The Monetary Base and the Exchange Rate Channel of Monetary Transmission in the Age of ZIRP: Part 1

A Mark Sadowski post

In this post we are going to add a measure of the value of the US dollar to the baseline VAR which I developed in these three posts; 1,2 and 3.

In particular, we are going to add the Real Trade Weighted U.S. Dollar Index: Broad (TWEXBPA).

The first thing I want to do is to demonstrate that the monetary base Granger causes the value of the dollar during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and TWEXBPA.

Sadowski GC7_1

The following analysis is performed using a technique developed by Toda and Yamamoto (1995).

Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for both series. I set up a two-equation VAR in the log levels of the data including an intercept for each equation.

Most information criteria suggest a maximum lag length of two. An LM test suggests that there is no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically stable, and the Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are cointegrated at this lag length. This suggests that there must be Granger causality in at least one direction between the monetary base and the value of the US dollar.

Then I re-estimated the level VAR with one extra lag of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 3, I left the intervals at 1 to 2 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.

Sadowski GC7_2

Thus I fail to reject the null hypothesis that the value of the US dollar does not Granger cause the monetary base, but I reject the null hypothesis that the monetary base does not Granger cause the value of the US dollar at the 1% significance level. In other words there is strong evidence that the monetary base Granger causes the value of the US dollar, but not the other way around.

Since the monetary base Granger causes the value of the US dollar, it should probably be added to our baseline VAR model. This is because, under these circumstances, we might expect shocks to the monetary base in the VAR model to lead to statistically significant changes in the value of the US dollar.

With the value of the US dollar added to the baseline VAR model, most information criteria suggest a maximum lag length of five. An LM test suggests that there is no problem with serial correlation at this lag length. An AR roots table shows the VAR to be dynamically stable.

The Johansen’s Trace Test indicates that there exists one cointegrating equation at this lag length, but the Maximum Eigenvalue Test does not show any signs of cointegration. In any case, this is expected, since we already have evidence that the monetary base is cointegrated with both industrial production and the value of the US dollar. This matter is addressed in greater detail in the three posts where the baseline VAR is developed.

I am using a recursive identification strategy (Cholesky decomposition), which is the dominant practice in the empirical literature on the transmission of monetary policy shocks. Such a strategy means that the order of the variables affects the results. For the four-variable VARs I am arranging the output level first, the price level second, the monetary policy instrument third, and the financial variable last in the vector. This ordering assumes that the Federal Open Market Committee (FOMC) sees the current output level and price level when it sets the policy instrument, and that the output level and price level respond to a policy shock with one lag, but that financial markets respond to a policy shock with no lag.

As before, the response standard errors I will show are analytic, since Monte Carlo standard errors change each time an Impulse Response Function (IRF) is generated. The variables are all multiplied by 100 to make the IRFs easier to read and interpret (as I have been doing throughout). Also, instead of estimating the model with LTWEXBPA, I am multiplying LTWEXBPA by negative one and terming the result LRERROWUS, which stands for “real exchange rate of the rest of world in terms of the US dollar”. In other words this represents the real value of the rest of the world’s currency in terms of US dollars. This will also make the IRFs easier to interpret. Here are the responses to the monetary base and to LRERROWUS in the four-variable VAR.

Sadowski GC7_3

The response of the value of foreign currency a positive shock to the monetary base is not statistically significant in any month. However a positive shock to the value of foreign currency in month one leads to a statistically significant positive response in the level of prices in months two through four. The IRFs show that a positive 0.75% shock to the value of foreign currency in month one leads to a peak increase in the price level of 0.10% in month twenty-four.

When the value of foreign currency goes up and, by extension, the value of the US dollar goes down, this raises the price of imported goods and services, and this is reflected in the aggregate price level.

What about the fact that a positive shock to the monetary base does not have a statistically significant effect on the value of foreign currency? Doesn’t that contradict the results of the Granger causality test?

