assets in exchange. The demand curve for risk-bearing capacity seen by the private market thus shifts inward, to the left: a bunch of risky Treasuries and GSEs are no longer out there, as the government is no longer in the business of soaking-up as much of the private-sector’s risk-bearing capacity:

And this leftward shift in the net demand to the rest of the market for risk-bearing capacity causes the price of risk to fall, and the quantity of risk-bearing capacity supplied to fall as well. Yes, financial intermediaries that had held Treasuries and thus carried duration risk take some of the cash they received by selling their risky long-term Treasuries to the Fed and go out and buy other risky stuff. But the net effect of quantitative easing is to leave investors and financial intermediaries holding less risky portfolios because they are supplying less risk-bearing capacity.

How do we know that they are holding not more but less risky portfolios? We know because we know that supply curves slope up, and if they were holding more risky portfolios in total–supplying more risk-bearing capacity to the market–the price of risk would have not fallen but risen, and interest rate risk spreads would be not lower but higher, wouldn’t they?

So when the intelligent and thoughtful Mark Dow tweets:

I, too, think risks [of QE] overstated, but they’re non-zero. Main ones r credit leverage buildup…

I am at a loss. As long as supply curves slope up, QE does not increase but reduces the leverage of private-sector financial asset holders.

And when the intelligent and thoughtful Mark Dow tweets:

I, too, think risks overstated, but they’re non-zero. Main ones r… outsized int’l capital flows

I am again at a loss. Yes, the Federal Reserve has taken some domestic risky assets off the table. Yes, U.S. financial intermediaries and savers will respond by buying foreign assets to so deploy some of their now-undeployed risk bearing capacity. Yes, they will now bear some exchange-rate risk. But, once again, the fact that QE pushes interest rate spreads down is very powerful evidence that these capital flows are not “outsized”–that the extra exchange-rate risk U.S. financial intermediaries have now taken onto their books is less than the duration risk that QE took off of their books.

At least, that is the case as long as the supply curve for risk-bearing capacity slopes up, like a good supply curve should.

Perhaps those who claim that there are big risks to quantitative easing regroup. Perhaps they claim that financial intermediaries are perverted, and that the lower is the price of risk the greater is the amount of risk-bearing capacity they supply to the market because they lose their jobs if they don’t make at least three cents on every dollar of assets in a normal year in which risk chickens come home to roost.

In that counterfactual world, the supply-and-demand graph would look like this:

And in that counterfactual world, the Federal Reserve’s adoption of quantitative easing policies triggered an enormous expansion of the quantity of risk-bearing capacity demanded by firms and households and a huge private-sector lending boom as firms issued enormous tranches of risky bonds and as firms and households took out risky loans. In that counterfactual world, employment in bond underwriting tripled as $85 billion a month in QE was more-than-offset by an extra $120 billion a month in private-sector bond issues. In that counterfactual world, we saw a rapid recovery of housing construction and a thorough equipment investment boom as far across the U.S. as they eye could see.

That didn’t happen.

So what are the risks of QE?

It really seems to be this:

• Commercial banks traditionally accept deposits, put the deposits in long-term Treasuries, rely on the law of large numbers and on deposit insurance to allow them to always hold their long-term Treasuries to maturity, and so have a riskless and profitable business model.
• When commercial banks cannot do this, they find some way to gamble with government-insured deposits.
• ????
• LOSS!!

But this is not a source of systemic risk: because the deposits they may be gambling with are government insured by the FDIC, no run on the banking system or the shadow banking system occurs when risks come due. It would be embarrassing, yes. And the proper response to thinking that commercial banks are running undue risks with government-insured money is to send in the bank examiners–not to undertake policies that raise unemployment.

So put me with Ryan Avent, who tweets:

[The] risk [is that] of not being considered a [very] serious person by peers [unless you claim to greatly fear the risks of quantitative easing]

It almost seems to me that all those who talk about how quantitative easing raises the risk of future financial crisis by causing financial intermediaries to “reach for yield” that they should not do have either failed to consider that a shock to the demand curve in the market for private risk-bearing capacity different from the shop to the supply curve in the market for private risk-bearing capacity, or failed to consider that they have implicitly committed themselves to believing in a supply of private risk-bearing capacity that slopes the wrong way and led every Treasury bond and mortgage-backed security withdrawn from the market via quantitative easing to have been matched and more than matched by a tsunami of private risky-asset issue that we simply did not see. Instead of enlarging their risky-asset holdings as Treasury and MBS duration risk were taken out of the market and triggering such an issue boom, financial intermediaries supplying risk-bearing capacity shifted some of their asset holdings into reserve deposits and whimpered at the decline in their rate of return.

