The Monetary Base and the Channels of Monetary Transmission in the Age of ZIRP: Conclusion

A Mark Sadowski post

  1. The Object of This Exercise

One point of this series of posts (see list at the end) was to show that during the US age of zero interest rate policy (ZIRP) from December 2008 through May 2015, using nothing more complicated than a simple conventional Vector Auto-Regression (VAR) model with a minimum of structure, the monetary base has had statistically significant effects on the goods and services markets through broad spectrum of financial market variables. The other is that we now have a baseline VAR model that can be used for further policy analysis, and which can be improved in the future through the use of different impulse response identification structures, or perhaps more advanced estimation methods.

  1. Why the Monetary Base?

The monetary base is unique among monetary aggregates in that the central bank has the ability to decide precisely how large it will be at any given moment. Even the amount of bank reserves, which along with currency in circulation is a component of the monetary base, cannot be determined with any precision by the central bank, since it is a residual of the amount of currency in circulation, which is itself determined by the depositors’ desire to hold currency. The only other variable which the central bank can arguably target with such precision is the overnight interbank lending rate, which is itself largely determined by the size of the monetary base through the conduct of open market operations.

Thus, if one is interested in studying the effects of monetary policy at the zero lower bound in short term interest rates, the monetary base is the logical variable to represent the instrument of monetary policy. The fact that, away from the zero lower bound, the usual instrument of monetary policy, namely the overnight interbank lending rate, is itself essentially determined by the size of the monetary base, means that this change in instrument during the age of zero interest rate policy (ZIRP) is less meaningful than many may realize.

And, it is worth noting, any monetary economics model that uses as its primary monetary variable one which cannot be easily controlled by the central bank is, quite simply, useless for studying the effects of monetary policy.

  1. The Importance of Proper Model Specification

In each of these posts I have discussed in detail the types of diagnostics that I have conducted in fitting the model to the data. The reason for this is two-fold.

One is that I wanted these posts, as technical as they may at times seem to be, to be highly accessible. That is, I want these results to be easily reproducible by anyone who has the time series econometric background and access to commonly used econometric software packages such as EViews, Stata, SAS, R, Gretl, etc. If I am doing anything at all novel here, it is to as much as possible enable others to verify these results for themselves.

Second, without proper model specification, the obtained results almost certainly will be statistically biased, meaning that the reported statistical significance of the results, if any, will be highly questionable.

And if a time series model’s results are reported without routine references their statistical significance, then they should always be viewed with deep skepticism.

  1. Other Channels of Monetary Transmission

The three most important channels of money transmission that I didn’t directly address in this series of posts are 1) the Traditional Real Interest Rate Effects Channel, 2) the excess bank reserve channel of the Bank Lending Channel, and 3) the Cash Flow Channel.

According to the Traditional Real Interest Rate Effects Channel, reductions in the expected long-term real interest rate lead to increased expenditures on physical investment and durable goods. My own estimates indicate that during the age of zero interest rate policy (ZIRP) there is no correlation between expected long-term real interest rates (as measured by the 10-Year Treasury Inflation-Indexed Security) and industrial production or the price level. Moreover there’s no correlation between the monetary base and expected long-term real interest rates.

According to the excess bank reserves channel of the Bank Lending Channel, a rise in excess reserves leads to an increase in bank lending. My own estimates indicate that there is in fact a correlation between excess bank reserves and bank credit, but that this correlation is hard to disentangle from the other channels through which changes in the monetary basis appear to be influence bank credit, namely the balance sheet channel and the household liquidity effects channel. Moreover, I am skeptical that an increase in bank reserves would have much of a marginal effect on the amount of bank credit. In any case, regardless of how changes to the monetary base are influencing the amount of bank credit, the effect is statistically significant. The more important problem is of course that the level of bank credit does not have a statistically significant effect on output or prices.

According to the Cash Flow Channel, reductions in nominal interest rates lead to increase the liquidity of debtor households and firms at the expense of creditor households and firms. The interest rate that is most representative of this effect is the 10-Year Treasury Security yield. As we saw in the post on the Bond Yield Channel, positive shocks to the monetary base lead to increases in the 10-Year Treasury Security yield, the opposite of what the effect of expansionary monetary policy is theorized to be under the Cash Flow Channel. Furthermore, increases in the 10-Year Treasury Security yield lead to increases in industrial production, the opposite effect of what is implicitly theorized under the Cash Flow Channel.

Is there reason to believe that these three channels of monetary transmission work away from the zero lower bound in short term interest rates? The short answer is no.

