Brexit devaluation is monetary offset in action

A James Alexander post

I agree with Scott Sumner in his strong belief in the Efficient Market Hypothesis. The market is always right. But quite what it is “right” about is not always easy to tell. Sure, on central bank announcements the immediate market reaction is very telling. The market reaction is the policy, and this is not always what the central banks thinks is the policy.

The surprise Brexit vote did lead to some huge movements as markets opened on Friday 24th June and then more on Monday 27th after a weekend of follow-up news and reflection. But what were the markets signalling, especially the GBP devaluation and the large drops in the most domestically-oriented equities?

Scott Sumner wrote:

Brexit will reduce the foreign demand for British goods, services and assets. Since one needs pounds to buy British stuff, this reduces the value of the pound, as well as the quantity of exports. Think of it as a leftward shift in the demand for pounds, on an S&D diagram.

I am willing to admit less demand for pounds due to expected less FDI, as some global firms who based their EU businesses in the UK think of moving that production to inside the newly shrunk EU ex-UK. This is not a domestic demand shock directly as businesses would be choosing to produce less goods and services in the UK that were then exported.

Exports would also be expected to fall, further weakening demand for pounds, already weakened from less expected FDI.

The indirect effect is a demand shock for labour in the UK, that in turn reduces demand for goods and services within the UK.

UK workers are not less productive, there is just less demand for them. It is very hard to claim that the potentially lost UK export industries are more or less productive than average UK industry.

Other things being equal, less FDI, less exports and a potential, indirect, demand shock will shrink the size of the UK real economy. The drops of 10%-30% in some domestic-focused UK shares in particular sectors like commercial and residential real estate (British Land and Barret Developments), retail (Tesco and Marks & Spencer), banks (Lloyds Bank and Close Brothers) and media (Sky and ITV) illustrate this fear well.

However, care needs to be taken when looking at these sectors and judging the stock price falls. Property companies and banks are leveraged plays on the local economy, so that any local economy weakness gets magnified, sometimes greatly. Retail companies have been suffering from weak NGDP for years, as well as from the secular change to internet retailing, and so their equity prices may be particularly vulnerable to small changes in AD.

And care also needs to be taken not to assume the worst. Both the outcome of the negotiations with the EU and the eventual trade agreements with the Rest of the World will alter greatly these worst-case scenarios. In the absence of a government, fear and uncertainty get free reign but will dissipate over time, perhaps more quickly than many expected. if the latest news on Mrs May’s unopposed path to the top job is anything to go by.

Here comes the offset

But as Scott knows well a leftward shift in the demand curve for pounds is not like a shift in the demand curve for apples. A devaluation will lead to major monetary offset in the country experiencing the devaluation – even if these benefits are not immediately apparent – or understood by many commentators. The benefits will still accrue unless the devaluation is artificially prevented. So far the signs from the Bank of England are that it will be encouraged.

The current account may or may not improve following a devaluation as Chris Giles succinctly explained in the FT last week. He was echoing many macro experts. Unfortunately, Giles, like most economists and commentators who understand the subtleties of devaluation on trade deficits missed the bigger picture. The main benefit of a devaluation is something else.

In early 2014 in a discussion about Abenomics Scott re-posted a classic comment from the legendary Mark Sadowski. The punchline is very clear:

Devaluation improves a country’s trade balance only if the Marshall-Lerner condition on trade elasticities holds, and research shows that they’re not met in the majority of cases, either past or present:

That’s not to say that currency devaluation isn’t beneficial, of course it is, but the benefit flows primarily from increased domestic demand. 

Chris Giles does understand, and in fact warns in a follow-up  piece about higher inflation in the UK as a consequence of the devaluation and how it might hurt households:

In summary, Brexit has unleashed a different sort of currency depreciation, according to modern economics, one that is less likely to encourage domestic investment for exports, is more likely to raise inflation and will be more painful for hard-pressed families.

But it will drive up NGDP up, as domestic demand has to rise in nominal terms, and this, given wage and price stickiness, will drag up GDP in real terms too.

In fact, this rising nominal demand will be especially welcome in a UK economy starved  of nominal growth for the last 12 months. Something on which Giles and most of the UK macro-economic commentariat have been notably, not to say shamefully, silent.

Around the same time as the Sadowski re-post but on a different subject, Britmouse over at uneconomical also had an excellent post making a related point:

It is not that a[negative]productivity shock causes a rise in unemployment.  A productivity shock causes a tightening of monetary policy which causes a rise in unemployment.  

