I didn’t know it was a day either, until work brought me to Her Majesty’s gleaming mega city of Toronto for the second time in as many months. Skyscrapers are springing up like China. Speaking of which, there is a sizable Chinese (and south/west Asian) presence, which must be a big reason why the city is booming so (that and two decades of neoliberal reforms in Canada).
Canada is famous for the ‘Canadian Green Card’ program wherein foreigners with an objective skill profile and cash to live on are given a Canadian work visa. Braaaains! This system seems to have allowed the population to grow quickly without undermining civic mindedness or…you know…spurring a secular rise in the rate of rape, car burning or votes for mean people. That is, Canada seems to have delivered on many of the promised benefits of immigration.
Here are some other things I’ve noticed:
1.Despite strong population growth, traffic is smooth. As a Yank, this impressed the hell out of me. One might almost think that first world countries could afford frivolities like new roads!
2. Torontonians are easygoing. I’m pleasantly surprised by how friendly people are given the size of the city (third biggest in North America!). Random personal interactions in Toronto are consistently more pleasant than any of the bigger cities in the Northeast (the only part of America I’ve seen).
3. The price level is reasonable. I’ve eaten midrange Japanese and Korean the last few days and its been about $25 a meal, not bad. Grocery store prices are maybe a bit lower than they’d be in urban Boston and roughly in line with Philadelphia, which is regarded an affordable city. I don’t know what wages are like, but they have to be fairly high or all these buildings wouldn’t be shooting up.
4. Toronto has more of an egalitarian feel than New York (shocking!), Philadelphia or Boston. I don’t know what I base this on, but this is a blog so I don’t have to explain myself. It does sort of look like Helsinki on steroids, so maybe that’s it.
5. I should have gone to the University of Toronto for my undergrad. Christopher Hitchens (may the lord rest his soul) once described the Great Hall at UT as akin to the cafeteria at Hogwarts. Think epic stone architecture. That seems like as good a reason as any to go to a school.
So there you have it, Toronto is a great place and you should visit, or maybe move here.
BTW, I was saddened to learn than Croatia has been
hoodwinked admitted into the European Union. I didn’t know it was happening so soon. I’ll not go into this now, but there have to be better ways of healing the damage from communism. They certainly couldn’t have extracted enough concessions from the corrupt national government, which I reckon, as Romania and Bulgaria, is dominated by former communists.
BTW, sorry for implying that immigration could ever carry negative side effects. I know I’m not really supposed to do that, I must be overtired.
I’d like to alert my readers to a fantastic new data site, Quandl.
The things I like about Quandl are that it:
1. Has loads of macroeconomic and financial time series
2. Has an API, and you can download series directly within the R or Python languages (and a few others).
3.It is free.
BTW, I have no connection to Quandl.
Rather than revise my approach further, I’m going to post a working version of how I propose to estimate market NGDP expectations.
Here is what I have in mind
Take a vector
Representing the price of a set of assets. I propose that this set of assets is strongly correlated with the market’s implicit outlook on U.S. nominal GDP. This is not necessarily a perfect data set, we can imagine that events in East Asia or Europe could steer any single one of these prices for an extended period. Ideally, we’d also have an inflation indexed bond spread, though in this case, this would hinder the analysis as TIPS have only traded since 2003. We might also include a safe private sector bond index.
Take the first and second principal components of for t spanning 1989 to 2012, quarterly frequency. These components might contain much of the market’s near-term outlook, distilled into an index of meaningless scale.
I’ve not labeled the graphs, the black line is the first PC, the red line is the second.
Next, model these components along with lagged nominal GDP in the vector ,using a selection of potential VAR models. We might not be able to say, with any credibility, what the components actually mean, but we can try to forecast nominal GDP with them.
Call these models: . In this example I look at a ‘vector error correction model’ on three lags of using only the first principal component, four lags using only the first, and three lags using both components.
Fit these models to data sequentially, covering period 1 (1989Q1) to as little as 40 (1998Q1) or as much as 97 (2012Q4).
At first, fit model to date index positions 1 to 40, forecast NGDP with this model for period 41 through 45 (remembering that NGDP is lagged one period relative to the market components). Save the percent change in NGDP for periods t+1 to t+5, in the 57×1 vector
Repeat this process over the dataset, in essence simulating the result a forecaster would have had in each quarter of the data set after position 40 (the amount of data needed to give the model a moderately good fit).
Here are the resulting s from three s
That is run off quarterly averages. This looks about right to me, as far as the deviations from 5% approximating the effective stance of policy. Here is the system run at quasi daily frequency, the choppiness of NGDP data make this cumbersome.
You could think of lots of different ways to make the models somehow account for the fact that the principal components are probably distorted by international factors, or otherwise not reliably related to NGDP expectations.
I suggest setting up some sort of Bayesian model averaging or combination system, on a wide set of potential models. That, would be an undertaking.
On and BTW: Link to the quarterly NGDP expectations here.
So the story here is that gold investors have found a new ‘crank asset’ for their marginal savings ?
I say crank asset with the best of intentions.
It is hard to overstate how important Thatcher was in turning back the worst elements of the Western mixed economy. She saved Britain from itself, at least for a time, and was right about the Euro. Rest in peace.
A quick post, inspired by silly things I’ve seen in the financial media.
The U.S. 10-year yield is about 1.8%, down from just over 2% before Cyprus. Some people say Treasury prices are rising because the Fed is buying up so much of the debt stock, though Sumner reminds us that this is not so (the PPPS at the bottom).
Instead of falling because of QEIII, U.S. treasury yields are low (despite a firmer recovery) because of developments in Europe.
