Why Monetary Policy Should Ignore Financial Stability

Financial stability is apparently the new hot reason to tighten monetary policy, and in this article for Quartz I go in on some reasons for why this is a horrible idea. An excerpt

Janet Yellen’s confirmation hearing showed signs that US monetary policy will soon adopt a third mandate. She said: “Overall, the Federal Reserve has sharpened its focus on financial stability and is taking that goal into consideration when carrying out its responsibilities for monetary policy.” While Yellen has traditionally downplayed this mission, the December FOMC meeting minutes also revealed a growing chorus of FOMC participants who believe that monetary policy should do more than just ensure full employment and price stability. Rather, they believe that monetary policy should look out for bubbles and pop them before they jeopardize financial stability. 

At first glance, this sounds like a good idea. After all, who wants financial instability? 

But back in 2002, Bernanke outlined several reasons why tightening monetary policy in response to bubbles is unlikely to work in practice. First, there’s no reason to believe that the Fed can accurately identify bubbles in advance. Second, even if a bubble appears, it’s not clear that raising short term interest rates could pop it. Third, even if monetary policy ends up bringing asset prices down, it is likely to do so only through hurting the livelihoods of average Americans.

Something that should be added, though, is that I still believe research on how monetary policy affects financial stability can be useful. Such research can help us better understand how monetary policy transmission might work, and what things regulators should look out for as the monetary policy landscape changes. However, none of this is any reason why the central bank should base the stock of money on the caprices of the financial markets.

Milken Institute Review: China's Latest Growing Pain

After writing a Quartz article on Chinese regional development over the Summer, the Milken Institute Review approached me to write a longer article on the history of Chinese regional inequality, and what the Chinese government can do going forward. My basic argument was that the last decade of growth, unlike the decade prior to that, was one of convergence among provinces. However, to ensure that this can continue going forward, it will be important to continue the process of urbanization and expansion of social services. Read more here.

Quartz: "Stop the taper talk—the Fed has actually done too little"

In light of the recent monetary/fiscal policy debate, here's an excerpt of my take from the end of November on monetary offset in the context of the Taper on Quartz:

In its recent minutes, it appears that the US Federal Reserve has been preparing to taper. Yet given the outsize role the Fed has played in supporting the recovery, that would almost certainly be a mistake. Unemployment has been ticking down, yet long-term unemployment is still very high and labor force participation is still low. While the recovery has made progress, it is still not guaranteed, and the Fed’s accommodation will be critical if the economy is to secure the gains it has made. 

The key to understanding the argument is to understand a concept central to economic analysis: the counterfactual. Counterfactuals are alternative histories of what could have been. In military history they are the answers to questions like “What would have happened if Napoleon had won the battle of Waterloo?” In this case, the key counterfactual is “What would have happened to the economy if the Fed hadn’t done quantitative easing?” Throughout this recovery, the federal government has been tightening its belt. Indeed, as MKM Partners chief economist Michael Darda has repeatedly noted, net government outlays have fallen for two consecutive quarters during this recovery, making the recent bout of austerity the biggest since the Korean War demobilization. Had the Fed not offset such a large contraction in spending, the US almost certainly would have been sent into another recession.

A Reply to Steve Williamson -- Why Dynamic Stories are Important

Steve Williamson caused a firestorm in the blogosphere over his modeling results that helicopter drops in liquidity traps reduce the inflation rate. While hist first few posts were filled with mathematical equations, he was gracious enough in a recent post to present a story of what's going on in the model (my emphasis is bolded)

Next, conduct a thought experiment. What happens if there is an increase in the aggregate stock of liquid assets, say because the Treasury issues more debt? This will in general reduce liquidity premia on all assets, including money and short term debt. But we're in a liquidity trap, and the rates of return on money and short-term government debt are both minus the rate of inflation. Since the liquidity payoffs on money and short-term government debt have gone down, in order to induce asset-holders to hold the money and the short-term government debt, the rates of return on money and short-term government debt must go up. That is, the inflation rate must go down. Going in the other direction, a reduction in the aggregate stock of liquid assets makes the inflation rate go up.

Translated further, Steve's story is as follows:

1. The central bank prints more money
2. People don't want to hold onto that money
3. To make sure people hold onto that money, the inflation rate must fall (to make holding money more attractive)
4. Hence, printing money lowers the inflation rate.

Any cursory scholar of monetary economics should find that counterintuitive. I would suggest that it's counterintuitive because it's, well, wrong. In particular, the jump from (2) to (3) isn't clear at all. If everybody receives a helicopter drop, and nobody wants to spend it, then how does inflation fall? Or in the words of Paul Krugman: "How does this requirement translate into an incentive for producers of goods and services — remember, we’re talking about stuff going on in the real economy — to raise prices less or cut them?"

On the other hand, a much more realistic view would be a "monetary disequilibrium" or otherwise stated as David Hume's price specie flow mechanism as described by David Hume in his essay "On Money". At the moment that people get more money, the inflation rate is fixed. Hence the rate of return isn't high enough to hold money, and so people spend that money. This causes prices to rise and generates inflation.

Here's the fundamental problem with Steve's model: he acts as if equilibrium conditions are enough to explain causality. Sure, in equilibrium it must be that the inflation rate must equal the liquidity value of holding onto money. But that can happen in two ways. Either the inflation rate could fall (Steve's story), or people could hold less cash lower real cash balances, thereby raising the marginal value of their liquidity holdings. Dynamic stories matter, and if you can't explain how you get to equilibrium, you may end up on the wrong side of truth.

Edit: Adjusted for a few comments from Nick Rowe

Quartz: China can boost consumption by moving children and the elderly into cities

Here is a link to my fourth Quartz article, which was on Chinese urbanization and how it relates to the whole consumption/investment debate. An excerpt:

The first chapter of Chinese urbanization was a story of migrant workers. The next chapter will be about their families.

As China continues to grow, rich, effective urbanization will require more than just providing job opportunities. It will require new policy initiatives to bring more children and elderly from the countryside into the city. By doing so, the Chinese government can begin to address Chinese income inequality, rebalance the economy toward services and consumption, all the while setting the stage for further economic reforms.

According to population data from the Chinese National Bureau of Statistics, over the past 30 years the proportion of Chinese people living in cities has more than doubled from around 20% to over 50%. Most of the migration into the cities has been in the form of migrant laborers leaving the countryside in search of higher wages.

As a result, prime age laborers are overrepresented in the cities while children and the elderly are underrepresented. According to the 2009 population survey, the proportion of people in cities between the ages of 0-19 was about 2 percentage points lower than in the villages. This number was reversed for people between the ages of 20-39. When mothers and fathers move to the cities in search of higher wages, they leave their children behind to be taken care of by grandparents. As such, if urbanization is going to continue, it will need to bring these groups into the fold.

