Christof Koch’s book list
There ought to be an ugly Germanic word for it, the anxiety at not having read enough (I like NichtLesenAngst).
And yet in 10 years of trying to make sense of the economic world around me, I have found nothing as reliably good as the blogosphere.
Thankfully, it is a conversation, not a syllabus. In a conversation you don’t have to read every word that is spoken.
Her early years were filled with unbelievable accomplishments and a tight-knit, almost claustrophobic relationship with her father, Harry. At age ten, she became the youngest person ever to gain entry into the prestigious Oxford University. […]. She finished her PhD at Oxford at age 18, and at age 19 took on her first academic position, as a junior professor at Harvard University.
John D. Cook on whether anything is really continuous:
Strictly speaking, maybe not, but practically yes.
Carmen Reinhart on low interest rates:
The periods around World War I and World War II are routinely overlooked in discussions that focus on deregulation of capital markets since the 1980s. As in the past, during and after financial crises and wars, central banks increasingly resort to a form of “taxation” that helps liquidate the huge public- and private-debt overhang and eases the burden of servicing that debt.
Such policies, known as financial repression, usually involve a strong connection between the government, the central bank, and the financial sector.
[The different possible microeconomic explanations] also raise[s] the classic question of macroeconomics, when multiple microeconomic stories give the same macroeconomic answer, whether telling apart microfoundations matters.
Economic models are more quantitative parables than scientifically precise models, and elegant parables are more convincing. Dark matter is particularly inelegant: Models that need an extra assumption for every fact are less convincing than are models that tie several facts together with a small number of assumptions. Financial economics is always in danger of being simply an interpretive or poetic discipline: Markets went down, sentiment must have fallen. Markets went down, risk aversion must have risen. Markets went down, there must have been selling pressure. Markets went down, the Gods must be displeased.
A note to Ph.D. students: All good economic models are reverse-engineered!
None of these modeling approaches stands above the others in the list of facts so far addressed. A serious effort to distinguish them has not been made. But, given the fact that the state variables are so correlated, and that the models are all quantitative parables not detailed models-of-everything meant to be literally true, that effort may not be worth the bother.
Hans-Joachim Voth argues (in German) that the problem with investing in stocks is that when you most want to sell them, you can’t, as financial markets are often “closed” during wars and great economic crises.
So, if not stocks, what else should people put in their rainy day fund?
Nassim Taleb recommends in the “The Black Swan” to follow a “convex” investment strategy: Hold 85 to 90 percent in the most safe assets (such as US government bonds) and the rest in “venture capital-style portfolios” (p205).
But why should the stock prices of risky firms rise during economic catastrophes? They are risky because their business model is speculative, not because they do well in bad times.
Then there’s Kim Oosterlinck who shows (ungated here) that in occupied France during World War 2 small artworks outperformed other assets such as gold, equities, bonds or foreign currencies. He argues that usually such items are “conspicuous consumption”, so they are valued for the bragging rights they come with. But in bad times the fact that they can be transported and sold discreetly is more important.
Nouriel Roubini has to say on this topic:
If you worry about financial Armageddon, it is indeed metaphorically the time to stock your bunker with guns, ammunition, canned food and gold bars.
Taken literally, it would be a bad idea to save like that.
The problem is that we’re not good at dealing with low probability events. Usually we just ignore them and when we do think about them we fixate on some particular kind of catastrophe.
For example, when the nuclear disaster of Fukushima happened, the conversation turned to whether one should take precautions in case a radiation emergency occured in Europe. When there was a major power outage in the North of Germany, people talked about how it was necessary to keep sacks of potatoes at home.
But any such scenario is really unlikely. If you still want to be prepared for them, you should try to come up with a reasonable estimate for how likely each scenario is and then rationally account for them all depending on their relative likelihood. It’s a bit like “moral licensing” for altruism: We spend all our “panic capital” on one scenario and then assume we’ve done enough and don’t think about all others.
So say you’ve bought your potassium iodide tablets, you’ve stored your 14 days (German) of food at home and you keep your Krugerrands hidden away. You then hopefully realize that most investors, most of the time, should keep the majority of their free wealth in a low-cost portfolio of global stocks and bonds.
And – most importantly – the bulk of risks that people face are idiosyncratic, so they’re specific to an individual. You might fall seriously ill, loose your job or there could be a fire in your home. But the fallout from such cases is best mitigated with classical insurance rather than some specific asset.
From director Wolfgang Herzog’s Reddit “Ask Me Anything”:
I do traveling for very intense quests in my life. I do that on foot.
But do contractions cause uncertainty, they ask, or does uncertainty cause contractions? Given that we know that people are highly reactive to each other, the causality most likely runs both ways, in a feedback loop.