Many researchers, including Bernanke and Gertler (1995), believe that empirical evidence does not support strong interest rate effects operating through the cost of physical capital, as is theorized under the Traditional Real Interest Rate Effects Channel. My own estimates from during times when the US economy has been away from the zero lower bound in short-term interest rates show that while there is a correlation between the ex-post real (i.e. adjusted by the year on year PCEPI) 10-Year Treasury Security yield and private nonresidential investment and private residential investment, there is no correlation with durable goods spending. More importantly, I find that higher real interest rates lead to higher physical investment spending, not lower. That only really makes sense in a model that acknowledges the role of money in determining interest rates.

As for the excess bank reserves channel, several studies have shown that during times when the central bank is targeting the overnight interbank interest rate as its instrument of monetary policy, bank credit usually Granger causes the monetary base. This is often misinterpreted by endogenous money enthusiasts as meaning that the actions of the central bank are constrained by the demand for bank credit. The reality is that when the central bank targets an interest rate, the level of bank credit and the monetary base (and practically everything in the economy for that matter) are endogenous to the central bank’s interest rate target. The reason why I bring this up however is that away from the zero lower bound in short term interest rates there is little reason to believe that it is the level of excess bank reserves that is determining the level of bank credit. Rather, it is the central bank’s interest rate target that is ultimately determining the level of bank credit.

And finally, as weak as the empirical evidence is for the theorized workings of the Cash Flow Channel during the age of ZIRP, it is even weaker during the times when the US economy has been away from the zero lower bound in short-term interest rates. Anyone who has taken note of the yield curve’s obvious predictive power for business cycles knows that the correlation between nominal long term-rates and aggregate nominal spending must be the opposite of what is theorized under the Cash Flow Channel.

  1. Whither the Role of Interest Rates?

In most of these posts I was able to generate statistically significant monetary policy effects without any reference at all to an interest rate as the instrument of monetary policy. The only exception was in the posts on the Exchange Rate Channel where I found it necessary to include the overnight interbank interest rate owing to the fact that most major US trading partners have been targeting that rate as the instrument of monetary policy during the age of US ZIRP, and the real exchange rate is determined not only by the monetary policy of the domestic country, but also by the foreign trade partner.

However, as we have just discussed, the empirical evidence does not support the Traditional Real Interest Rate Channel, and the empirical evidence flatly contradicts the supposed workings of the Cash Flow Channel, and these two are the only other monetary transmission channels that rely at all on interest rates. Thus we are left with the realization that interest rates are only important to the extent that they are targeted by the central bank, and even then their importance seems to be primarily stemming from what it indicates about what is happening to the monetary base.

  1. Does the Liquidity Trap Exist?

Modern applied macroeconomic models of the liquidity trap usually rely on some version of the David Romer’s ISLM/ISMP model.

If expected short-term real interest rates cannot be lowered to a level prescribed by some version of a Taylor Rule, the only way to increase real output is by increasing inflation expectations. This is because the central bank can only determine the level of real output through changes in the expected short-term real interest rate.

But as we have seen here there is really is no monetary transmission channel that works primarily, much less exclusively, through expected short-term interest rates. Thus any model that relies on the supposed inability of the central bank to lower expected short-term interest rates to demonstrate the ineffectiveness of monetary policy is guilty of assuming its conclusion.

Central banks usually target short term interest rates as an instrument of monetary policy not because there is any credible mechanism by which they directly and significantly impact the economy, but because 1) they are quickly and accurately measurable, 2) the central bank can exercise a great deal of control over them, and 3) because changes in them usually lead to reasonably predictable outcomes in terms of policy goals. These qualities may make short term interest rates convenient as an instrument of monetary policy away from the zero lower bound, but they should in no way cause us to confuse short-term interest rates for the actual mechanisms by which monetary policy is transmitted.

  1. Are There Other Reasons to Believe in the Liquidity Trap?

The most popular way to “prove” the existence of the liquidity trap is to simply point at a graph of the monetary base of a country in ZIRP (and/or the velocity of the monetary base) and then profoundly intone, “see?” But all this really shows is that the velocity of the monetary base is a variable, something which every applied macroeconomist already knows.

And an argument consisting of little more than a line graph depicting the time series of values of a quantity should never be accepted as a proof of anything, except that the person who is arguing that it proves something is not familiar with what constitutes acceptable empirical evidence, is incapable of understanding what constitutes acceptable empirical evidence, or is simply willfully ignoring what constitutes acceptable empirical evidence because it contradicts their preferred model.

The series of posts:1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,

 

19 thoughts on “The Monetary Base and the Channels of Monetary Transmission in the Age of ZIRP: Conclusion

  1. Mark: I have read this post, but I have not read the others. My apologies for asking two questions that your previous posts have probably answered:
    1. How big is the effect? (Or, to ask about the reciprocal: in order to get a 1% increase in NGDP, how big a % increase in MB do we need, at the ZLB?)
    2. Which of the possible “channels” (I’m skeptical of “channels”) look most plausible/important?