Quartz: Emerging markets need to stop focusing on their exchange rates

Here's a link to my 3rd Quartz article on how much of the emerging market sell-off was about monetary policy failures in the emerging markets themselves. In particular, by trying to maintain exchange rate policies, central banks in these countries overexpose themselves to foreign economic conditions. The highly positive response to the recent delay of taper serves as further evidence that many of these emerging economies need better ways of insulating themselves from foreign monetary shocks. Much of the work, draws on blog posts from Lars Christensen. His examples comparing monetary policy in Australia and South Africa versus policy in Brazil and Indonesia were particularly helpful. A few excerpts:

The sell-off in emerging markets is not an omen of a prolonged economic contraction. Although capital flows were also turbulent after the 2008 financial crisis, growth in emerging markets continued. Nor is this a sign of financial crisis. “One thing most people seem to agree on is that this is not a replay of the late 1990s,” writes Ryan Avent in the Economist. A combination of exchange rate flexibility and low external debt means that emerging markets are unlikely to experience the same level of carnage as was seen in the 1997. Rather, the sell-off is a statement about how monetary policy has been unable to stabilize output in emerging markets and the importance of monetary reform. 

...Since all capital controls eventually leak, emerging markets can commit to stabilizing either the exchange rate or domestic output—not both. Economists Joshua Aizenman, Menzie Chinn, and Hiro Ito (pdf) have shown that this tradeoff is real. In the 1972 to 2006 time period, “Greater monetary independence [was] associated with lower output volatility while greater exchange rate stability [implied] greater output volatility.” 

Between these two options, the answer should be clear. Central banks in emerging markets need to focus on domestic output, and not the exchange rate.

Some Noahpinion Posts: Gold, Macro, and Bubbles

I just wanted to remind my readers that I am spending this fall guest blogging at Noahpinion, and so far I have three posts up. 

First, I have one on the determinants of the value of gold. A key excerpt:

Gold glitters, but from an investment perspective it does little else. It is backed by neither cash flows (like stocks are) nor a value at maturity (like bonds are). It's just a metal that, historically, has always been highly valued: a value that exists beyond its role in jewelry or in industry. 

So what gives? Broadly speaking, when people make a bull case for gold, they tend to talk about two catalysts. First, they argue that because central banks are engaging in expansionary monetary policy, this will lead to massive levels of inflation that will drive gold prices higher. Second, they argue that gold is valuable because it acts like a panic button and serves as insurance against crisis. These in fact, were the primary motivators behind Paulson's famous bet on gold. In this post, I hope to show that the theory underlying (1) is flat out wrong, and that the logic behind (2) does not correspond to the actual challenging facing the world right now.

Second, I had a post on an elementary outline of general equilibrium theory as it applies to macro. I particularly enjoyed writing the post because I had the chance to play around with drawings to illustrate the theory. In the post, I argue:

Any macroeconomy can be broken down into two main markets: a real market for current goods and services, and a financial market for claims on future goods and services. For brevity, I will reduce the model for financial assets to the market for money, which, because of money's role as a store of value and medium of exchange, captures the notion of "claims on goods". To simplify further, I take all the markets for goods and reduce them down to one composite market, say, for apples. From this caricature, we can start thinking about how markets fit together.

Third, I had a post arguing that there is little evidence for a current bubble in stock prices. I think this was the weakest of the three posts, but I took a look at both forward and backwards PE ratios and concluded that the evidence did not smell of a crisis.

Without a doubt, QE has been an incredible boon for financial markets. Backed by QE3, the SP500 stock index has risen by more than 12% year to date. Yet in spite of this increase in the stock market, overall real economic conditions remain relatively stagnant. Year over year inflation as measured by the core PCE price index ticks in at only 1.2% YoY, and last quarter's real GDP grew by only 1.4% YoY. This disconnect is a bit unsettling, because it suggests that bullishness in the stock market has failed to translate into broader growth. On this basis, some commentators, such as Frances Coppola, have argued that quantitative easing does nothing for the broader economy and worsens economic inequalities. But this concern can be reduced to an even simpler question: Has recent stock market growth just been a bubble?

Not Quite Blogging

This is a good time to announce that I am taking a break from writing economics blog posts on this blog for the Fall Term. Instead, I will be featured on Not Quite Noahpinion! Our team profile can be found here, and I already have a post on gold returns up.

But don't worry, this blog won't completely die out. I will be writing more technical pieces for this blog. In particular, I want to write some tutorials on some R functions, as I have accumulated a good bit of experience with them over the summer and I hope to make the beauty of R more accessible to my fellow college researchers.

I encourage you to follow Noah's blog, and as always, you can stay in touch with me through Twitter (@yichuanw). Tune in for more economics come winter!

Quartz: High-speed rail is at the foundation of China’s growth strategy

This is a link and data supplement to my second (solo) Quartz column, which was about China's high speed rail system. The thrust of the column is that, even though high speed rail is oft maligned as an overinvestment, it actually serves many crucial roles to help China both grow in absolute terms and rebalance towards consumption. I feel like this argument will be a gold mine for China infrastructure bulls like Scott, as by digging through some Chinese news articles I found nuggets like this (my translation)

Wan Xiao [the train station manager] explained, at 10 PM on June 25th, after the railway administration released the news on the website that the line would open on July 1st, that evening around 50,000 people called to ask about the train, and over 14,000 messages were left.

(You can check my translation of "万晓介绍，6月25日晚上10点，当铁路局通过官方网站发布汉宜铁路7月1日开通的消息后，当晚的访问量就达到了其上限人数5万人次，留言达到1.4万多条。")

I had a lot of fun reading these Chinese news articles, and I'm sure my parents got a kick out of seeing me finally reading Chinese of my own accord.

There were three points from the column that I wanted to elaborate on.

First, instead of focusing on some kind of Rail/GDP number, I decided to focus instead on Rail Turnover/Length of Rail. I felt that this statistic was more descriptive because it captured the idea of rail intensity, or how much each stretch of rail was being used. In many of these overinvestment narratives, you hear about how there are all these train stations that aren't being used and how it's all just overbuilt. But when you look at the sheer amount of people and cargo that are being moved on the rails, it makes you question whether the problem is excess capacity or actually excess demand.

Second, while I partially addressed ridership concerns in the article, I wanted to go further into the effect of high speed rail on the composition of transportation infrastructure in China. Obviously, if China builds more rail, airlines become less competitive. I see this as an advantage because it allows China's infrastructure to grow in a more environmentally friendly manner. Additionally, better rail infrastructure takes the burden off of highways by moving more passengers onto the faster moving trains. Therefore, by emphasizing the trains, China can take the burden off of the "planes and automobiles" part of its transport strategy.

While this may seem self evident now, there were actually concerns high speed rail would actually worsen the highway situation. In an editorial criticizing Chinese rail, China expert Patrick Chovanec outlined a bear scenario in which the introduction of high speed rail crowds out slow speed passenger rail (which it has). The migrant workers, who then cannot afford to pay for these high speed rail tickets, then opt for bus travel, further crowding the highways.