Brexit may cause a supply shock but there will only be a rise in unemployment if monetary policy is also tightened. If monetary policy is eased there will be moves within the economy from one job to another, but not an overall reduction in employment. That is the benefit of stable nominal growth, some people may experience low or no nominal wage growth but they will not be made unemployed.

Will the UK be poorer on leaving the EU? Maybe, but economies are quick to adjust as long as nominal growth is maintained at a suitable level. Unilateral free trade would also help as sagely proposed by the economists for Brexit.

On Fiscal policy and the ZLB (What´s he on about?)

A Mark Sadowski post

Evan Soltas writes on a theme that is much in vogue lately: “Fiscal Policy and the ZLB”:

I have been doing some reading for my undergraduate thesis, which looks at the role of credit-supply shocks in the Spain during its housing boom and bust, and I came across some interesting thoughts from Bob Hall. Commenting on research by Alan Auerbach and Yuriy Gorodnichenko, Hall makes some useful points that contradict a lot of the received wisdom about the efficacy of fiscal policy:

I conclude that the chapter uncovers a proposition of great importance in macroeconomics—that the response to government purchases is substantially greater in weak economies than in strong ones. The finding is a true challenge to current thinking. The first thing to clear away is that the finding has little to do with the current thought that the multiplier is much higher when the interest rate is at its lower bound of zero…Standard macro models have labor and product supply functions that are close to linear over the range of activity in the OECD post-1960 sample. The simple idea that output and employment are constrained at full employment is not reflected in any modern model that I know of. [Bolding is by me, not Hall.]

On the economics blogosphere, the “current thought” is also that, because monetary policy is in certain respects (that is, if only by social convention) constrained when the policy rate hits zero, fiscal policy becomes discontinuously powerful at the zero lower bound. Once the policy rate is a quarter of a percentage point, time to turn off fiscal policy, one might infer. Scott Sumner is one of the clearest and most persuasive exponents of this view—see here, for instance.

It turns out that the best evidence on fiscal policy does not support it. That conclusion is new to me, which is to say that I think I have written things that rely on that view, and I would now consider them to be wrong.

……………………………………………………………….

To be clear on what I mean here: First, it is a classic result that, when the efficacy of monetary policy is uncertain, it should not fully stabilize demand. Second, if the zero lower bound poses any restrictions on monetary policy, and it obviously does, if only in many indirect ways, then the appropriate amount of conservatism actually increases the risk of a future zero lower bound event rises, which is basically a function of the current policy rate. Fiscal multipliers are, as a result, well above zero when the policy rate is positive and decrease slowly and smoothly in the policy rate.

It makes sense if you think in terms of the fiscal multiplier being a measure of monetary policy incompetence. But just because monetary policymakers may be less than fully competent doesn’t mean that monetary policy is impotent.

Soltas’ reference to Scott Sumner needs explaining (perhaps even to Evan). When Scott says that the fiscal multiplier is zero above the zero lower bound he is not suggesting it is positive at the zero lower bound. Scott doesn’t believe in the liquidity trap, so when Scott says this what he is really saying is that even under the assumption of the existence of the liquidity trap, monetary policy makers should be held fully accountable for the level of nominal income away from the zero lower bound (as they clearly are now in the US). 

Seven years of US experience in steering nominal income without the use of the fed funds rate as the policy instrument should completely disabuse us of the existence of the liquidity trap. When you take into account the ability of monetary policy to offset fiscal policy, the fiscal multiplier is zero, even at the zero lower bound in interest rates.

The chart illustrates:

The US is going to fall behind the Euro 19

A James Alexander & Mark Sadowski post

Things are changing. It is looking like Euro Area NGDP growth could soon overtake that of the US, with similar consequences for RGDP.

Mario Draghi and most of the ECB is keen to do more monetary easing. The QE programme seems to be working on NGDP despite his struggles to raise the inflation rate.

Janet Yellen and most of the Federal Reserve thinks monetary conditions are too easy and, using the discredited Philips Curve, believes that low levels of economic slack mean inflation is about to accelerate.

Both central banks use false Phillips Curve models, it’s just that they think they are in different places on the Curve. The Europeans think they have plenty of slack. Massive slack, or rather high unemployment shows something is wrong with the economy and monetary policy can do something about that by raising nominal income. When there is less obvious slack, as in the US, monetary policy should be set to provide stable levels of nominal GDP growth. The US is clearly failing here.

The result is improving economic prospects for the Euro 19 and worsening ones for the US.