Well no, not really. To see why, let’s re-estimate the VAR as a VAR in differences (a VARD). Remember there is a loss of statistical efficiency when one estimates a VAR in levels (a VARL). Most information criteria suggest a maximum lag length of three in the VARD. The LM test suggests that there no problem with serial correlation at this lag length. The AR roots table suggests that the VARD is dynamically stable at this lag length.

Here are the responses to the monetary base and to LRERROWUS in the four-variable VARD. I’ll restrict the time period to 10 months as it isn’t of interest after that point.

Sadowski GC7_4

A positive shock to the rate of change in the monetary base generates a statistically significant positive response to the rate of change in the value of foreign currency in the second month.

So why are positive shocks to the monetary base leading to statistically significant changes in the rate of change of the value of foreign currency, but not to the level of the value of foreign currency?

The value of foreign currency in terms of dollars is not only determined by US monetary policy, it is also determined by the conduct of monetary policy in other currency zones. Among the four studies that I mentioned in very beginning of this series of posts, only Honda et al. considered the effect of Quantitative Easing (QE) on the foreign exchange rate. They also did not find a statistically significant effect in levels. But they did not go any further than that.

A more sensible approach is to look at the effect of monetary policy on bilateral exchange rates. That way the monetary policy of the other currency zone can be incorporated into the model.

In Part 2 I am going to disaggregate LRERROWUS into separate currencies and enter them into the baseline VAR while including variables reflecting their individual monetary policies.

 

The Monetary Base and the Stock Price Channel of Monetary Transmission in the Age of ZIRP

A Mark Sadowski post

In this post we are going to add US stock market indices to the baseline VAR which I developed in these three posts. (1, 2, 3).

In particular, we are going to add the Dow Jones Industrial Average (DJIA) and the S&P 500 Index (SP500).

The first thing I want to do is to demonstrate that the monetary base Granger causes stock market indices during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and DJIA.

Sadowski GC6_1

And here is a graph of the natural log of SBASENS and SP500.

Sadowski GC6_2

The following analysis is performed using a technique developed by Toda and Yamamoto (1995).

Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for all three series. I set up two two-equation VARs in the log levels of the data including an intercept for each equation.

Most information criteria suggest a maximum lag length of two for the VAR that includes the Dow Jones Industrial Average as a variable. An LM test suggests that there is no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically stable, and the Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.

Most information criteria suggest a maximum lag length of five for the VAR that includes the S&P 500 Index as a variable. An LM test suggests that there is no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically stable at this lag length, and Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.

Then I re-estimated the two level VARs with one extra lag of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 3, I left the intervals at 1 to 2 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.

Sadowski GC6_3

Thus, the results are as follows:

  • I fail to reject the null that the Dow Jones Industrial Average does not Granger cause the monetary base, but I reject the null that the monetary base does not Granger cause the Dow Jones Industrial Average at the 1% significance level.
  • I fail to reject the null that the S&P 500 Index does not Granger cause the monetary base, but I reject the null that the monetary base does not Granger cause the S&P 500 Average at the 1% significance level.

In other words there is strong evidence that the monetary base Granger causes stock market indices, but not the other way around.

Since the monetary base Granger causes stock market indices they should probably be added to our baseline VAR model. This is because, under these circumstances, we might expect shocks to the monetary base in the VAR model to lead to statistically significant changes in the stock market indices.

With the Dow Jones Industrial Average added to the baseline VAR model, most information criteria suggest a maximum lag length of three. However, an LM test suggests that there is problem with serial correlation at this lag length. Increasing the lag length to four eliminates this problem. An AR roots table shows the VAR to be dynamically stable.

With the S&P 500 Index added to the baseline VAR model, most information criteria suggest a maximum lag length of three. However, an LM test suggests that there is problem with serial correlation at this lag length. Increasing the lag length to four eliminates this problem. An AR roots table shows the VAR to be dynamically stable.

The Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that there exists one cointegrating equation at this lag length in both VARs. But this is expected, since we already have evidence that the monetary base is cointegrated with industrial production. This matter is addressed in greater detail in the three posts where the baseline VAR is developed.