But given the ridership statistics I pointed out in my article, that just doesn't seem to be the case. Moreover, recent evidence suggests that rail has had the benefit of crowding out auto travel. One example I found was from the Qiangjiang news, which reported that after the opening of the Wuhan-Yichang rail line, demand for bus service from Qianjiang -- a city alongside the high speed rail tracks – to Wuhan fell by nearly 60 to 70%. The Yichang transport station this year also saw its ground transport volume unchanged from one year ago at 45,000 passengers. Given the massive growth in rail, this suggests that the bear case outlined by Mr. Chovanec hasn't come to pass.

Third, while my article focused on the passenger side of rail development, freight rail has also become more intense. In particular, freight intensity in many inland provinces, such as Guangxi, Hunan, and Qinghai has been growing at a steady pace. This bodes well for the development of inland infrastructure, as it means more freight needs to be moved, and high speed rail will help open up the capacity for that to happen.

As always, a link to the data is on the data page. Please reach out if you have any questions.

A Primer On General Equilibrium, or Why Money Matters

This post is meant to be a short summary of how to think about macroeconomics in terms of general equilibrium. During my conversations with Michael Darda this past summer, it became painfully apparent that clients tend to struggle with how to put money and goods markets together. As a result, I thought I should put together a little piece on my basic approach to thinking through these kinds of "across market" effects, and why it matters for some of the policy debates of our day.

Any macroeconomy can be broken down into two main markets: a real market for current goods and services, and a financial market for claims on future goods and services. For brevity, I will reduce the model for financial assets to the market for money, which, because of money's role as a store of value and medium of exchange, captures the notion of "claims on goods". To simplify further, I take all the markets for goods and reduce them down to one composite market, say, for apples. From this caricature, we can start thinking about how markets fit together.

In normal times, people receive apples and money from the sky in the form of endowments (i.e. their wealth), and they make decisions about how to balance their cash and apple balances. Apples are transacted, bellies are filled, and life is good.

But suddenly, a recession hits. What does this look like? By definition, a recession is when there is a general glut of goods that aren't consumed. In this toy economy, this corresponds to a situation in which some people have apples but choose not to eat them! This may seem peculiar, but remember that the market for apples in this model represents a composite of all goods markets. So it could be the case that while everybody has apples, some want Red Delicious while others are looking for the tartness of Granny Smith. In more formal economic models, this is glibly incorporated by requiring that people do not consume their own endowment and instead trade for consumption. In any case, apples aren't eaten and we have a rotten general glut.

But this seems peculiar -- aren't markets supposed to clear? Not necessarily. Prices don't always adjust instantly, so we can have excess supplies and excess demands. However, economists do have a way to constrain what this non-clearing state looks like. In particular, according to Walras' law, assuming everybody spends all of their wealth, if there are excess supplies (i.e. too much produced) in some markets, then they must add up to excess demands (i.e. too little produced) in other markets. In other words, even if supply does not equal demand in each market, supplies must add up to demands across markets.

The requirement that everybody spends their endowment is crucial. It means that Walras' law doesn't apply just to the market for apples because not everybody spends all their wealth on apples. Instead, some people may put their wealth in money. But once we include the money market, we do have the condition that everybody spends their endowment, and therefore Walras' law does apply to the entire macroeconomy of apples and money.

This leads to the most important conclusion from general equilibrium theory as related to monetary economics:

If there is an excess supply of goods, it must be the result of excess demand for money.

The goods market by itself is not enough to generate a recession with a general glut of goods. Only when there is the possibility of excess demand in money markets can recessions actually occur. Therefore the market for money is what gives a macroeconomy its business cycle feel. This is why money is so important for macro -- fluctuations in the money market are the proximate cause for any general fluctuation in the goods market. This is why, as Miles Kimball says, money is the "deep magic" of macro.

While this "apples and money" approach is the canonical presentation of general equilibrium, it is not the unique representation. For another interpretation, think about what the financial market really is. Since it represents the entire universe of claims on future goods, finance can be understood as a veil between the present and the future. So instead of focusing on the relationship between goods and financial markets at one point in time, we can cut out the middle man and instead think of general equilibrium as a sequence of goods markets that occur across multiple points in time. In this version, there is no financial market per se, but buying an apple in "tomorrow's goods market" represents buying a financial contract in the canonical model. Therefore, instead of thinking about the markets for goods and money, we can instead think about the markets for goods today and tomorrow.



The same excess supply and demand relationship works in this model. If there is an excess supply of goods today, then it must mean that there's an excess demand for goods tomorrow. So in this version of the model, the reason apples aren't eaten today is because people want to wait and eat apples tomorrow. So we get a corollary to the above conclusion:

If there is an excess supply of goods today, it must be the result of an excess demand for goods tomorrow.

Each of these stories has its own strength. Since the first goods-money model includes actual money, it can help us understand how the price level is determined through monetary neutrality. On the other hand, since general equilibrium is only concerned about relative prices, and since individual dollars are not transacted in the second story, the second story has no "goods/money" relative price -- i.e. the second story cannot pin down an aggregate price level. However, the second story does a better job of being explicit about intertemporal choice. And for now, this intuition about relative prices between the past and the future will be powerful enough that I will focus on this second approach.

So if recessions are caused by an excess demand for goods tomorrow, how does policy fix a recession? Here, our microeconomic intuition will suffice. If we want to reduce excess demand for a good tomorrow, all we need to do is raise its price relative to today. And since the price of an apple tomorrow is just the amount of money I need to save to afford it tomorrow, lowering the rate of interest between today and tomorrow is sufficient to raise the relative price of tomorrow's apple and get me to consume today. Note that this has an analogue in the first "goods/money" story. By lowering the interest rate on financial assets (and expanding the supply of money), this makes financial assets less worthwhile to hold. People then pivot away towards the goods market, and the general glut is consumed.

If recessions are generated by this process, the interest rate story shows why monetary policy can be politically difficult. Monetary policy, in this model, just tries to change the relative price of consumption today and tomorrow to resolve the general glut. But just when people most want to save, the interest rate falls and it becomes more expensive to do so! This is part of the more general political difficulty of the price system. Under a price system, the most desired objects are the most expensive. While this may be unpleasant, it's certainly efficient and necessary for avoiding recessions.

Now, one question that arises is why the real rate of interest doesn't automatically equilibrate to solve these excess demand problems. This is actually a very good question, and is the reason why monetary economics is so important. The primary explanation is that the Federal Reserve may not move fast enough to provide enough money to serve as claims on tomorrow's goods, and therefor the real rate spikes when a crisis hits. This is why we invest so many resources into studying monetary economics, because it is the proximate cause of most recessions.

So here we close the loop. From a relatively simple model, we now have a theory of employment (apple recession), interest (relative prices), and money (in the canonical representation).

Now we get to the fun part -- applying this framework to some of the policy debates of the day.

Let's start with monetary policy. By visualizing a macroeconomy through a sequence of markets, it becomes apparent why forward guidance matters. Even if the interest rate for today is zero, future interest rates may not be. Therefore, by promising to hold rates low for an extended period of time, that makes future apples more expensive relative to current apples. Now, the exact adjustment path may not be ideal, but so as long as we lower the interest rate enough to create enough excess supply in the future, we will be able to restore demand today.

