Monday, August 19, 2013

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.


  1. Excellent post.

    But I have come to nitpick.

    Your government expenditures measures are extremely different concepts. The US measure is evidently "total government expenditures" which is a budget measure. The eurozone measure is evidently "final consumption expenditure of general government" which is an SNA measure.

    Eurostat has seasonally adjusted "total general government expenditures" in the "quarterly non-financial accounts for general government" which would be the equivalent of the US measure you have used. It also has "final consumption expenditure of general government" for the US which would be the equivalent of the eurozone measure you have used.

    Be forewarned that while "final consumption expenditure of general government" (SNA) and "government consumption and gross investment" (NIPA) are similar concepts, they are not the same. The NIPA measure includes government investment and the SNA measure does not. It is possible to create the NIPA equivalent measure from eurostat data (I've done it) but it is probably far more trouble than it is worth since it is requires adding together several different components and seasonally adjusting it.

  2. Mark,

    I welcome nitpicking, especially yours. Thanks for the info on the data series. I'll be more careful next time. David Beckworth also made some suggestions regarding using the CAPB instead, so I've updated the post to take that into account.

  3. The YoY NGDP growth for 2013 in the United States appears to be close to 4 percent. How are you calculating that? Shouldn't it be closer to 3 percent?

    1. I think I did something silly with an average. I'll be sure to fix it when I get to work tomorrow.

  4. good post, all very reasonable.. i like that first graph, do you have R code on github for that? or which graph package/settings did you use for that?

  5. So going by your definition of stance of monetary policy, are you saying monetary policy in the US is currently easy? (since NGDP is less than its trend). If yes, then why does Scott Sumner always say monetary policy is currently tight?

    Further, can you elaborate little more on “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”

    1. 1) My ability to let typos through is unparalleled. If NGDP > Trend, too easy, NGDP < Trend too tight, therefore monetary policy right now is too tight.

      2) Interest rates represent the demand for credit, not necessarily for money. So if monetary policy is too tight, that triggers a recession. This makes the demand for credit fall and we observe low interest rates. The converse is true if the Fed is too easy.

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