I am using a recursive identification strategy (Choleskey decomposition), which is the dominant practice in the empirical literature on the transmission of monetary policy shocks. Such a strategy means that the order of the variables affects the results. For the four-variable VARs I am arranging the output level first, the price level second, the monetary policy instrument third, and the financial variable last in the vector. This ordering assumes that the Federal Open Market Committee (FOMC) sees the current output level and price level when it sets the policy instrument, and that the output level and price level respond to a policy shock with one lag, but that financial markets respond to a policy shock with no lag.

As before, the response standard errors I will show are analytic, since Monte Carlo standard errors change each time an Impulse Response Function (IRF) is generated. Here are the responses to the monetary base and the stock market indices in the four-variable VARs.

Sadowski GC6_4

Sadowski GC6_5

The response of the Dow Jones Industrial Average to a positive shock to the monetary base is significantly positive from month one through month three. The instantaneous response of the S&P 500 Index is positive but statistically insignificant. This is followed by a statistically significant positive response in month two. Furthermore a positive shock to the Dow Jones Industrial Average in month one leads to a statistically significant positive response in the level of industrial production in months four through eight, and a positive shock to the S&P 500 Index in month one leads to a statistically significant positive response in the level of industrial production from months five through eight.

The IRFs show that a positive 2.3% shock to the monetary base in month one leads to a peak increase in the Dow Jones Industrial Average of 1.6% in month three. In turn, a positive 2.3% shock to the Dow Jones Industrial Average in month one leads to a peak increase in industrial production of 0.15% in month five.

The IRFs also show that a positive 2.2% shock to the monetary base in month one leads to a peak increase in the S&P 500 Index of 1.5% in month three. In turn, a positive 2.7% shock to the S&P 500 Index in month one leads to a peak increase in industrial production of 0.17% in month eight.

That Quantitative Easing (QE) raises stock prices is probably one of the least controversial claims about it. In fact, perhaps one of the most iconic images in the age of zero interest rate policy (ZIRP) is the series of graphs concerning the relationship between the S&P 500 Index and QE posted by Bill McBride of Calculated Risk, such as this one. (The references to “Operation Twist” are, if anything, a distraction.)

Sadowski GC6_6

Nominal stock prices have probably risen in response to positive shocks to the monetary base due to higher Nominal GDP (NGDP) expectations.

So the real question is why might higher stock prices lead to higher output?

James Tobin’s q theory provides one mechanism through which increased NGDP expectations may increase output through its effects on the prices of stocks. Tobin defines q as the market value of corporations divided by the replacement cost of their physical capital. If q is high the market price of corporations is high relative to the replacement cost of their physical capital, and new equipment and structures is cheap relative to the market value of corporations. Corporations can then issue stock and get a high price for it relative to the cost of the equipment and structures they are buying. Thus nominal investment spending will rise because corporations can purchase new equipment and structures with only a small issue of stock.

Franco Modigliani’s life-cycle theory of consumption provides another mechanism through which increased NGDP expectations may increase output through its effects on the prices of stocks. In the life-cycle model, consumption spending is determined by the expected resources of consumers, which are made up of human capital, physical capital and financial wealth. A major component of financial wealth is the holdings of stock shares. When stock prices increase, the value of financial wealth increases, thus increasing the expected resources of consumers, and nominal consumption spending rises.

The bottom line is, in the Age of ZIRP, positive shocks to the monetary have probably raised NGDP expectations, which has raised stock prices, which has increased nominal investment and consumption spending, which has raised real output.

Next time I shall add a measure of the value of the dollar to the baseline VAR. How has QE affected the value of the dollar, and how have changes in the value of the dollar impacted the economy?

Tune in next time and find out.

 

 

 

What Central Bankers Have Forgotten: Voters Like Prosperous Free Markets

A Benjamin Cole post

As a lot, central bankers are not entrepreneurs or real estate developers, and are very risk-averse, and are minutely concerned with the strict control of prices (as measured) as opposed to robust prosperity.