Interestingly enough, quantitative easing is not in this picture. However, that's a long conversation that will receive its own post in the future.

We can also think about fiscal policy in this framework. If a recession is just a sign that there's excess demand for goods tomorrow, then for fiscal policy to work, it must convince people to bring some of their future consumption into the present. The conventional old-Keynesian approach to this (i.e. the Intro macro approach), is to argue that by giving people more money today, that makes them want to consume more today, which then directly solves the recession. But the actual mechanism is more subtle, and the efficacy of fiscal policy is entirely determined by its effect on intertemporal choice.

The Ricardian critique of fiscal policy also pops out of this framework. Ricardian equivalence, roughly speaking, argues that since consumers will take the future costs of taxation into account, therefore fiscal policy will have little effect. In this model, this future cost of taxation means people don't reduce their excess demand for goods tomorrow. Because they know the government will take away those apples, the agents are trying to save up so as to have enough to eat when tomorrow comes. Therefore the whole Ricardian effects/positive multiplier debate again just comes down to whether fiscal policy is actually effective at changing the patterns of consuming today and tomorrow.

In this context, the Federal Lines of Credit proposal from Miles Kimball makes a lot of sense. By extending lines of credit to those people who need it most, Federal Lines of Credit can persuade people to reduce their demand for goods tomorrow in favor of goods today.

I'm not entirely satisfied with this model. In particular there are the glaring omissions of rigorous foundations for inflation or intertemporal production. However, I do think it does serve as a baseline for understanding why intertemporal choice is so important for understanding macro, and I hope to expand on it in future posts.

Pictures were drawn in Paper 53.

A Practitioner's Thoughts on Market Monetarism

This summer, I have been working at MKM partners with Michael Darda doing a wide range of macro research. Since Michael is one of the leading street economists who uses a lot of market monetarist concepts in his work (monetary offset, nominal GDP targeting, market signals), I have accordingly been doing a lot of work on monetary policy and nominal GDP during my time here. This blog post is meant to talk about some problems I have encountered as I have tried to write about these market monetarist concepts in my reports for Michael, and I hope this can be useful for fellow market monetarists -- especially fellow practitioners.

Before I delve into the specific problems, it might be useful to consider a summary of key propositions that recur in market monetarist discussions. In no particular order, they are summarized below:

1. Interest rates are an unreliable indicator of the stance of monetary policy. As Milton Friedman reminds us, low interest rates typically indicate that monetary policy has been too tight, and high interest rates typically indicate that monetary policy has been too easy. For example, monetary policy throughout Japan's lost decade was too tight as the central bank would raise interest rates at the first sign of inflation. But as a result, Japanese interest rates have held steady at very low levels. On the other hand, monetary policy in the United States during the 1970's was far too easy, and as a result interest rates were very high. This is because the level of the 10 year nominal rate is determined more by money velocity than anything else.

2. The only reliable indicator of the stance of monetary policy is a nominal aggregate, such as nominal GDP. Given that interest rates are an unreliable guide, we are left with judging a policy stance by its outcomes. Since the goal of monetary policy is to provide a nominal anchor, then the stance of monetary policy is determined by how the nominal aggregate performs relative to the target. So if nominal GDP is above trend, monetary policy is too tight, and if it is above trend, then policy is too easy. 

3. Market signals serve as the optimal forecast of future economic conditions. Since the price of securities typically reflect all available information, they can serve as a high frequency measure of market expectations. This is particularly attractive because it means a relatively small firm like MKM can abstract away from building a structural forecasting model and instead focus on interpreting the price signals in individual markets. 

4. Never reason from a price change. Clients struggle with this one, but it's really quite simple. In economics, whenever there's a change in conditions, it's because a curve -- supply or demand, liquidity preference, etc. -- has shifted. As a result, a quantity or price changes. But for any given increase in price, whether quantity goes up or down depends crucially on whether the change in price is caused by a supply or demand shock. This sounds like trivial microeconomics, but people often forget it when they start talking about finance. Clients tend to go straight to questions such as "how does this rate hike affect housing markets?" or "how will this increase in crude prices affect the economy?" without asking "why are rates rising?"
Looking back at some of the work I did this summer, I have two takeaways -- one positive, one negative -- from these four core ideas.

First, the positive. The notion of market signals and of reasoning from curve shifts (i.e. 3 + 4), and not price changes, led me down an interesting path of trying to identify curve shifts from financial market data. This led to my "Market Monetarist Approach to the Interest Rate Puzzle". The core idea here is that you can use three financial indicators -- the SP500, the TIPS spread, and the 10 year treasury -- as proxies for three "real" economy indicators -- nominal GDP, the inflation rate, and the risk free rate. Now, the stock market one is a bit difficult because equity values are not only a positive function of cash flows (~ nominal GDP), but also a negative function of the risk free rate (because of discounting). Nonetheless, it's one of the few real time metrics we have for growth expectations.

With these three changes, I interpreted the recent change in the relationship between the 10 year inflation breakeven and the SP500 as a sign of an aggregate supply shock. This was my conclusion from some time series analysis that showed the slope of the relationship between the SP500 and the TIPS spread has not changed, but the intercept has increased. Statistically, this translates to the statement "at all levels of expected inflation, the stock market has higher returns." If we accept the above dictionary into real economy terms, this translates to "at all levels of inflation, nominal GDP is higher" -- the smoking gun of a supply shock.

I did some follow up work on interpreting these structural shifts in the interest rate puzzle post.

But now, the negative. Identifying the stance of monetary policy by the outcome leads to circular statistics. If you attribute all fluctuations in nominal GDP to bad monetary policy, then of course monetary policy will seem like a big issue! Put another way, you can observe the positive relationship between nominal GDP and real growth without requiring that monetary policy drives nominal GDP. The tight correlation between nominal GDP and a whole host of other aggregates does not identify a market monetarist viewpoint of the world. And because of the Lucas critique, monetary policy may be unable to exploit this relationship to restore real growth. Perhaps if you use a good bit of economic history, you could identify certain scenarios of exogenous monetary contractions. But in the end, focusing on nominal GDP to determine the stance of monetary policy makes it hard to do any kind of systematic statistical analysis.

But that doesn't mean there isn't any statistical evidence.

In my view, one of the more robust pieces of evidence for the power of monetary policy comes from an analysis of fiscal multipliers in open and closed economies. To see why this matters, we need to think about Mundell's impossible trinity. The impossible trinity states that no economy can simultaneously have free flows of capital, a pegged exchange rate, and a sovereign monetary policy at the same time -- you have to give up at least one. Given that most countries have been dismantling their capital controls (especially since capital controls eventually become porous), you can identify whether a country has a sovereign monetary policy by seeing if it has a pegged exchange rate regime. Econometrically, the exchange rate regime serves as an instrument for effective monetary policy that avoids the problems inherent in using interest rates.