The deeper and more interesting question concerns what initiates this uncertainty.
It is conditional optimism that brings out the best in us.
A point similarly made by Peter Thiel.
In the book by Swiss author Martin Suter “Der Koch” (“The Chef“), the Tamil cook Maravan comes from Sri Lanka to Zurich as a refugee. He works in a good Swiss restaurant and his cooking skills have unexpected side effects.
When I studied in Zurich, I lived in a ramshackle old house next to the forest at the edge of the city and in the apartment above us lived a Tamil family. When they cooked, the stairway filled with the scent of spices and a couple of times they brought us food.
Sri Lanka’s largest ethnic group are the Sinhalese of which most are Buddhists. The North of Sri Lanka with its center Jaffna is predominantly Tamil who are Hindus. Tamils also populate India’s state Tamil Nadu just across the Palk Strait. Their language is a Dravidian non-Indo-European language and there are visible cultural differences. Men wear lungis and the Tamil Temples are adorned by colorful figures and look more like angled Mayan pyramids than the curved shapes of the North of India.
The Tamil Tigers have been seeking independence from the rest of Sri Lanka for several decades and in 2008 and 2009, they were finally defeated by the Sri Lankan army.
Switzerland has a sizable Tamil community. And back then, I saw the family’s father at a demonstration. He wore a Tamil Tigers T-Shirt and flag and marched with a group of other Swiss Tamils over the Quay Bridge towards Bellevue.
I traveled to Sri Lanka last year and the differences between North and South are stark. The South is better developed than India, there are more middle-class cars and fewer beggars.
Sri Lanka’s capital and largest city Colombo is in the Southwest, the island’s Southern center is moist and lush with green tea plantations and in the East there are laid-back surfer villages. But as you get to the North, the country becomes more flat. The sun burns and you can view far into the distance. The Lonely Planet likens it to the savannas of East Africa.
Jaffna is a weird place. There are many ruins. At night, large swarms of bats cross the city. Singapore’s Lee Kuan Yew had just died and there were posters in the street mourning for him.
The military holds a strong presence in the Northern Province. This is criticized, as they basically treat it like a colony.
It felt not just more Tamil there, but more Indian. The food was better, especially the Dosas. In the South it’s “rice and curry” most of the time.
The Dutch left the city a castle and the library is pretty. But when the Lonely Planet says the most exciting place to go during evenings is an expat bar run by former aid workers during the civil war, then you know there isn’t much going on.
In the book, Maravan cannot escape the troubles from home. And Jaffna, too, is a far from having recovered.
“La Grande Peur” or the Great Fear was a time of great uncertainty that happened in France just before the onset of the Revolution. Rumors of violent hords of bandits roaming the country-side spread and people thought the old order had stopped functioning.
Are we experiencing a Great Fear right now?
In many ways it’s not clear why we should. The world in 2016 is actually in quite a good shape. But somehow people seem to hold particularly gloomy views this year.
Tyler Cowen has to say on this issue:
The broader and more disturbing implication is that the entire global economy may be more vulnerable to mood swings.
Most likely, we’ll have to get used to a more mood-ruled world, and those will start off as being the moods of others, not our own. How do you feel about that?
And here are George Akerlof and Robert Shiller (added emphasis):
The term overheated economy, as we shall use it, refers to a situation in which confidence has gone beyond normal bounds, in which an increasing fraction of people have lost their normal skepticism about the economic outlook and are ready to believe stories about a new economic boom. It is a time when careless spending by consumers is the norm and when bad real investments are made, […].
Most economists are uncomfortable with such notions.
Most academic economists, if asked to define the term overheated, would say that it describes a period in which inflation, […], has been increasing.
Inflation itself, particularly when it is increasing, can ultimately create a negative effect on the atmosphere of an economy, akin to the effect of broken windows and graffiti on a city. These lead to a breakdown in the sense of civil society, in the sense that all is right with the world. (p65, “Animal Spirits”)
And Ben Bernanke:
[…] measures of the national “mood,” like Gallup’s “way things are going” question or questions about the “direction of the country,” show a high level of dissatisfaction.
Check out the program or this non-representative pick of papers that caught my eye:
An increase in the household debt to GDP ratio in the medium run predicts lower subsequent GDP growth, higher unemployment, and negative growth forecasting errors in a panel of 30 countries from 1960 to 2012.
[…] we find that sharply higher uncertainty about real economic activity in recessions is fully an endogenous response to other shocks that cause business cycle fluctuations, while uncertainty about financial markets is a likely source of the fluctuations.
This piece by Andreas Fagereng, Luigi Guiso, Davide Malacrino and Luigi Pistaferri in the Aggregate Implications of Micro Consumption Behavior session is interesting:
Third, returns are positively correlated with wealth. Fourth, returns have an individual permanent component that explains almost 20% of the variation.