Does anybody say otherwise?

David Kotok: Deflation Fears: “1. United States 10-Year Treasury Yield…

…2.4% 2. Germany 10-Yield Bund Yield, under 1%. 3. Japanese 10-Year JGB Yield, under 0.5%. 4. France 10-Year Government Bond Yield, 1.3%. 5. Canada 10-Year Government Bond Yield, 2%. 6. United Kingdom 10-Year Government Bond Yield, 2.5%. 7. Mexico 10-Year Government Bond Yield, 3.2%. 8. Italy 10-Year Government Bond Yield, 2.4%…

Because only people who really, really, really want to make bad mistakes do things in the un-debuggable Excel (or Numbers)…

The (corrected) calculations for last weekend’s http://delong.typepad.com/sdj/2014/08/under-what-circumstances-should-you-worry-that-the-stock-market-is-too-high-the-honest-broker-for-the-week-of-august-16.html and http://delong.typepad.com/sdj/2014/08/under-what-circumstances-should-you-worry-that-the-stock-market-is-too-high-the-honest-broker-for-the-week-of-august-16.html:

---

Import data from http://delong.typepad.com/20140824_Shiller_Data.csv…

…define a function (shift) for constructing leads and lags, and perform elementary data manipulations: construct variables DATE (1871:1-2014:7), REAL_PRICE (1871:1-2014:7), REAL_DIVIDENDS (1871:1-2014:7), REAL_EARNINGS (1871:1-2014:7), MA10_EARNINGS (1881:1-2014:7) (the trailing 10-year moving average of real earnings that Campbell and Shiller use as their estimate of cyclically-adjusted permanent earnings), CUMULATIVE_RETURN (the cumulative return on a reinvested index portfolio since 1871 (1871:1-2014:7), LEAD10RETURN (1881:1-2004:7) (the 10-year forward realized annual rate of return), LEAD20RETURN (1881:1-1994:7) (the 20-year forward realized annual rate of return), CAPE (1881:1-2014:7) (the Campbell-Shiller cyclically-adjusted price-earnings ratio), and EXCESS_RETURNS (1871:1-2004:7) (the difference between the 10-year forward annual realized rate of return and the cyclically-adjusted earnings yield):

{r, echo=FALSE} # start the R block
Shiller <- read.csv("~/Dropbox/20140824_Shiller_Data.csv")
View(Shiller) # read in the data.frame and name it "Shiller"

DATE = Shiller$DATE
REAL_PRICE = Shiller$REAL_PRICE
REAL_DIVIDENDS = Shiller$REAL_DIVIDENDS
REAL_EARNINGS=Shiller$REAL_EARNINGS
MA10_EARNINGS = Shiller$MA.10._OF_EARNINGS
CUMULATIVE_RETURN = Shiller$CUMULATIVE_RETURN # pull out the variables

CAPE = REAL_PRICE/MA10_EARNINGS # define the Campbell-Shiller cyclically-adjusted price-earnings ratio--CAPE--as the real price divided by a 10-year trailing moving average of real income

shift<-function(x,shift_by){
stopifnot(is.numeric(shift_by))
stopifnot(is.numeric(x))
if (length(shift_by)>1)
return(sapply(shift_by,shift, x=x))
out<-NULL
abs_shift_by=abs(shift_by)
if (shift_by > 0 )
out<-c(tail(x,-abs_shift_by),rep(NA,abs_shift_by))
else if (shift_by < 0 )
out<-c(rep(NA,abs_shift_by), head(x,-abs_shift_by))
else
out<-x
out
} # construct a function to create leads and lags of variables

LEAD1MORETURN = (shift(REAL_PRICE,1) + REAL_DIVIDENDS/12)/REAL_PRICE-1
LEAD1YRRETURN = (shift(CUMULATIVE_RETURN,12)/CUMULATIVE_RETURN) - 1
LEAD10RETURN = (shift(CUMULATIVE_RETURN,120)/CUMULATIVE_RETURN)^(1/10)-1
LEAD20RETURN = (shift(CUMULATIVE_RETURN,240)/CUMULATIVE_RETURN)^(1/20)-1
EXCESS_RETURN = LEAD10RETURN - 1/CAPE # define the 1-mo return, the 10-year realized forward return, the 20-year realized forward return, and the deviation of realized returns from the CAPE earnings yield

Make sure we have all the data and they look like they should...