The likely further deceleration of NGDP in the US is predicted by the anaemic growth in the US monetary base. The further acceleration of NGDP in the Euro 19 is seen in the strengthening growth in the Euro monetary base.

The market also has to anticipate future changes in the monetary base, and as can be clearly seen there have been numerous false starts, especially in the Euro Area. However, the evil Trichet has gone and the austro-Germans on the board of the ECB significantly weakened in authority and influence. The bias is firmly in favour of more easing, and the market should continue running with that.

Sadly, the Euro Area economy has a lot of ground to make given those horrible NGDP and Base Money growth rates over the last seven years.

QE and Business Investment: The VAR Evidence: Part 3

A Mark Sadowski post

What we are going to do next is to construct three bivariate Vector Auto-Regression (VAR) models to generate Impulse Response Functions (IRFs) in order to show what a shock to the inflation expectations, stock prices and the value of the US dollar leads to in terms of business investment. As mentioned in Part 2, all four of our series (T5YIEM, DJIA, TWEXBPA and ANXAVS) have unit roots. With unit roots in our models, we are faced with a procedure that could lead to a VAR model in differences (a VARD), a VAR model in levels (a VARL), or a Vector Error Correction Model (a VECM).

Since there is no evidence of cointegration between investment in equipment and stock prices or the value of the US dollar, we are really only faced with a choice between a VARD and a VARL in these two cases. And although the existence of cointegration between investment in equipment and inflation expectations means that a VECM is an option in the third case, given the pros and cons of doing so, I am not going to estimate a VECM. For a detailed discussion of what these pros and cons are, see this post.

This means we are confronted with a tradeoff between statistical efficiency and the potential loss of information that takes place when time series are differenced. As we shall soon see, this is not at all an issue, so in the interests of brevity, I am only going to estimate three VARLs.

Motivated by the dominant practice in the empirical literature on the transmission of monetary policy shocks, I am going to use a recursive identification strategy (Cholesky decomposition). Such a strategy means that the order of the variables affects the results. I will follow the traditional practice of ordering the goods and services market variables before the financial market variables in each vector. The response standard errors I will show are analytic, as Monte Carlo standard errors change each time an IRF is generated. In order to render the IRFs easier to interpret, for the rest of this analysis, with the exception of T5YIEM (which is already in percent) I have multiplied the log level of each series by 100.

Let’s look at the effect of a positive shock to inflation expectations first.

Most information criteria suggest a maximum lag length of two in the VAR involving inflation expectations. The LM test suggests that there is no problem with serial correlation at this lag length. The AR roots tables suggest that the VAR is dynamically stable at this lag length. Here are the responses to a shock to inflation expectations.

A positive shock to inflation expectations leads to a statistically significant positive response to investment in equipment in months two through 31, or a period lasting nearly two and a half years. The IRFs show that a 13 basis point shock to inflation expectation in month one leads to a peak increase in investment in equipment of 1.04% in month 11. Recall that we previously showed that a positive 2.6% shock to the monetary base (QE) leads to an increase in inflation expectations of 4.8 basis points.

Now let’s look at the effect of a positive shock to stock prices.

Most information criteria suggest a maximum lag length of one in the VAR involving stock prices. The LM test suggests that there is no problem with serial correlation at this lag length. The AR roots tables suggest that the VAR is dynamically stable at this lag length. Here are the responses to a shock to stock prices.

A positive shock to stock prices leads to a statistically significant positive response to investment in equipment in months two through 40, or a period lasting over three years. The IRFs show that a 3.1% shock to stock prices in month one leads to a peak increase in investment in equipment of 1.10% in month 15. Recall that we previously showed that a positive 2.3% shock to the monetary base (QE) leads to an increase in stock prices (DJIA) of 1.6%.

Finally let’s look at the effect of a negative shock to the value of the US dollar.

Most information criteria suggest a maximum lag length of four in the VAR involving the US dollar. The LM test suggests that there is no problem with serial correlation at this lag length. The AR roots tables suggest that the VAR is dynamically stable at this lag length. 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 make the IRFs easier to interpret. Here are the responses to a shock to the value of the US dollar.

A positive shock to the value of foreign currency in month one leads (with the sole exception of month 5) to a statistically significant positive response in investment in equipment in months three through 27, or a period lasting over two years. The IRFs show that a 0.90% shock to the value of foreign currency in month one leads to a peak increase in investment in equipment of 1.16% in month 25. Recall that we previously showed here and here that a positive 1.9-2.5% shock to the monetary base (QE) leads to an increase in the value of the euro (1.5%), the Canadian dollar (1.4%), the Mexican peso (1.1%) and the Japanese yen (1.1%) in terms of the US dollar.