Partisanship [in US politics] was low and roughly constant from 1873 to the early 1990s, then increased dramatically in subsequent years.
Claudia Sahm offers good comments:
Recessions are almost by definition a time of instability, and it is hard to trace down the roots of instability in models that largely assume it away. I am a big fan of belief shocks, I don’t think we can fully understand recession/recovery without appealing to shifts in expectations. And yet, I have no idea how you cleanly, credibly separate beliefs from credit supply.
A unit test is a little program that checks if some part (or unit) of your code works as expected. What arguments are there for bothering to write such tests?
- They make it less likely that bugs go unnoticed.
- It’s reassuring to first run your tests when you haven’t touched your codes for a while to check if things still work.
- After changing something in the code, it’s good to see if anything’s broken.
- Writing test also nudges you to keep functions small, as it’s more difficult to test functions when they have many input argument.
I didn’t find the existing examples of how to use it easy to follow, so I’m putting here an explanation of how to test one individual function.
You can find all codes here.
Say we have a function
add_one.m we want to test:
For our unit test, we then write an additional script which we have to name with either
test_ at the beginning or
_test at the end. So here’s the new script
The first three lines are always required and we only need to change the function name to match the name of the file.
The following function
test_normal1 is our first test case. We will pass in the value
x = 1 and check that the result is indeed 2.
So now go to the Matlab command line and run:
There’ll be a dot for every test case for this function. In this case everything worked fine, but there would be an extensive message if an error had occured.
So let’s add some more tests:
It’s a good idea to give the functions meaningful names so that when there’s an error, we know where things went wrong. Don’t worry if the names get really long, they’ll only live in this script anyway.
The tricky thing is to think of the irregular ways the function might be used. For example, the following tests check that we get the right output even if we pass in an empty matrix or an
Now let’s give the function something where would expect an error. If we pass the function a string
'Hello world' it returns a numerical vector. That’s not what we want, so let’s add
add_one.m function. So now it fails if the input is not a number.
The following test case then checks if indeed an error is returned:
I use try-catch here to check if the function returns an error. There might be better ways to do this, but this works for me.
But we don’t always just have to check that results are equal, as sometimes we want to make sure that the difference is below some numerical threshold. In this case, calculate the absolute or relative error as
actDiff and check that it’s less than some acceptable error like this:
One thing I lack so far is a way to test local functions, so functions that you define within some other function and which only that function can use.
So that’s it. If somebody has ideas for improvements, please let me know!
Fama: [The efficient-market hypothesis] a model, so it’s not completely true. […] The question is: “For what purposes are they good approximations?” As far as I’m concerned, they’re good approximations for almost every purpose. I don’t know any investors who shouldn’t act as if markets are efficient. […]
Thaler: For the first part—can you beat the market—we are in virtually complete agreement.
Fama also cites Daniel Kahneman as recommending people to invest in ETFs.
Also, Fama doesn’t think governments or central banks should step in to deflate asset market bubbles:
Fama: We disagree about whether policy makers are likely to get it right, though. On balance, I think they are likely to cause more harm than good.
Thaler argues that the rational model is how people should behave, but it’s not how they do behave. And if you want to predict how people act, you have to take that into account:
Thaler: I believe the rational model, and I think that a lot of people screw it up, and that we can build richer models with a better predictive power if we include the way people actually behave as opposed to [the behavior of] fictional “Econs” that are super smart and have no self-control problems.
Interesting thoughts by Chris Blattman:
But as I read the story, I couldn’t help but think that it’s that smugness that makes half the country hate the Times audience and want to vote for a man like Trump.
The so-called liberals of New York (like me) who push for equal rights with one hand while pushing their kids to private schools with the other. Or support more open borders on principle, failing to mention that it lowers the cost of their house help without threatening their own jobs.
Tetlock sees a division of intellectual labour, where Martin Ford and his ilk shape interesting hypotheses and more cautious and statistically minded people break them into smaller, testable pieces.
His politically mild background is important, as it turns out. His work has taught him that everyone takes a heavy ideological endowment from their environment.
But Tetlock’s belief in the possibility of a more rational world seems, happily, to be the only one that is not open to revision in the face of contrary evidence.
Nathan Lane points to this history by Richard Becker of the S programming language which then became R. See 28:12 for the history of the assignment operator
<-in R over
=in other languages. Spoiler: their keyboard had a button with an arrow.
Sci-hub is back up:
Andrew Gelman and David Rothschild on why political prediction markets are performing worse than expected:
But more recently, prediction markets have developed an odd sort of problem. There seems to be a feedback mechanism now whereby the betting-market odds reify themselves.