{r, echo=FALSE}
plot(DATE,REAL_PRICE, main="Real Stock Index Price", xlab="Date", ylab="Real Stock Index Price", pch=16, cex=0.5)
plot(DATE,REAL_DIVIDENDS, main="Real Stock Index Dividends", xlab="Date", ylab="Real Stock Index Dividends", pch=16, cex=0.5)
plot(DATE,REAL_EARNINGS, main="Real Stock Index Earnings", xlab="Date", ylab="Real Stock Index Earnings", pch=16, cex=0.5)
plot(DATE,MA10_EARNINGS, main="Cyclically-Adjusted Real Earnings", xlab="Date", ylab="10-Yr MA of Trailing Real Earnings ", pch=16, cex=0.5)
plot(DATE,CAPE, xlab="Date", main="Campbell-Shiller Cyclically-Adjusted Price-Earnings", ylab="Campbell-Shiller CAPE", pch=16, cex=0.5)
plot(DATE,LEAD10RETURN, main="Realized Ten-Year Forward Returns", xlab="Date", ylab="10-Yr Forward Realized Annual Rate of Return", pch=16, cex=0.5)
plot(DATE,LEAD20RETURN, main="Realized Twenty-Year Forward Returns", xlab="Date", ylab="20-Year Forward Realized Annual Rate of Return", pch=16, cex=0.5)
plot(DATE,EXCESS_RETURN, main="Realized Ten-Year Excess Return Over CAPE Earnings Yield", xlab="Date", ylab="10-Year Excess Return Over 1/CAPE", pch=16, cex=0.5)
plot(DATE,LEAD1MORETURN, main="One-Month Returns", xlab="Date", ylab="1-Month Realized Forward Return", pch=16, cex=0.5, xlim=c(1870,2015))
plot(DATE,LEAD1YRRETURN, main="One-Year Returns", xlab="Date", ylab="1-Year Realized Forward Return", pch=16, cex=0.5, xlim=c(1870,2015)) # plot everything and look at it

Yes, everything as it should be so far...

Now on to the analysis proper...

Let's start with the simplest possible forward-return regression: regressing the ten-year future realized return in the Campbell-Shiller stock index database on the Campbell-Shiller cyclically-adjusted earnings yield INVERSECAPE--the inverse of the CAPE:

{r, echo=FALSE}
INVERSECAPE = 1/CAPE
return_regression_2.lm = lm(formula = LEAD10RETURN ~ INVERSECAPE)
summary(return_regression_2.lm)

In response to:

Call: lm(formula = LEAD10RETURN ~ INVERSECAPE)

R reports:

Residuals:
Min        1Q    Median        3Q       Max 
-0.106298 -0.030839  0.002955  0.028179  0.103866 
Coefficients:
           Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.007659   0.002878  -2.661  0.00788 
INVERSECAPE  0.995904   0.036513  27.275  < 2e-16

Residual standard error: 0.04284 on 1482 degrees of freedom
(240 observations deleted due to missingness)
Multiple R-squared:  0.3342,
Adjusted R-squared:  0.3338 
F-statistic: 743.9 on 1 and 1482 DF,
p-value: < 2.2e-16

The significance levels that R reports are wrong: its naive regression package assumes that each of the 1482 observed 10-year returns is independent of each of the others. They are not. Each monthly return shows up as a component in 120 10-year returns. The right t-value for the cyclically-adjusted earnings yield INVERSECAPE is not 27.3 but rather something between 4 and 5--still highly, highly significant.

More important, a third of the variance in future 10-year returns is accounted for by knowing the value of INVERSECAPE. More important, the intercept is zero and the coefficient is 1. More important, the ability of the earnings yield to forecast future 10-year returns remains highly, highly significant. More important, you get these not just by knowing what INVERSECAPE is and then performing some linear transformation on it, but by just the INVERSECAPE itself. What this equation tells us is that, since 1881, 0 + 1 x INVERSECAPE is a remarkably good linear forecast of ten-year future returns.