And maybe not even: Most central bank staffers are in sinecures, paid on a seniority basis, and will get nominal pay raises on a schedule. For central bankers, prosperity equals deflation, even better if coupled with recession.

In short, central bankers are divorced from the economies over which they have such influence.

Voters Not Divorced

But most voters live and breathe in the real economy. They know when job markets are tight or loose, when there are boom times, or when the economy is dead.

It is indisputable that in economic downtimes, voters do not embrace free enterprise. In the Great Depression, the U.S. federal government ballooned, with voter encouragement. The last Great Recession in the U.S. helped usher in Obamacare.

If policymakers and central bankers want voters to hail capitalism and free enterprise, they need to create boom times in Fat City.  Shoot for tight labor markets, and lots of sniveling and whimpering about labor shortages. Then voters will love free enterprise.

And what would the United States look like if there were chronic labor shortages? How would lefties justify more and more welfare?

Gee, would that be such a bad outcome?

I mean, for anybody but central bankers?

The Monetary Base and the Bond Yield Channel of Monetary Transmission in the Age of ZIRP

A Mark Sadowski post

In this post we are going to add US bond yields to the baseline VAR which I developed in these three posts (here, here & here).

In particular, we are going to add the yield of 10-Year Treasury Constant Maturity Securities (GS10) and yield of Moody’s Seasoned Aaa Corporate Bonds (AAA).

The first thing I want to do is to demonstrate that the monetary base Granger causes bond yields during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and of GS10 measured in percent.

Sadowski GC5_1

And here is a graph of the natural log of SBASENS and of AAA measured in percent

Sadowski GC5_2

The following analysis is performed using a technique developed by Toda and Yamamoto (1995).

Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for all three series. I set up two two-equation VARs in the log levels of the data including an intercept for each equation.

Most information criteria suggest a maximum lag length of two for the VAR that includes 10-year Treasury Bond yields as a variable. An LM test suggests that there is no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically stable, and the Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.

Most information criteria suggest a maximum lag length of five for the VAR that includes Aaa Corporate Bond yields as a variable. The LM test suggests that there is a problem with serial correlation, but this problem disappears when the lag length is increased to six. The AR roots table suggests that the VAR is dynamically stable at this lag length, and Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are not cointegrated at this lag length.

Then I re-estimated the two level VARs with one extra lag of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 3 and 1 to 7 respectively, I left the intervals at 1 to 2 and 1 to 6 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.

Sadowski GC5_3

Thus, the results are as follows:

  • I fail to reject the null that 10-year Treasury Bond yields does not Granger cause the monetary base, but I reject the null that the monetary base does not Granger cause 10-year Treasury Bond yields at the 5% significance level.
  • I fail to reject the null that Aaa Corporate Bond yields does not Granger cause the monetary base, but I reject the null that the monetary base does not Granger cause Aaa Corporate Bond yields at the 1% significance level.

In other words there is strong evidence that the monetary base Granger causes bond yields, but not the other way around.

Since the monetary base Granger causes bond yields they should probably be added to our baseline VAR model. This is because, under these circumstances, we might expect shocks to the monetary base in the VAR model to lead to statistically significant changes in bond yields.

With 10-year Treasury Bond yields added to the baseline VAR model, most information criteria suggest a maximum lag length of three. However, an LM test suggests that there is problem with serial correlation at this lag length. Increasing the lag length to four eliminates this problem. An AR roots table shows the VAR to be dynamically stable.

With Aaa Corporate Bond yields added to the baseline VAR model, most information criteria suggest a maximum lag length of five. An LM test suggests that there is no problem with serial correlation at this lag length. An AR roots table shows the VAR to be dynamically stable.

The Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that there exists one cointegrating equation at this lag length in both VARs. But this is expected, since we already have evidence that the monetary base is cointegrated with industrial production. This matter is addressed in greater detail in the three posts where the baseline VAR is developed.