    • Mark, I have a slight modification on Nick’s first question: in order to get a 1% increase in NGDP, how big a % increase in MB do we need, when NOT at the ZLB?

      • Tom,
        That’s not an easy question to answer mainly because away from the zero lower bound the Fed hasn’t been setting targets for the monetary base, and consequently I haven’t bothered to estimate such a model. Reasonably reliable estimates of the effect of a change in the fed funds target on RGDP and the price level do exist, although offhand I don’t honestly don’t know what they are.

        The main difference between being at the ZLB, and being off it, is that short term interest rates have been fixed while on it, which is really what is making statistically significant estimations of the effects of monetary base shocks even possible.

      • Mark, thanks for your reply. Are you saying it’s not easy, but possible? Your first paragraph gives that impression, but I’m not sure if you’re then ruling that out in the 2nd. What’s the distinction between a change and a shock? Thanks.

      • A “shock” just means a one-period one-standard-deviation change.

        I’m not ruling out measuring the effect of the monetary base during times when the federal funds rate is the monetary policy instrument is the federal funds rate, but I don’t think it really makes much sense. To coin an analogy it’s like asking how fast you can swim while walking on land.

      • Mark, thanks for the reply. Regarding this part of your response:

        “…times when the federal funds rate is the monetary policy instrument”

        Why does a variable being the target of policy (or not) affect our investigation of the possibility that this variable is causing another variable?

        For example, I used to fly sailplanes (gliders). We’d tape a piece of yarn to the canopy right in front of the pilot to give the pilot an indication of the angle of the glider wrt the direction of travel through the air. Now if I had a time series indicating the angle of the rudder and another indicating the angle of that yarn wrt the glider’s center line, I could investigate if the rudder angle was causing the yarn angle. This would be the case regardless if the rudder angles themselves were used as a series of targets or if that series of rudder angles was used to accomplish some other target (such as flying directly along some path on the ground like a runway or a highway). Where does my analogy go off the rails? After all, in order for the CB to execute a federal funds rate target, it often engages in OMOs doesn’t it? Aren’t those OMOs analogous to the series of intermediate rudder angles I’d use to maintain my ground path target in the glider?

        BTW, in the glider the only time it’s desirable to have that yarn at any angle other than 0 degrees is when landing during a cross wind, in which case you want to fly through the air “crooked” so that the landing gear is in line with the direction of travel of the glider wrt the ground.

      • Regarding my yarn analogy, here’s a visual.

        It’s temping (for me) to torture the analogy further … perhaps I should just suck it up and take a (your?) statistics course. Here’s my further torture: all that bit with the glider assumes that the ailerons are not used (angle = 0). But in reality the ailerons are typically used in conjunction with the rudder (the yarn (“yaw string”) lets you know if you’re coordinating rudder and ailerons correctly), which could make teasing out causation in the terms I described above (recording rudder angles but not aileron angles) more difficult: you’d have this unobserved variable (ailerons) dancing around, potentially completely screwing up your ability to detect any causation from rudder to yarn. Can these Granger tests be used to detect if two or more independent variables are causing a third?

        Then of course there’s the elevator… it could (conceivably) be used to perfectly stall (bring airspeed to zero) the glider, making the yarn angle nothing but noise.

        OK, forgive me, but this just occurred to me: one time a visiting pilot forgot to connect the elevator control rod. He could push the elevator up (and thus increase the angle of attack of the glider: i.e. he push the nose up), but he had to rely on gravity (fighting air resistance) to bring the elevator back down again. Once he realized this he undid his harness and sat as far forward as possible, biasing the nose of the glider downwards… so that he could counteract this with up elevator to fly the thing back to the airport and land (yes, he survived). The Keynesian liquidity trap model seems like this to me: we can’t scoot forward far enough. We can push the nose up further, slowing the glider down and risking a stall (i.e. make money tighter), but we can’t very well force the nose down to pick up airspeed (looser money doesn’t help much). It’s “one sided” control. Haha… OK, I apologize again for my “flight of fancy” here. Still, I’m dreaming of time series data to detect if such a state of affairs exists: what’s required to detect when we haven’t scooted forward far enough (or alternatively to detect when the control rod can push but not pull)?

      • Tom,
        I’m almost sorry I resorted to an analogy now.🙂

        The danger with analogies is they are rarely perfect and sometimes people may have a hard time following them (like I am having trouble following yours right now).

        “Why does a variable being the target of policy (or not) affect our investigation of the possibility that this variable is causing another variable?”