{r, echo=FALSE}
plot(INVERSECAPE,LEAD10RETURN, main="Realized Ten-Year Forward Returns vs. CAPE Earnings Yield", xlab="CAPE Earnings Yield", ylab="10-Year Realized Foreward Returns", pch=16, cex=0.5)
abline(lm(LEAD10RETURN ~ INVERSECAPE))

Note that this particular functional form for understanding how knowing CAPE should shape your forecast of future returns is not important. INVERSECAPE is convenient because it comes in the same units as returns. But regressing future long-run returns on CAPE itself does about as well. Submit:

{r, echo=FALSE}
INVERSECAPE = 1/CAPE
return_regression_2.lm = lm(formula = LEAD10RETURN ~ CAPE)
summary(return_regression_2.lm)

And R spits out:

Call: lm(formula = LEAD10RETURN ~ CAPE)
Residuals:
    Min        1Q    Median        3Q       Max 
-0.116777 -0.029650  0.004347  0.028478  0.093157 
Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.1383475  0.0029889   46.29   <2e-16
CAPE        -0.0045885  0.0001727  -26.57   <2e-16

Residual standard error: 0.04321 on 1482 degrees of freedom
(240 observations deleted due to missingness)
Multiple R-squared:  0.3226,    
Adjusted R-squared:  0.3221 
F-statistic: 705.8 on 1 and 1482 DF,  
p-value: < 2.2e-16

The same 1/3 of variance accounted for. The same parameter at the median of the distribution. This formulation suggests that expected ten-year real returns turn negative at a CAPE value of above 30--that offsetting the 3.3% real earnings yield at that point is an anticipated equal decline in valuation metrics over the next ten years, but the plot reveals that this prediction relies heavily on the linearity. Submitting:

{r, echo=FALSE}
plot(CAPE,LEAD10RETURN, main="Realized Ten-Year Forward Returns vs. CAPE ", xlab="CAPE", ylab="10-Year Realized Foreward Returns", pch=16, cex=0.5)
abline(lm(LEAD10RETURN ~ CAPE))

produces from R:

Basically what we know about expected returns is that on the one occasion when CAPE rose above 30, the dot-com crash of 2000 was in the near future and the housing crash of 2008 came into the ten-year return window. That is not much information on which to base a long-run "sell" decision.

Let's think about not what economists call risk--variation about expected returns--but what people call risk: the chance that your money won't be there in real terms. The lowest realized 10-year returns did indeed come when the CAPE was at its highest and thus the earnings yield INVERSECAPE was at its lowest. But the second-lowest returns happened when CAPE was not high but normal. And the other periods of negative realized returns happened when CAPE was high, but not that low. Plus there is a lot of mass of the distribution with both high CAPE and very healthy positive returns. What's going on?

{r, echo=FALSE}
plot(DATE,LEAD10RETURN, main="Realized Ten-Year Forward Returns", xlab="DATE", ylab="10-Year Realized Foreward Returns", pch=16, cex=0.5)
plot(CAPE,LEAD10RETURN, main="Realized Ten-Year Forward Returns", xlab="DATE", ylab="10-Year Realized Foreward Returns", pch=16, cex=0.5)

There are only four historical periods during which a ten-year investment in the S&P has not at least held its real value: ten years before the post-World War I deflation and the post-WWI depression of the start of the 1920s; (barely) in the Great Depression and the WWII inflation; 10 years before the stagflation of the 1970s and the subsequent Volcker depression; and 10 years before the recent financial unpleasantness for those dates where the ten-year return window includes both the dot-com and the housing-bubble crashes.

There is little more to be squeezed out of this particular data set.

{r, echo=FALSE}
return_regression_3.lm = lm(formula = LEAD10RETURN ~ INVERSECAPE + CAPE + CAPESQ)
summary(return_regression_3.lm)

All the data will say is that once the CAPE earnings yield is known there is absolutely no point in adding either CAPE or CAPE² in the hopes of picking up some predictive ability via curvature, while the computer does have a (weak) preference for placing predictive weight on the yield if it is added to the regression:

Call: lm(formula = LEAD10RETURN ~ INVERSECAPE + CAPE + CAPESQ)
Residuals:
    Min        1Q    Median        3Q       Max 
-0.110743 -0.029043  0.002934  0.028354  0.099453 
Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  2.550e-02  2.612e-02   0.976    0.329    
INVERSECAPE  7.356e-01  1.268e-01   5.801 8.07e-09
CAPE        -2.194e-04  1.559e-03  -0.141    0.888    
CAPESQ      -3.578e-05  2.679e-05  -1.336    0.182