With a few more assumptions, we'll be going places. Suppose that central banks with sovereign monetary policies tend to maintain some kind of nominal stability -- whether inflation or nominal GDP. Then as a result, these central banks would tend to offset fiscal policies more, as those central banks under pegged exchange rates would have to subjugate their monetary policy to maintaining the exchange rate. As a result, if monetary policy matters for real growth, then countries with pegged exchange rates (and therefore no sovereign monetary policy) should exhibit higher fiscal multipliers. This is because these countries have no potential for fiscal offset. By this chain of logic through Mundell's policy trilemma, I have reduced the problem of "Does monetary policy matter for real growth?" to "Are fiscal policy multipliers higher in pegged exchange rate regimes?"

And are they? Most certainly. In an NBER working paper titled "How Big (Small?) are Fiscal Multipliers?", the authors find that the long run multiplier for countries under pegged exchange rates is around 1.4, whereas the multiplier for countries under floating exchange rates is statistically no different from 0. In fact, the authors themselves come to this conclusion about monetary policy. In particular, they show that the monetary offset if floating rate regimes doesn't come through the current account, but rather through private consumption. Their conclusion is that "consumption responds positively to government consumption shocks only when the central bank accommodates the fiscal shock" -- a sure sign that monetary policy is an important force governing the nominal (and real) economies in the short run.

This pegged/floating exchange rate example bears itself out through the natural experiment comparing austerity in the Eurozone and the United States. Because Eurozone monetary policy has been much more tepid, they can be identified as lacking a responsive monetary policy. So although both economic areas have undergone savage austerity, only the Eurozone has really suffered -- more evidence that monetary policy really does matter.

(Note, an older version of the plot with government spending was used, but data concerns were raised by Mark Sadowski and David Beckworth. In particular, Beckworth pointed out the correct measure of austerity is the change in the cyclically adjusted primary balance, as provided by the IMF Fiscal Monitor)

However, one consequence of this kind of analysis is that it's hard to quantify the effect of monetary policy on nominal GDP growth -- there's little guidance on how much QE translates into how much growth. Perhaps the expectations channel means that this effect is impossible (and maybe even meaningless) to quantify, but it is a limitation of this mode of analysis.

Once we accept this analysis and think of monetary policy as driving nominal growth, then the market monetarist mindset of using deviations of nominal GDP to track monetary policy starts to make sense. Once you establish the empirics through other means, the theory of market monetarism comes into play.

Overall, I find the core ideas espoused by Scott Sumner and fellow market monetarists very powerful. In some regards, they lend themselves easily to financial econometrics and help to organize a a coherent explanation of the macro environment. But some of these ideas need more formal empirical backing -- something that becomes very apparent when talking to clients.

China's Growth: A Look Inland - Data Supplement

Today my Quartz column on the changing economic geography of China was published. In this post I intend to cover some extensions of the article that did not make the cut, and in addition go through some of my data analysis procedures so as to provide a resource for fellow students doing similar research.

A central idea is that the base unit of analysis for the Chinese economy should be the province. This is because China's massive size make its provinces as large as entire countries. For example, Guangdong, a coastal province, has 108 million residents. In comparison, Mexico only has 112 million residents and the entire Western United States only has 71 million residents. The entire continent of Europe has only around 740 million people -- a little less than half that of China's 1.3 billion. As such, lumping all the Chinese provinces together into one entity called "China" papers over so much heterogeneity in income levels and growth rates -- resulting in a very misleading picture about the actual economic situation.

To get an idea of these massive income differences and why it's important to look at provincial data, consider the stories of Guangdong and Guangxi, two neighboring provinces in southern China. In 2011, Guangdong, the relatively rich coastal manufacturing center, had per capita income of about 51,000 yuan (~$8,300 USD). Yet Guangxi, an inland province right next door, had nominal per capita income of only 25,200 yuan (~$4,100). Does it really seem plausible that Chinese growth will slow down so suddenly  that two neighboring provinces whose names differ by one Chinese character* will maintain such a large income gap into perpetuity? Given that Guangxi's per capita income increased by a factor of 3.36 from 2001 to 2011 and Guangdong's per capita income only increased by a  factor of 2.06, I would have to say no. Moreover, even if income levels do not completely converge, income growth should. Since Guangxi's income growth rate is still so high, I have to conclude that it's growth will likely be sustained for some time. Had I not analyzed the provincial data, I would have instead seen a downward trend in national real GDP growth numbers and concluded that China will suddenly slow down. But by taking into account the way growth rates evolve across provinces, I arrive at a more optimistic GDP number.

Geography is especially important given that many of the arguments made by Krugman and a recent IMF working paper center on Chinese labor markets. The argument is that since China has become richer, China has reached "peak peasant" and can no longer sustain such high levels of growth. But I'm left asking -- which provinces have hit this peak? Given that the inland provinces are still relatively poor, there still seems to be a lot of room for these provinces to grow. Although the move towards manufacturing in inland provinces may be a sign that coastal provinces are facing labor shortages, the "reach for peasants" suggests that inland China still has plenty of labor market slack left As a result, I am left quite skeptical about these dramatic bear stories for a sudden slowdown the Chinese economy.

I also want to add one more graphic to this conversation about China's growth. While the scatterplot in the column does a good job of showing convergence, I wanted another plot to just show how much individual Chinese provinces have grown in the 10 years spanning 2001 to 2011. I settled on the chart below. Besides the components in the legend, the small numbers to the left and right of each dot is the nominal per capita income (in thousands) for the specified province and year. The black number in the middle of the band is then the ratio between 2011 and 2001 levels of real GDP.

The nominal number is useful because it allows relatively quick conversions into U.S. dollars. As such, it seems that the per capita income in Shanghai is around $13,300 -- a level slightly ahead of Mexico's per capita income of $10,247 and the U.S. poverty line for a single person household of $11,344. The black multiple then emphasizes how much individual Chinese provinces have grown. These above-three multiples correspond to over 12% growth, so if a child entered elementary school in 2001, then by the time he or she goes into elementary school, GDP in that province would have doubled.

Of course, there are risks to the bull case that I present in my Quartz column.

Chief among these risks is if there's an environmental constraint prevents the inland provinces from obtaining the same levels of income as the coastal provinces. The Solow model (on which convergence is based) does not take into account natural resources, so if natural resources run out this process of convergence could fall apart. This does not have to be a hard scientific constraint either -- public outcry against environmental destruction would have a similar effect. While I agree that China does face serious environmental challenges (particularly in air and water pollution), I don't think protests will play as large of a role that people suggest. Remember that the recent large scale environmental protests -- in Zhejiang against a petrochemical plant and in Guangdong against a nuclear plant -- have taken place in the richer coast. Therefore inland China still has a way to go before this environmental constraint becomes more severe.

Others may raise the issue that the Chinese provincial data are a dangerous form of "science fiction". Indeed, it is a bit peculiar as the sum of all the provincial GDP numbers does not equal the total national GDP. But as Princeton professor Gregory Chow notes, while year to year GDP growth rates may be easy to manipulate, levels are not. Since the levels are recollected every year, measurement errors accumulate and therefore any kind of fake data becomes unsustainable. As a result, I focused on a 10 year average growth rate to resolve the issue of year to year measurement errors. Moreover, a recent San Francisco Fed economic letter found that national Chinese data seems to be accurate and consistent with a wide variety of indicators. Thus it seems doubtful that the main convergence result was just the result of data manipulation.