I am using a recursive identification strategy (Cholesky decomposition), which is the dominant practice in the empirical literature on the transmission of monetary policy shocks. Such a strategy means that the order of the variables affects the results. For the four-variable VARs I am arranging the output level first, the price level second, the monetary policy instrument third, and the financial variable last in the vector. This ordering assumes that the Federal Open Market Committee (FOMC) sees the current output level and price level when it sets the policy instrument, and that the output level and price level respond to a policy shock with one lag, but that financial markets respond to a policy shock with no lag.

As before, the response standard errors I will show are analytic, since Monte Carlo standard errors change each time an Impulse Response Function (IRF) is generated. Here are the responses to the monetary base and bond yields in the four-variable VARs.

Sadowski GC5_4

Sadowski GC5_5

The instantaneous response of bond yields to a positive shock to the monetary base is positive, but statistically insignificant in both cases. This is followed by a statistically significant positive response in the third and fourth month. Furthermore a positive shock to 10-year Treasury Bond yields in month one leads to a statistically significant positive response in the level of industrial production in months three and four, and a positive shock to Aaa Corporate Bond yields in month one leads to a statistically significant positive response in the level of industrial production from months three through five.

The IRFs show that a positive 2.1% shock to the monetary base in month one leads to a peak increase in 10-year Treasury Bond yields of 0.13 percentage points in month four. In turn, a positive 0.17 percentage point shock to 10-year Treasury Bond yields in month one leads to a peak increase in industrial production of 0.19% in month ten.

The IRFs also show that a positive 2.2% shock to the monetary base in month one leads to a peak increase in Aaa Corporate Bond yields of 0.097 percentage points in month four. In turn, a positive 0.13 percentage point shock to Aaa Corporate Bond yields in month one leads to a peak increase in industrial production of 0.21% in month thirteen.

So Quantitative Easing (QE) raises 10-year Treasury Bond and Aaa Corporate Bond yields?!?

Yes, in fact Michael Darda has repeatedly shown that long-term Treasury yields have generally risen under QE programs in figures such as the following.

Sadowski GC5_6

Confusingly, this runs counter to the stated objectives of the Federal Reserve’s Large Scale Asset Purchase Program (LSAP).

If the Federal Reserve purchases 10-year Treasury Bonds in large quantities that should drive the price of 10-year Treasury Bonds up and their yields down, right? Wrong!

That would be true if households or firms did that. But when the Federal Reserve increases the monetary base to purchase 10-year Treasury Bonds it increases expected Nominal GDP (NGDP), or expected aggregate demand (AD), and higher expected AD means higher inflation expectations, ceteris paribus.

Why might an increase in inflation expectations lead to an increase in bond yields? Well one reason would be that an increase in inflation expectations lowers the expected return for bonds causing the demand for bonds to decline and their demand curves to shift to the left.

This is almost certainly true in the case of 10-year Treasury Bonds, since it is unlikely that their supply would increase endogenously to an increase in expected NGDP. Rather, as NGDP increases it is more likely that the quantity supplied of 10-year Treasury Bonds will decrease, as Federal tax revenues increase, and Federal spending on social insurance, such as unemployment compensation, decreases, resulting in a decrease in the Federal deficit. Thus whatever increased demand that the Federal Reserve created for 10-year Treasury Bonds through its LSAPs was almost certainly more than counterbalanced by decreased demand by households and firms due to increased inflation expectations.

Moreover, there is another way that increased inflation expectations may lead to an increase in bond yields. For a given interest rate, when inflation expectations increases, the expected real borrowing cost falls, hence the quantity of corporate bonds supplied increases at any given bond price and interest rate. Thus an increase in inflation expectations causes the supply of corporate bonds to increase and the supply curve to shift to the right.

Furthermore, an increase in NGDP expectations may lead to an increase in the expected profitability of physical investment opportunities. The more profitable equipment and structure investments that a corporation expects it can make, the more willing it will be to issue bonds in order to finance those investments. Thus an increase in expected NGDP may cause the supply of corporate bonds to increase and the supply curve to shift to the right, resulting in a decrease in the price of bonds and an increase in their yields.