Residual standard error: 0.0421 on 1480 degrees of freedom
(240 observations deleted due to missingness)
Multiple R-squared:  0.358, 
Adjusted R-squared:  0.3567 
F-statistic: 275.1 on 3 and 1480 DF,  
p-value: < 2.2e-16

---

Sources: http://delong.typepad.com/20140824_shiller_stock_data.rmd | http://delong.typepad.com/20140824_Shiller_Data.csv

Greg Sargent: GOP’s Obamacare repeal follies continue: “One of the most amusing subplots of the 2014 elections…

…has been the endless and frequently comic struggles of GOP Senate candidates to articulate their position on Medicaid…. Iowa Senate candidate Joni Ernst… says people on Medicaid should should be allowed to keep it…. And yet she proudly touted her vote against Iowa’s Medicaid expansion and continues to be a gung-ho advocate for repealing Obamacare, which would roll back funding for the expansion…. Ernst’s repeal stance would mean all those people lose their coverage. Does she no longer think that should happen?…. Tom Cotton in Arkansas once again refused to say this week whether his repeal stance means he would roll back his state’s version of the Medicaid expansion…. Terri Lynn Land in Michigan has also refused to clarify her position on this…. Commentators are not telling the full story of how the politics of Obamacare are really playing out…. At this point [candidates] are largely attacking the word ‘Obamacare’ while reassuring swing voters they support its general goals, without saying how they would accomplish those goals…

Joe Weisenthal notes Lorcan Roche Kelly of Agenda Research on an extended ad-lib in Mario Draghi’s Jackson Hole speech. Ad lib emphasized; things in the text not mentioned struck through:

Inflation has been on a downward path from around 2.5% in the summer of 2012 to 0.4% most recently.

I comment on these movements about once a month in the press conference, and I have given several reasons for this downward path in inflation, saying it is because of food and energy price declines; because after mid-2012 it is mostly exchange rate appreciation that has impacted on price movements; more recently we have had the Russia-Ukraine geopolitical risks, which will also exert a negative impact on the euro area economy; and of course we had the relative price adjustment that had to happen in the stressed countries as well as high unemployment. I have said in principle most of these effects should in the end wash out because most of them are temporary in nature–though not all of them.

But I also said if this period of low inflation were to last for a prolonged period of time, the risk to price stability would increase. Inflation expectations exhibited significant declines at all horizons. The 5year/5year swap rate declined by 15 basis points to just below 2%–this is the metric that we usually use for defining medium term inflation. But if we go to shorter- and medium-term horizons, the revisions have been even more significant. The real rates on the short and medium term have gone up, on the long term they haven’t gone up because we are witnessing a decline in long-term nominal rates, not only in the euro area but everywhere really.

The Governing Council will acknowledge these developments and within its mandate will use also unconventional all the available instruments needed to ensure price stability safeguard the firm anchoring of inflation expectations over the medium to long term.

The speech text says:

1. The ECB knows that inflation has declined.
2. The decline in inflation has not led to any decline in expectations of inflation.
3. THE ECB will, if necessary, within its mandate, use QE and other policies to keep expectations of inflation from declining.

The speech as delivered says:

1. The ECB knows that inflation has declined.
2. My usual line is that the decline in inflation is due to temporary factors that will be reversed.
3. That explanation is now long in the tooth: the longer “temporary” lasts the greater the danger.
4. In fact, it is too late to “safeguard the firm anchoring of inflation expectations”.
5. Inflationary expectations have already declined.
6. We will use all the tools we have to reverse this.

Is this deviation a mere line wobble–Draghi going accidentally off-message either because of jet-lag or because his personal view is not the ECB consensus and some of the former leaked out? Is this deviation an audience effect–Draghi seeking to give a speech pleasing to his West Side (of the North Atlantic) audience and thus unimportant, since he will revert to the East Side message assuming his plane avoids Bárðarbunga? Or does it signal a recognition on Draghi’s part that the Eurozone is heading for a triple dip, and that if he doesn’t assemble a coalition to do much more very quickly to boost aggregate demand we will have to change the name “The Great Recession” to something including the D-word, and he will go down in history as the worst central banker since the 1930s?