The bottom line is that China's great size means that attention needs to be paid to the individual provinces. On the basis of the provincial levels of growth, I am left quite optimistic about the future of Chinese growth.

If you want to try and replicate it (please do), just consult the public dropbox folder. The workflow goes from running all the STATA do files first and then transitioning into solowQz.R file to draw all the pictures. I have also included a Makefile to go through this workflow. (A Makefile executes all the code in order according to the dependencies. If you plan on doing any major work with code you really should learn a little bit on how to use them)

The one interesting methodological issue was how I used convergence to forecast future provincial growth. What I did was run a weighted least squares regression of average growth rate against initial log income, in which data was weighted by population and the estimator minimized the sum of weighted square residuals. On the basis of this regression, I assumed that the same relationship between initial income and growth continued into the next ten years and constructed measures of what growth should look like. After I had per capita income estimates, I assumed that population in each province would stay, and on this basis calculated total real GDP numbers by adding up the GDP in each province.

I had a fun time drawing the maps as well. I used R to interface with the GADM databases, and you can look at the code in chinaMap.R to get a better idea of what's going on.

If there are any more questions on code, please reach out. My email can be found on my About Me page.

*Guangxi and Guangdong literally translate to the western and eastern expanses, respectively. They are really are two sides of a lingual coin.

China's Circularity Problem

In the context of future looking monetary policy, the circularity problem refers to the problem that central banks face when they try to use market signals to guide policy. The general problem is that the market signals may include expectations of future policy in addition to their expectations of future shocks, so that the market signals fool the central bank into pursuing inappropriate policy. For example, if the private sector believes there will be a large shock to consumer demand in the future, but also believes that the central bank will fully offset the shock, then market expectations of inflation may not change. If the central bank looks at the inflation expectations and concludes that there is no threat to aggregate demand, the central bank may end up not offsetting the shock, and the markets fall in response.

The most recent example of this in U.S. financial markets is the Fed taper. Before the Fed taper talks, the general expectation was that quantitative easing would continue into the indefinite future and that there would be no premature tightening. As a result, the stock market seemed very resilient because there were expectations of strong growth conditional on Fed easing. The Fed misinterpreted these expectations as independent of the Fed's policy of QE and decided to tighten.

However, fiscal authorities can also face the same circularity problem. If an economy is highly dependent on government spending, then real economic conditions may be determined conditional on expected future fiscal easing. And if the fiscal authority sees the strong current economic conditions as a justification for austerity, then this too may cause a fall in growth in the same way that a premature monetary contraction can slow growth.

The Chinese government is currently facing this fiscal policy circularity problem. In an interview on June 18th with the IMF mission chief for China Markus Rodlauer, he notes that high frequency data such as retail sales, investment growth all point to moderate growth. Even thought the PMI may have faltered a little bit, it's well within historical ranges. Rodlauer takes this and makes the conclusion that there's really no need for stimulus. 

While he may be right, it is also likely that the Chinese government could fall into a fiscal circularity problem. Especially since Chinese fiscal policy has the ability to reallocate a large amount of resources, much of business is conducted on the basis of expectations of future government policy. Under these conditions, concluding that economic conditions are strong on the basis of high frequency data may cause the fiscal authority to be too sluggish in responding to a slowdown in growth.

Casting and Melting with Paired Data

Today's post is not about economics, rather it's a note from an R programming struggle that may be helpful for fellow undergraduate researchers.

I'm often testing forecasting models, and what this ends up creating is a bunch of "forecasted" variables that are paired with the "actual" values. R has fabulous faceting capabilities, and I have often wanted to reshape the data in a way where the category of forecasted variable as an identifier, and then two columns that list the forecasted and actual variables. In other words, if the code starts from something like

       aAct      aPred       bAct      bPred id
1 1.2076384 -0.6735547  1.4994464 -1.0691975  1
2 0.4999706 -0.7188215 -0.3601551  0.7224729  2
3 1.0340859 -0.1108304 -0.5941295  0.5027085  3

And I want to convert it where one column has an id, another one identifies whether I'm forecasting a or b, and a third column that has the forecasted value, and then a fourth column with the actual value.

The procedure in R involves "melting" the data frame and then "casting" it. Melting is rather simple -- you provide a set of identifiers, and then the data frame is melted down to only that identifier, the values, and another indicator variable that tells you what the value is supposed to represent. In the above example, if we let df be the data frame described above, I would run:

df.m = melt(df, id.vars = 'id')

   id variable      value
1   1     aAct  1.2076384
2   2     aAct  0.4999706
3   3     aAct  1.0340859
4   1    aPred -0.6735547
5   2    aPred -0.7188215
6   3    aPred -0.1108304
7   1     bAct  1.4994464
8   2     bAct -0.3601551
9   3     bAct -0.5941295
10  1    bPred -1.0691975
11  2    bPred  0.7224729
12  3    bPred  0.5027085

Now I need to "unmelt" part of the data frame to get the forecast/actual pairings. In R, this is known as casting and I know that I personally had a pretty hard time decoding the documentation. The function goes along as

cast(df.m, <IDENTIFIERS> ~ <VALUES>)

The second part is known as the casting formula and is the part that I have struggled with. But in its most simplest form, the casted frame will look like something with all the identifiers added together as uniquely identifying units ,and then the <VALUES> variables being the labels for the actual value column. If that sounded confusing, I apologize. Perhaps solving the example would help.

First, I need to find a way to identify whether a row is looking at a or b, and whether it is a forecast or an actual variable. So I first create these variables:

df.m$var = substring(df.m$variable, 1, 1)

df.m$type = substring(df.m$variable, 2) 

Which gives me the data frame:

> df.m
   id variable      value type var
1   1     aAct  1.2076384  Act   a
2   2     aAct  0.4999706  Act   a
3   3     aAct  1.0340859  Act   a
4   1    aPred -0.6735547 Pred   a
5   2    aPred -0.7188215 Pred   a
6   3    aPred -0.1108304 Pred   a
7   1     bAct  1.4994464  Act   b
8   2     bAct -0.3601551  Act   b
9   3     bAct -0.5941295  Act   b
10  1    bPred -1.0691975 Pred   b
11  2    bPred  0.7224729 Pred   b
12  3    bPred  0.5027085 Pred   b

Now I can cast the frame. In this case, I would use the formula

df.mc = cast(df.m, id + var ~ type)

This is how you interpret the formula. Id + var means that every observation is uniquely identified by it's id code and the variable we're forecasting -- a or b. Then "type" on the right side represents the new variable names that will be filled by the values.

Hope this is useful to others so they don't end up spending hours agonizing over the issue as did I.