        I think we once talked about what Granger causality tests show about the monetary base during times when the fed funds rate is the instrument of monetary policy. In particular, they show that bank credit Granger causes the monetary base, whereas right now the monetary base Granger causes bank credit. This is largely symptomatic of the fact that when the fed funds rate is the instrument of monetary policy the monetary base reacts passively to changes in the fed funds rate target.

        “Can these Granger tests be used to detect if two or more independent variables are causing a third?”

        Yes, provided you have identified all of the variables involved. In particular, a valid criticism of Granger causality tests is that tests may show one variable Granger causes another variable when in fact a third variable is causing the other two.

        “The Keynesian liquidity trap model seems like this to me: we can’t scoot forward far enough. We can push the nose up further, slowing the glider down and risking a stall (i.e. make money tighter), but we can’t very well force the nose down to pick up airspeed (looser money doesn’t help much). It’s “one sided” control.”

        Or like pushing on a string. Except that the string is really like one of those thick climbing ropes, and you have a widow’s cruse that produces an infinite amount of thick climbing rope at no economic cost. (Oops, I think I coined another analogy, and mixed metaphors at the same time!)

        Seriously though, the statistical tests show that QE has statistically significant macroeconomic effects, so it’s definitely not “one sided”. It’s just that whenever you increase the monetary base the velocity of the monetary base drops. (This is of course also true when the central bank is targeting the overnight interbank interest rate.) You just need to do enough of it.

    • Nick,
      Your first question is one I half expected someone to ask after the third post and was really astonished when nobody did. The problem is that I’m using industrial production and PCEPI as proxies for RGDP and the GDP deflator respectively. Based on the three variable baseline VAR discussed in post #3, if one sums the responses to get a rough estimate of “nominal output”, then a 2.4% positive shock to the monetary base in month one leads to a maximum increase in nominal output of about 0.25% in month 10. However, given that industrial production is more variable than RGDP the actual response to NGDP is probably somewhat less than that.

      As for the channels, I almost linked to your posts on the “people of the concrete steppes” and the “upward sloping IS curve” but decided against it mostly because I was trying to keep my explanations as short as possible. In a sense my discussion of channels is really for those people, as I sense they really need to “see” some kind of mechanism by which things get transmitted.

      Empirically the inflation expectations, bond yield, stock price, and exchange rate seem to be the most important channels in that order. The inflation expectations channel is capturing something much more fundamental than the rest, and the exchange rate channel is probably less important to the US than it would be to other countries because the US is the fourth least open country to trade apart from Argentina, Brazil and Sudan.

      Incidentally, you might find post 8, where I discuss the real exchange rate of the Canadian dollar in terms of the US dollar, of interest to you as a Canadian.

      • Thanks Mark.
        So, basically a 10% increase in MB leads to a bit less than 1% increase in NGDP. Not bad. Especially since everyone knows it’s mostly temporary.

      • So, in 2013, by increasing the monetary base by 40%, the Fed roughly added 4% to NGDP growth? Did I get that right?

        And if the Fed had continued QE3 (adding 2% per month to the base), NGDP growth would have been about 2.4% faster (on an annualized basis) than in the last year (say 6% instead of 3.6%)? If so, it seems that the Fed really could have ended the recession if only they had held on to QE3 for an extra year.

      • @Nick,
        It might be interesting to compare this estimate with similar ones for other zero lower bound episodes (if and when I get around to it).

        @LK Béland,
        Yes, that sounds about right. And yes, I totally agree, it looks like the Fed ended QE3 a little prematurely.

  2. Great post with news that needs to be used.🙂
    I hope you don’t mind if I reference it in some future post, with all due credit of course.
    I noticed that Marcus hasn’t posted in a while. I hope he is doing well.

  3. Well, I am chastened by the concluding statement, since I am one that looks at many a graph and draws conclusions.
    But excellent series of posts, eompelling and Sadowski sets a new standard for presenting why he came to a conclusion. No obfuscation here.
    And as I always say, print more money.

    • Benjamin,
      Your arguments consist of considerably more than just pointing at a graph and saying “see?” I can find dozens of examples of what I am thinking, and in contrast to you, they are all used to argue “don’t bother printing more money.”

  4. Mark, I’ll try to get to your remaining posts soon since it took a lot just to get through the first six—its had to keep up with you!.

    Nick, the 10%/1% ratio is interesting, but I am also interested (see my comment to post 6) in the impact of Expected vs Unexpected changes in the MB (and I suppose perceived-permanent vs. perceived-temporary). Mark references some work by Romer and Romer and Bernanke and Kutner, however, those papers appear to define shocks as deviations from expected fed fund or discount rates, not MB quantities. This probably get’s into announcement effects, event studies etc.which is not the initial purpose of Mark’s work, but may be worth a couple of dummy variables in the VARs at some point.

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