I would like to know…

Apropos of Cosmos Elysée (2009): On the Certainty of the Bayesian Fortune-Teller, Brad Delong: Elementary Philosophy of Probability and the War on Nate Silver, Sky Masterson: An Ear Full of Cider, Adam Elga (2010): Subjective Probabilities Should Be Sharp, and Brad DeLong: [Tuesday Virtual Office Hours: Follow-Up Questions on the Philosophy of Probability5…

Thrasymakhos: Today we discover that Sam Wang does not seem to be a Bayesian:

*Sam Wang Why you’re wrong to get excited about “60%”: Some people are excited… Nate Silver… [gives] a probability of a GOP [Senate] takeover at 60%. To cut to the chase: I do not think that number means what you think it does…

Thomas Bayes: It is simple. It means that Nate Silver stands ready to bet on Senate control next January at odds of 3-2.

Thrasymakhos: “Stands ready”?

Thomas Bayes: Yes. He stands ready to make a (small) bet that the Majority Leader of the Senate will be a Republican on January 5, 2015 if he gets at least 2-3 odds, and he stands ready to make a (small) bet that the Majority Leader of the Senate will be a Republican on January 5, 2015 if he gets at least 3-2 odds. Since Nate Silver has gained considerable success so far in life by making predictions and laying odds that reality has thereafter validated, his views on the odds are worthy of great respect unless you think you have important private information that he does not or a superior analytical methodology–and you probably should not think that, as those who did think they had better ideas of the odds than Nate Silver in the past are, for the most part, shirtless. What else could it possibly mean? What else could people take it to mean?

Jerzey Neyman: No, no, no! You have got it all wrong! Sam Wang has it right!

Sam Wang: Think of five… tosses… [of] coins are not perfectly fair, and the overall situation is a little unfavorable to Democrats. That is basically the amount of uncertainty expressed in Silver’s probability. Fundamentally, any probability in the 40-60% range is a numerical way of saying “I don’t know.”…. The certainty fallacy. Silver has done something common among paid writers, which is to do what it takes to attract eyeballs. He has rounded a probability that is barely over 50% to make the statement that one side is ahead…. Basically, whenever you see a probability like that, you should mentally say “plus or minus 20%” just to get the right idea…

Thomas Bayes: Now I am confused. So Sam Wang thinks:

1. Nate Silver should say that the probability of a GDP Senate takeover might be 40%, might be 60%, and might be 80%-that would give people the right idea.
2. Nate Silver should not say that the probability right now, August 25, 2014, with 71 days to go before the election, of a GDP Senate takeover is 60%–that would give people the wrong idea.
3. Nate Silver should not say that the probability of a GDP Senate takeover might be 30%, might be 60%, and might be 90%–that would give people the wrong idea.
4. Nate Silver should not say that the probability of a GDP Senate takeover might be 50%, might be 60%, and might be 70%–that would give people the wrong idea.

Do I have it right?

Jerzey Neyman: Exactly!

Thomas Bayes: May I say that I am having a much harder time understanding what Sam Wang means–when he says that we should say that the probability is in ¹, and not say that the probability is 0.6 or that it is in ² or in ³–than what Nate Silver means when he says that he thinks the probability is 0.6?

Jerzey Neyman: You may say it. But why should that be hard to understand?

Thomas Bayes: I find it hard to understand how a probability could be something different than it currently is.

Jerzey Neyman: But surely you agree that somebody who knew more than you did about the forthcoming Senate election would have a different estimated probability of Republican takeover than your and Nate Silver’s 60%? And that his or her estimate would be a better probability than yours?

Thomas Bayes: Yes, of course. Probabilities are associated with information sets. As information arrives and information sets grow…

Sky Masterson: Including, importantly, growing by adding the information that somebody with an information set much larger than yours wants to make a large bet against against you at your probability…

Thomas Bayes: …the probability you hold shifts. That’s what “learning stuff” means.