More on Growth and Convergence Within Countries

In my last post on China, I touched on the issue of Chinese growth by showing a graph with the distribution of Chinese per capita incomes by province, and arguing that there is a strong convergence story pushing China towards more growth. In the comments, Tamar makes a note that many countries do not converge. For example, per capita incomes in Mississippi and Connecticut differ by a factor of about 2, even though the United States is a relatively developed Country. He also suggested that I take a look at Brazil. And so I did. I took a look at the distribution of  province per capita income divided by country per capita income for three emerging market economies: Brazil, Mexico, and China, and found that indeed, they were quite close!

I wondered if it was because I didn't weight for populations, so I downloaded some Mexican population data from their government's website. I didn't have time to do Brazil, but even comparing China and Mexico I found that the distributions were quite similar.

On first pass, this bodes poorly for a convergence hypothesis.

But let's think back to the Solow model. We should only observe convergence in income levels if technologies and savings rates are all identical. But it's entirely plausible that these can differ across provinces, and that they differ for extended periods of time. Therefore a better metric to evaluate convergence is not whether they converge in levels, but rather if they converge in growth rates. In the Solow model, at the steady state, all countries grow at a rate equal to the rate of population growth plus the rate of technological change. If they're all bound together (eg if they're all large counties in one country), then g should be similar across them, and demographic trends typically do not differ hugely among provinces in the long run.

So if we look at growth rates, now we see convergence at work. As a technical note, I only had data for Mexico from 2003 to 2010. So I got the ratio by exponentiating the 7 year ratio by 10/7. 

So even though Mexico and China have similar distributions in terms of their with country income levels, they have widely different distributions for growth. Therefore I stand by my original belief that China still has a lot of long run growth potential to go as the poor provinces catch up to the rich.

China's Provinces and why National Data can Mislead

Close your eyes and think of China. What do you see?

If you were like me, you saw a large metropolis filled with high rise apartment buildings, inked with chronic air pollution, humming along to the sounds of millions of residents getting through their days.

I believe this is also the image many economic commentators have in their minds when they talk about an upcoming "Chinese" slowdown. But what I want to do in this short little post is to demonstrate why thinking this way neglects one of China's most important quality: its size.

China has a total of 1.34 billion people spread over 23 provinces, 4 municipalities, and 5 autonomous regions. Individual provinces in China can have as many people as entire countries. The coastal province of Guangdong has a population of 105 million -- just shy of Mexico's 112 million and far exceeding every country in the European Union. Sichuan, an inland province (known for its spicy food), has a total of 80 million inhabitants -- larger than the entire Western Untied States combined. In this sense, it's better to think of China as a collection of smaller countries united under a currency union called China, and not as a uniform economic entity.

For example, consider the following map from Wikipedia showing per capita income by province.

As can be seen, there are vast disparities in income. Whereas the coastal provinces are quite rich, the inland ones are quite poor. However, the chart understates these differences because it uses a log color scale. Below is a histogram of the 2012 per capita income and population statistics pulled from the China Data Center associated with the University of Michigan.

GDP per capita in Shanghai was 85,000元 whereas GDP per capita in neighboring Anhui was only  28,792元. Translated into market exchange rates this means an average GDP per capita of $13,848 in Shanghai and only $4690 in Anhui. If we take the Solow model seriously, what this suggests is that there is a massive potential for convergence within China. Even if the inland provinces do not face as favorable conditions as the coastal provinces did when they got rich, do you really expect the 80 million residents of inland Sichuan to stay at 60% of coastal Guangdong's income forever? Especially since China does do so much manufacturing, Dani Rodrik's work on unconditional manufacturing convergence suggests that these poorer provinces will inevitably partially catch up with the richer provinces. There's just not enough income for them to get caught in a middle income trap.

There is also no systematic relationship between population and income. No matter the combination of big or small, rich or poor, there is a Chinese province that fits the description.

Recognizing this heterogeneity also provides a good reason for why looking at China's GDP per capita statistics provide an overly rosy picture of China's wealth and an overly dour prospects of China's future growth. Because there are a few provinces that are now somewhat rich while most provinces are still very poor, mean GDP per capita for the nation does not accurately represent the plight of most provinces. You can see this by the fact that most provinces in the above scatter plot are below the regression line that approximates the mean level of GDP per capita. As a result, we underestimate the role convergence has to play in bringing more Chinese economies out of poverty and therefore underestimate the true growth potential that China has.

Bottom line is that "turning point" arguments that fail to consider the subtleties of individual provinces will lead us astray. Too often, we associate China with middle income images of massive apartment complexes, where in reality much of China is still very poor. Any serious evaluation of where China is going requires careful consideration of how we think growth in individual provinces will evolve. And based on the provincial data, I am quite optimistic.

A Market Monetarist Approach to the Interest Rate Puzzle

What’s going on with real rates, inflation breakevens, and the stock market? From the beginning of 2010 to the end of 2012, these three variables have affected each other in a predictable way. Higher inflation breakevens pushed up the stock market as they served as a sign that aggregate demand was rising. Growth in real rates was associated with increases in the stock market as the real rates served as a predictor of future growth. However, these relationships have broken down in this first half of 2013. In this post, I aim to explain why. By combining movements in market data with traditional economic theory, there is convincing evidence that the recent change is due to a positive aggregate supply shock, and therefore bodes well for economic growth looking forward.

This post will proceed in three acts. In Act One, I introduce some work that has already been done on this question. In Act Two, I present a new approach to process the market data and the theory that justifies the observations. And in Act Three, I address any residual concerns. Let us now begin.

Act One -- The Work that Has Been Done

Recent trends in financial markets since 2010 are summarized below. In it we have the movement in the 10 year real interest rate, the 10 year inflation breakeven, and the SP500. During the 2010-2012 time period, the 10 year inflation breakeven was very tightly correlated with the SP500, and if you squint you will notice that increases in the 10 year treasury yield also were correlated with increases in the SP500. However, this seemed to reverse itself starting in 2013. Even as inflation expectations were falling, the SP500 still gained steady ground. Also, when the 10 year real interest rate spiked in recent weeks, we saw a temporary fall in the SP500.



Evan Soltas has documented the breakdown of the interest rate relationship. There are two signals communicated by a rising rate. First, it could be a signal of stronger future growth -- which should send the SP500 up. On the other hand, it could be a sign that monetary policy will be too tight -- which should send the SP500 down. By looking at 90 day rolling correlations between the daily percent change in the 10 year treasury yield and the SP500 stock index, we can tell the difference. Evan has observed that the correlation coefficient between the two changes is quickly approaching zero. According to him, this signals that “over the past 90 days, monetary tightening has been as important to rates as has been macroeconomic strengthening”. The June survey of primary dealers further confirms this hypothesis.

Brad Delong and Matt Yglesias have both come into this debate on Evan’s side, arguing that the Fed has been engaging in a stealth monetary policy tightening. To them, these trends are signs that growth could suffer again in the upcoming months as the Fed decides to tighten too early.

On the other hand, I have looked at the relationship between inflation breakevens and the SP500 and believe what we’re really looking at is a positive supply shock. I find that even though 2013 has been characterized by falling inflation breakevens alongside a rising SP500, marginal increases in the TIPS spread still have a positive effect on equity prices. The only difference is that the SP500 seems to have a higher trend growth level -- an alpha with respect to inflation, if you will. I interpret this as an expectation of higher output at every level of inflation. I identify this with a textbook increase in aggregate supply, and thus argue against the monetary tightening hypothesis.