I think this plot says it all:
That is a correlation of -0.79, between U.S. bonds and the Spain-Germany spread. Correlation does lift the odds of causation, just not that much.
Think of U.S. bond prices as a function of 1. NGDP expectations and 2. a “haven fee” which goes up when EMU default risk rises. Before the QEIII framework was in place, we couldn’t be sure if Europe was mostly affecting U.S. yield through #1 or #2 , but it is plainly obvious to anyone who watches financial markets carefully that Europe is the driver. You’d have to come up with one hell of a just-so model to explain how quantitative easing is able to push down U.S. yields (which is supposedly bullish) while also seemingly exacerbating the Euro Zone mess. Why the negative correlation?
Since the Bernanke-Evans rule went into effect, Treasury yields have still moved opposite the Spain-Germany spread, but commodity prices, TIPS spreads and stocks all tell us that NGDP is set to grow around 4%. So I am fairly confident that most of recent weakness in yields springs from the “haven fee” effect.
Funny isn’t it? The staunchly anti American “Europe” project is now indirectly subsidizing the U.S. Treasury.
PS: I’m not meaning to be a jingoistic Yank by pointing out that the U.S. Treasury is getting a subsidy from the European Commission. I’m just taking a swing at the EU, which has brought so much trouble to the continent.
The latest Econtalk is with Scott Sumner. Can’t wait to listen. A great week for podcasts.
Steven Pinker, who is maybe my favorite public intellectual, has new and worthwhile interview on the London Real podcast.
If you’re facing an hour and a half train or car trip or just need some conversational supplementation, I’d check this one out.
I liken the ’87 crash to that sequence in Pulp Fiction where John Travolta accidentally shoots the man sitting in the back of Samuel L. Jackson’s car. Stick with me. The mistaken shot is the stock market crash (or maybe the rally before the crash), and Travolta and Jackson are the financial market participants. The two frantically seek shelter in Quentin Tarantino’s house, uncertain of how the situation can be resolved. They call Ving Rhames (representing Alan Greenspan) in a panic. Greenspan calmly answers by sending Harvey Keitel (monetary easing) and all is well…at least for a while.
This (not safe for work) clip from the film is at least what came to my mind when a macro professor explained the ’87 crash to me some years ago. Everyone freaked, Greenspan said he was on the problem, cut rates, and “confidence” was maintained. When people speak of financial market confidence, they really mean the implicit NGDP forecast hidden in market prices.
In January I posted about a method I was toying with for estimating the market’s underlying NGDP forecast. I have some results, and will post something soon. But before that, I’d like to share some principal component graphs I’ve made along the way, which support the Mr.Wolf interpretation of October 1987. You can click the graphs below for a closer look.
To start, lets look at the first principal component from: the ten year Treasury yield and logs of the S&P 500, West Texas Intermediate month ahead futures, the major currencies, trade weighted dollar index, and three month copper futures. These are somewhat arbitrarily chosen, just what I was able to get back to ’86 without hassling my Datastream source too much. The component was calculated from mid 1986 to early 2013, though in this first graph I focus on the ’87 crash. If you are new to principal components, think of this series as a sort of multidimensional midpoint of the five series, the scale has no meaning.
I’ve proposed that principal components like this are proportional to expected NGDP in some way. In the next post we’ll see that lagged values of these components are strongly correlated with NGDP. If the component really is a proxy for NGDP expectations (the true stance of monetary policy in the Market Monetarist model), then it would seem that expected NGDP certainly dipped abruptly on Black Monday, but not catastrophically so. Put simply, the ’87 crash didn’t much shake NGDP expectations.
Now let’s look at the market prices themselves. I’ll show fewer days of trading in these plots, because it becomes too messy to get a clear read on October ’87 if I show as much as I did in the graph above. Also, note that the dates are in YYYY-MM-DD format for these plots.
If you read Sumner in 2010, you’ll quickly see the difference between ’87 and ’08/’09.
This graph of 20 day log differences shows us that oil traders were unimpressed with the stock market collapse. Oil prices didn’t fall until weeks later when stocks were rallying. Copper prices plunged on black Monday (that abrupt drop just before the 1987-10-26 date marker), but quickly rallied thereafter.
Next, the S&P 500 index along with the U.S. dollar index:
This is a fascinating graph. The dollar strengthened on Black Monday, but then quickly weakened as monetary policy eased. I had to double check the figures; I couldn’t believe the dollar fell 10% in a quarter, but it happened. This is the opposite of what we saw in late 2008 when the dollar soared.
The last constituent of the component is the five year yield:
The 5 year yield shot up before the crash, plunged back to August 1987 levels on Black Monday and then held steady for the rest of the year.
Lastly, here is a plot of the component in 2008-2009. This really needs to be mapped to an NGDP forecast to easily interpret (next post) but still gives a rough sense of how different the two market crashes were.
We already knew that market NGDP expectations held up in ’87 and plunged in 2008/2009. I view this more as a test of the principal component approach. This doesn’t mean the component is really mirroring NGDP expectations, but if I’d seen a sustained drop in the component after Black Monday, it would be back to the proverbial drawing board. Failure to reject is something.
Seems reality is too real for some folks. The authorities have shutdown Intrade.
This as a big step back, but not so surprising. As Hugh Hendry has said, governments can’t stand markets, because markets deal in the truth, no matter how unpalatable it may be. The market is like a physician who won’t sugarcoat your diagnosis.
The need to evade overreach by government is growing more acute. Seasteading, or buyout of Kaliningrad. We need a place innovators can flee to. ”Exit not voice”.