Jerzey Neyman: Maybe I can make an analogy that will help you understand. Suppose that there were a large urn full of marbles–red marbles and blue marbles. Suppose that there is a being, named Tykhe, who periodically takes marbles out of the urn at random by a process that will leave only one marble in the urn on November 4, 2014. Suppose that if that one remaining marble is red then a Republican becomes majority leader of the Senate in January 2015, and if that one remaining marble is blue then a Democrat becomes majority leader of the Senate in January 2015. Suppose we know right now that there are 100 marbles left in Tykhe’s urn, and that somewhere between 40 and 80 of them are red. Thus we should say that the probability of Republican senate control in the next election is between 40% and 80%. We should not say that the probability is 60% because our knowledge of the urn does not extend to knowing that there are 60 red ambles in it. We should not say that the probability is between 50% and 70% because we do not know that there are not 45 or 75 red marbles in it. And we should not say that the probability is between 30% and 90% because we do know that there are not 30 red marbles in it and not 90 red marbles in it. Is that clear?

Thomas Bayes: But there is no urn…

Thrasymakhos: So, Jerzey and Sam, your position is that we should reserve the word “probability” and use it only to refer to the betting odds of somebody with the superior information set provided by being able to look into Tykhe’s urn right now and count the marbles?

Jerzey Neyman: Exactly!

Thomas Bayes: But there is no urn. And there is no being Tykhe…

Thrasymakhos: It does make me wonder. Why do you reserve “probability” for the betting odds that someone who had the information set associated with having counted today’s–and not yesterday’s, and not tomorrow’s, and not last year’s, and not November 4’s–marbles in Tykhe’s urn would offer, and not the betting odds of somebody with a different information set?

Jerzey Neyman: The urn and Tykhe are an analogy. The point of the urn and Tykhe is that the real probabilities are those associated with an observer who understands the generating process, and whose uncertainty is over (a) how exactly some of the details of that generating process have played out to date and (b) the effect of unknowable future events in the generating process.

Thomas Bayes: But there is no urn. And there is no being Tykhe. And there are no marbles…

Mentor: Ah! It is always interesting to see young sophonts with poorly because freshly-evolved brains of a low order of intelligence attempt to wrestle with these conundrums. I am Mentor, of Arisia, an anthology intelligence of a high order. My visualization of the Cosmic All is correct to a tolerance of 2^(-50)–and with a confidence of 2^(-50) I know who will control the senate come January 2015. What you see as unknown details of how the generating process has played out to date are things I know as well as I would know the back of my own hand, were I the kind of being that had hands. What you see as the effect of unknowable future events I can foresee as easily as you can predict how old you will be the next time your birthday falls on a Sunday–or… no, I decided 10,000,000 years ago I would not tell you that and cut off this explanation here…

Thrasymakhos: Seems to me, Sam and Jerzey, that as long as creatures like Mentor exist, all probabilities must be either 2^(-50) or 1-2^(-50).

Jerzey Neyman: But no such creatures as Mentor exist!

Thomas Bayes: But there is no urn. And there is no being Tykhe. And there are no marbles. There are no red marbles. There are no blue marbles…

Mentor: I beg your pardon?

Jerzey Neyman: You are a fictional character in a space opera, not a real being!

Mentor: And what do you suppose you are? Do you think you are real?

Thomas Bayes: But there is no urn. And there is no being Tykhe. And there are no marbles. There are no red marbles. There are no blue marbles. How can the limits of our knowledge about marbles that do not exist in an urn that does not exist manipulated by a being that does not exist have any impact on our probabilities of things that do exist?

Mentor: I am at least a much-beloved figure in space operas that have had a wide readership for three generations.

Thrasymakhos: So Mentor’s assessments cannot be probabilities because he doesn’t exist…

Sokrates: Would Sam Wang accept, as a friendly amendment, the proposition that Nate Silver should say not that the probability is between 40% and 80% right now, but that if he could access and fully process all the information that is already out there today it might push the probability down to 40%, it might push it up to 80%, it might keep it the same?

Thomas Bayes: But Nate Silver can’t. Nobody can.

Mentor: I can! And more…

Thrasymakhos: Yet the assessments of the nonexistent person looking at nonexistent Tykhe’s nonexistent urn are probabilities even though none of the three exist?

Jerzey Neyman: It’s just an analogy. Real world probabilities are much more like an unknown but bounded number of different kinds of marbles in urns than they are like Bayesian or Arisian woo-woo!

Sokrates: But if he could.

Thomas Bayes: But he can’t. And because he can’t, and doesn’t know whether doing something he can’t do would push it down to 40%, up to 80%, or leave it unchanged, Nate Silver should say that the probability is 60%