Act II - Another Look at the Data

One unfortunate oversight of the analysis Evan and I have each done is that we don’t fit our stories together. He says tightening, I say aggregate supply, and we each point to our individual data. But an open question remains: how do our theories explain the other person’s data?

To try and estimate this, I roll with Evan’s calculations, but with slight modification. Instead of calculating correlation coefficients, I instead compute rolling regression coefficients. I look at week to week changes in inflation breakevens, the 10 year TIPS yield, and the SP500. For each week I compute regressions of percent changes in the SP500 against percentage point changes in the TIPS yield and inflation breakevens for the past 26 week window. The regression slopes measure the response of the SP500 to either interest rate changes or expected inflation. It corresponds loosely to the correlation coefficient Evan calculates. The regression intercept measures the “intrinsic” trend growth of the SP500, independent of interest rates or inflation. By looking at these coefficients in context, I will try and construct a more holistic vision of what the financial markets are trying to say.



First, let us take a look at the right side panels which describe the responsiveness of the SP500 to expected inflation. In some sense, changes in the SP500 represent changes in expected future nominal GDP. Therefore, when we look at the relationship between inflation expectations and the SP500, this serves as a proxy for the relationship between inflation and nominal GDP.

In my view, the spike in the TIPS breakeven intercept is a smoking gun for a positive aggregate supply shock. Think about what the higher intercept means. The regression is of changes in the SP500 against changes in the TIPS breakeven. Therefore an increase in the intercept means that the SP500 grows faster for every level of expected inflation. This effect is quantitatively important as well. In comparison to the 6 months ending 2012, the intercept for the past 6 months suggests that the SP500 has kicked it up from about 0% weekly trend growth that is independent of inflation expectations to about 0.8%. Meanwhile, the slope for the breakeven-SP500 relationship is still positive. This all suggests a more permanent aggregate supply shock is driving the intercept up, whereas day to day aggregate demand shocks keep the slope positive. A diagram of this is shown below.

However, careful readers will note that you can get “more output at every price” from a story with a structural shift in aggregate demand with marginal shocks coming from aggregate supply. However, this hypothesis fails on two counts. First, if marginal changes in inflation reflected changes in aggregate supply, not demand, then because aggregate supply shocks send prices in the opposite direction of output, we should expect the TIPS breakeven slope to be negative. Second, the AD story does not match up with the changes in levels. As I showed above, inflation expectations have fallen while the SP500 has risen. If there were a large aggregate demand shock, then we should have seen both the SP500 and TIPS breakeven rise in levels. Therefore, a positive aggregate supply shock provides the most natural interpretation for the right hand panels.

Now comes the out of sample test. Can an aggregate supply shock explain the low slope and moderately higher intercept in the SP500-real rate relation? Absolutely.

To see how, I appeal to a version of the IS-MP (Investment Savings, Monetary Policy) model, pictured below. In the diagram, nominal GDP growth is on the x-axis and the real interest rate is on the y-axis. The IS curve is the standard IS curve from intro macro. It describes various combinations of interest rates and nominal GDP levels that give equilibrium in the goods market. At lower levels of the real interest rate, people want to hold onto less money and consume more goods. This results in higher levels of nominal GDP and a downward sloping curve. The MP curve is slightly different because it describes not equilibria but a central bank reaction function. At higher levels of nominal GDP, the Fed sets higher a higher interest rate in order to prevent rapid inflation. These two curves now give a unique equilibrium characterized by an interest rate and a level of nominal GDP.

Now what happens if there is a supply shock? The increased productive capacity, on first approximation, has no effect on the IS curve. To see why, suppose the monetary authority does not react. Then because a supply shock leaves nominal GDP relatively unchanged, then the IS curve should not move. However, because the Federal Reserve is an inflation targeting central bank, the MP curve shifts down. Now that every unit of nominal GDP consists of more real growth and less inflation, monetary policy becomes easier. Therefore, the new monetary policy curve will look something like MP(2) in the picture above.

This matches two more details from the regression.

First, the downward shift of the MP curve means that at every interest rate you observe more output. This matches the somewhat higher regression intercept on the interest rate graph.



Second, since the MP curve is moving, we should expect a weaker correlation between interest rates and output. This is illustrated above. If the MP curve is held constant while the IS curve shifts back and forth, then we will observe a strong correlation between interest rates and output, as shown by the blue line. On the other hand, if the MP curve is moving to MP(2) at the same time, we may end up observing the red dots and finding that the correlation drops. We also should expect this correlation confusion to be a bigger deal for the monetary policy shift than for the aggregate supply shift. As Bernanke is finding out, shifts in MP are linked to relatively unstable market expectations whereas a positive AS shock from something like oil discoveries is much more predictable. The theory behind this explanation of the fall in the correlation is illustrated in the sketch below, and it is actually exactly what we observe in the markets during the first half of this year.

The final step is to get the higher interest rate from the taper, and this can be seen as just the effect of a slight Fed tightening along with a slight rightward shift of the IS curve as business confidence requires. In the end, you have higher growth, higher rates, even though the Fed has tightened (as per the Dealer survey) relative to where it was before.

Act 3 -- Addressing Additional Concerns

While I believe the above story is the one most consistent with the regression data, there are always additional concerns.

Most importantly: what is the positive supply shock? I believe the most plausible supply shock could be the further discovery and development of unconventional oil and gas reserves. Therefore when compared to the counterfactual of perpetually rising oil prices, the new discoveries makes it easier for policy makers to respond to energy shocks and improve the economy’s productive capacity.

An important note is that a rise in oil prices, when it occurs alongside a rising SP500, does not contradict the aggregate supply hypothesis. An aggregate supply shock is characterized by a general fall in inflation as output rises. But if aggregate demand is moving at the same time, we could end up observing higher prices with even higher output. Therefore we identify an aggregate supply shock by seeing higher output *for any given level of inflation*. And this is precisely what we see from the rolling regressions.

Also, I have somewhat of a harder time explaining the past movements in the intercepts and slopes. Fortunately, the intercepts seem to move up and down together, whereas the slopes do the same. Moreover, the intercepts often go in opposite directions when compared to the slopes. This suggests that supply shocks may be more recurrent than we are led to believe.

Others may criticize the above approach as too ad-hoc. While to some extent, it certainly is, I believe I have done justice to the spirit of the AS/AD and IS/MP models. Furthermore, if you break down all the layers of abstraction and ad-hoc econometrics, the story is quite simple:

The massive increase in U.S. petroleum resources has expanded aggregate supply, allowing the economy to attain higher levels of output at every level of inflation. This serves as a massive tailwind for equity markets that no longer depend on aggregate demand inflation to grow. This requires a muddled monetary policy adjustment -- reducing the previously observed correlation between interest rates and growth. Nonetheless, the aggregate supply shock has increased trend growth, making the fluctuations in interest rates matter less.

Fin.