And, pointedly, increased nominal spending on equipment and structures probably means increased real output.

Next time I shall add domestic stock market indices to the baseline VAR. I suspect that the results will be less controversial than the results on long term interest rates probably are, but who knows?

The Monetary Base and the Inflation Expectations Channel of Monetary Transmission in the Age of ZIRP

A Mark Sadowski post

In this post we are going to add US inflation expectations as measured by the difference between the yield of 5-Year Treasury Constant Maturity Securities (GS5) and the yield of 5-Year Treasury Inflation-Indexed Constant Maturity Securities (FII5) to the baseline VAR which I developed in my last three posts.

This is often referred to as the 5-Year Breakeven Inflation Rate (T5YIEM).

The first thing I want to do is to demonstrate that the monetary base Granger causes inflation expectations during the period from December 2008 through May 2015. Here is a graph of the natural log of SBASENS and of T5YIEM measured in percent.

Sadowski GC4_1

The following analysis is performed using a technique developed by Toda and Yamamoto (1995).

Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for both series. I set up a two-equation VAR in the log level of SBASENS and T5YIEM in percent including an intercept for each equation.

Most information criteria suggest a maximum lag length of two. The LM test suggests that there is a no problem with serial correlation at this lag length. The AR roots table suggests that the VAR is dynamically stable at this lag length, and Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that the two series are cointegrated at this lag length. This suggests that there must be Granger causality in at least one direction between the monetary base and inflation expectations.

Then I re-estimated the level VAR with one extra lag of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 3, I left the interval at 1 to 2 and declared the lag of each variable to be exogenous variables. Here are the Granger causality test results.

Sadowski GC4_2

Thus I fail to reject the null hypothesis that inflation expectations does not Granger cause the monetary base, but I reject the null hypothesis that the monetary base does not Granger cause inflation expectations at the 1% significance level.  In other words there is strong evidence that the monetary base Granger causes inflation expectations, but not the other way around.

Since the monetary base Granger causes inflation expectations it should probably be added to our baseline VAR model. This is because, under these circumstances, we might expect shocks to the monetary base in the VAR model to lead to statistically significant changes in inflation expectations.

With inflation expectations added to the baseline VAR model, most information criteria suggest a maximum lag length of two. However, an LM test suggests that there is problem with serial correlation at this lag length. Increasing the lag length to three eliminates this problem. An AR roots table shows the VAR to be dynamically stable.

The Johansen’s Trace Test and Maximum Eigenvalue Test both indicate that there exists one cointegrating equation at this lag length. But this is expected, since we now have evidence that the monetary base is not only cointegrated with industrial production, but also with inflation expectations. As discussed in the posts where the baseline VAR model was developed, since there is cointegration we should probably estimate a Vector Error Correction Model (a VECM), since it can generate statistically efficient estimates without losing long-run relationships among the variables as a VAR in levels (a VARL) might. However, in cases where there is no theory which can suggest the true cointegrating relationship or how it should be interpreted, it is probably better not to estimate a VECM.

I am using a recursive identification strategy (Choleskey decomposition), which is the dominant practice in the empirical literature on the transmission of monetary policy shocks. Such a strategy means that the order of the variables affects the results. For the three-variable VAR, I arranged the output level first, the price level second, and the monetary policy instrument third in the vector. This ordering assumes that the Federal Open Market Committee (FOMC) sees the current output level and price level when it sets the policy instrument, but that the output level and price level respond to a policy shock with one lag. For the four-variable VAR, the financial variable is ordered last, implying that financial markets respond to a policy shock with no lag. This ordering is essentially the same as Christiano et al. (1996), Edelberg and Marshall (1996), Evans and Marshall (1998), and Thorbecke (1997), which place the VAR variables in order of the goods and services markets first, the monetary policy instruments second, and the financial markets last.

As before, the response standard errors I will show are analytic, since Monte Carlo standard errors change each time an Impulse Response Function (IRF) is generated. Here are the responses to the monetary base and inflation expectations in the four-variable VAR.

Sadowski GC4_3

The instantaneous response of inflation expectations to a positive shock to the monetary base is negative, but it is relatively small and statistically insignificant. This is followed by a statistically significant positive response in the third month. Furthermore a positive shock to inflation expectations in month one leads to a statistically significant positive response in the level of industrial production from months four through nine.

The IRFs show that a positive 2.6% shock to the monetary base in month one leads to a peak increase in inflation expectations of 0.048 percentage points in month three. In turn, a positive 0.10 percentage point shock to inflation expectations in month one leads to a peak increase in industrial production of 0.23% in month eight.

Why might an increase in inflation expectations lead to an increase in output?

Because debt payments are contractually fixed in nominal terms, an increase in inflation expectations should lower the expected value of liabilities in real terms. On the other hand, an increase in inflation expectations should not lower the expected value of assets in real terms. Monetary expansion that leads to an increase in inflation expectations therefore raises expected net worth, which lowers the perception of adverse selection and moral hazard problems, and leads to an increase in nominal spending and output. In fact, the view that increased inflation has an important effect on nominal spending has a long tradition in economics, and it is a key feature in the debt-deflation view of the Great Depression espoused by Irving Fisher.

Perhaps of even greater importance, inflation expectations are the closest proxy we have for nominal GDP (NGDP) expectations, or expected aggregate demand (AD), as an increase in expected AD should also lead to an increase in inflation expectations, ceteris paribus. And an increase in NGDP expectations should lead to increased nominal spending by definition.

Too bad we didn’t have a prediction market for NGDP until December 2014. But I guess it’s better late than never.

Next time I shall add nominal Treasury yields to the baseline VAR.

Everybody knows that the whole purpose of QE is to drive down nominal Treasury yields, right?

Does it? Tune in next time and find out.

Revisiting Martin Feldstein, and The Married Lady Who Did Not Like Sex

A Benjamin Cole post

Flash report: Latest stats from U.S. are that hourly wages in May are up 2.0% YOY.

Readers bear with me, I will start with an analogy.

So, there was a married lady, who disliked sex, and thought marriage counseling was needed—for her husband. She thought he should appreciate her more-elevated virtues, her intellect, her taste in furnishings, her hostess skills, and her career. He should forget base gratifications.

Which brings to mind Martin Feldstein, Harvard econ prof, who has been preaching Inflationary End Times since at least 2009, as I pointed out in my last post here. In his last missive (June 29, for Project Syndicate) Feldstein warned wage hikes were going to run wild, threatening “accelerating” inflation. (Please note that inflation never threatens to rise to a higher level, such as 2.5%, and stay there. It always threatens to “accelerate.”)

But, the BLS just reported May wages were up a galloping 2% YOY. Even wages are not accelerating, let alone prices.

Why The Married Lady Story?

What do employers and employees really want from the economy? They want to make money, lots of it. Call it base gratification, if you will.

The Feldsteins of the world say there is a higher virtue that should be honored in the marketplace: that of zero inflation.

Feldstein is the married lady of macroeconomics.

Democracy

Let me pose this question: In a modern democracy, how will voters feel about free enterprise and capitalism if there are chronic “tight” labor markets?

How will voters feel if there is chronically high unemployment and weak labor markets and stagnant wages?

The Feldsteins of the world may wish to ponder the question.

Because a marriage should be about mutual gratification.

The “scene” that Feldstein supposedly is looking at:

BC on Feldstein

Germans: “We have them (Greeks and the other lot) by the balls”

From 2011: “The real reason for Germany’s optimism

…Look, I said, I’m a born pessimist. For the sake of argument, let’s assume a worst case or nearly worst-case scenario for Europe. I don’t believe the euro zone can survive in its current form, and I think Europe is in for a deep recession, not a short shallow one. What would the impact of that be on India, China, and all the other developing countries, particularly in Africa, whose trade is rapidly expanding with developing world’s two giants?

Forget what the response on the panel was. It was unremarkable. What’s interesting is what happened later, during a coffee break, when I got into a discussion with two senior German executives attending the meeting.

The nature of these meetings is that the hallway chatter is always more interesting that the formal program. Part of the reason why is that, particularly when talking to journalists, the businesspeople or politicians tend to regard those conversations as off the record. So I’ll abide by that here. One of the German execs was a consultant, and the other headed what I’ll call a quasi-official German organization.

They were slightly irritated by the pessimism I’d expressed earlier in the day. “Don’t you realize,” one of them said, “that the cost to us (Germany) of bailing out Greece is far less than it cost us to reintegrate East Germany after the wall came down in 1989?”

I almost choked on my croissant. Yes, I replied, I am aware of that. I lived and worked in Berlin as a journalist in the mid 1990s, when that very painful (economically speaking) process was taking place in Germany. But doesn’t that, I said politely, rather beg the question: Germany integrating their brethren, who’d been isolated and impoverished during the cold war, was a dream come true, whatever the cost. Germans, on the other hand paying to bail out Greece is, to average German, rather the opposite of a dream come true, is it not?

He waved me off. No no, he said, it will be taken care of. The Germans, he said, understood how beneficial to them membership in the euro zone has been. Without it, the gentleman said, the value of the Deutschemark would be 50% or 75% higher than it is under the euro. “German industry would be wiped off the map.

Why Germany needs the euro

Here was my ‘choking on my croissant’ moment number two. Most economists would agree with what my friend at the meeting had said; but he seemed either oblivious (not likely) or simply unconcerned (more likely) with the flip side of what he had just uttered. Italy, to take the third-largest economy in Europe, one with a sizeable and modern industrial base, is stuck with a currency — the euro — which is stronger than the old lira would be under current circumstances. But membership in the euro zone means Italy can’t devalue to bring some relief to its exporters.

I pushed back politely. Look, I said, it’s not Greece I’m worried about. It’s Italy. Third-biggest bond market in the world. Bond spreads this morning again heading over 7%(before the ECB intervened this to push them back down again.) Too big to fail, too big to save. Is the government, even one under a new Prime Minister, going to push through sufficient austerity to avoid a default?

Now the consultant perked up, speaking what he too believes to be the unvarnished truth. They have to, he said, because “to be blunt about it, we have them [both the Greeks and the Italians] by the balls.”

Apparently, it´s not exactly working out that way!

The “eager beavers” at the FOMC must be disappointed

I gather this is true from comments by Bill McBride (Calculated Risk). For the last several months, his conclusion was always that “…this was a “solid” report”. Today´s report was just “decent”!

In fact, given that this was the 6th year of recovery employment report, it was dismal! One pointer, if the participation rate had not dropped 0.2 points the unemployment rate, instead of falling to 5.3%, would have climbed to 5.7%.

No mystery that despite the “low” unemployment rate, wages stayed put, and remained at their 2% line year over year. Given that the “eager beavers” are watching wage growth with “hawk-eyes”, they must feel “depressed”!

In his preview of the Employment Report yesterday, Tim Duy concluded:

Bottom Line: Incoming data continues to support the case that the underlying pace of activity is holding, alleviating concerns that kept the Fed on the sidelines in the first half of this year. I anticipate the employment report, or, more accurately, the sum of the next three reports, to say the same. Accelerating wage growth could very well be the trigger for a September rate hike, while Greece could push any rate hike beyond 2015. I myself, however, tend to be optimistic the Greece situation will not spiral out of control.

Seems he´s optimistic about everything, forgetting someone took out the “firing pin”. Shortly he´ll be talking December 15…March 16…

It was mind-boggling to hear this conclusion in Ed Lazear´s (a former CEA president) interview about the jobs report:

Interviewer: The numbers we have do not correlate with the zero interest rate policy. If you were at the Fed…

Ed: The numbers don’t give reason to raise interest rates but there´s no reason to keep rates low because that´s not helping the economy very much.

Uau!