Now that we’ve established the empirical facts concerning QE and investment in equipment, let’s discuss the monetary theory that explains these facts.

As we have previously discussed, a positive shock to the US monetary base increases expected Nominal GDP (NGDP), or expected aggregate demand (AD). Higher expected AD means higher inflation expectations, ceteris paribus. Higher expected AD also leads to higher nominal stock prices. And higher expected inflation leads to an increase in the expected real exchange rates of foreign currencies in terms of the US dollar.

So why do higher inflation expectations, higher stock prices and a lower US dollar lead to increased investment in equipment?

Inflation expectations are the closest proxy we have for expected NGDP as an increase in expected NGDP should lead to an increase in inflation expectations, ceteris paribus. An increase in expected NGDP should lead to an increase in investment in equipment as businesses anticipate rising sales and increased profit making opportunities.

James Tobin’s q theory provides a mechanism through which increased NGDP expectations lead to increased investment in equipment 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 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 they are buying. Thus investment spending will rise because corporations can purchase new equipment with only a small issue of stock.

An increase in the real exchange rate of foreign currency in terms of the US dollar can make US goods and services more competitive with goods and services priced in that currency, both here and in that currency area. And if US goods and services become more competitive with goods and services priced in foreign currencies, this provides an incentive for US businesses to increase their investment in equipment.

And what of Robert Waldmann’s theoretical argument that QE leads to less business investment by raising the price of long term Treasuries (lowering their yields)?

The biggest problem with this theory is the empirical fact, despite the widely accepted myth otherwise, that QE leads to higher bond yields.

In Waldmann’s defense, he states that he is sure that Michael Spence and Kevin Warsh are wrong, and that he is simply making a theoretical argument for their conclusion, something which DeLong and Krugman argued Spence and Warsh had failed to do.

And, something which I hitherto have not discussed, just how important is the equipment component of business investment?

The three main components of private nonresidential fixed investment (PNFI) are 1) equipment, 2) intellectual property rights, and 3) structures. In the US in 2014 PNFI totaled $2,233.7 billion. Equipment represented $1036.7 billion of that total or 46.4%. Intellectual property rights (software, R&D and artistic rights) represented $690 billion of that total or 30.9%.  Structures represented $507 billion of that total or 22.7%.

Thus equipment is by far the most important component of business investment, and I find it remarkably difficult to believe, given QE’s demonstrably positive effect on investment in equipment (as well as its demonstrably positive effect on the output and price level), that it might have a negative effect on business investment overall.

QE and Business Investment: The VAR Evidence: Part 2

A Mark Sadowski post

In Part 1 we demonstrated that Value of Manufacturers’ Shipments for Capital Goods: Nondefense Capital Goods Excluding Aircraft Industries (ANXAVS) is a monthly frequency proxy for private nonresidential investment in equipment.

In Part 2 we are going to check if inflation expectations, stock prices and the value of the US dollar are correlated with private nonresidential investment in equipment in the Age of Zero Interest Rate Policy (ZIRP). Specifically we’re going to check if the 5-Year Breakeven Inflation Rate (T5YIEM), Dow Jones Industrial Average (DJIA) and the Real Trade Weighted U.S. Dollar Index: Broad (TWEXBPA) each Granger cause ANXAVS. This analysis is performed using a technique developed by Toda and Yamamoto (1995).

First let’s consider inflation expectations. Here is T5YIEM and the natural log of ANXAVS from December 2008 through September 2015.

Using the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests I find that the order of integration is one for T5YIEM and two for LANXAVS. I set up a two-equation Vector Auto-Regression (VAR) in the levels of the data including an intercept for each equation.

Most information criteria suggest a maximum lag length of two. The LM test suggests that there is no problem with serial correlation at this lag length. The AR roots tables suggest that the VAR is dynamically stable at this lag length, and Johansen’s Trace Test and Maximum Eigenvalue Test both indicate the series are cointegrated at this lag length. This suggests that there must be Granger causality in at least one direction between T5YIEM and ANXAVS.

Then I re-estimated the level VAR with two extra lags of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 4, I left the intervals at 1 to 2, and declared the extra two lags of each variable to be exogenous variables. Here are the Granger causality test results.

Thus, I fail to reject the null hypothesis that private nonresidential investment in equipment does not Granger cause inflation expectations, but I reject the null hypothesis that inflation expectations does not Granger cause private nonresidential investment in equipment at the 5% significance level. In other words there is evidence that inflation expectations Granger causes private nonresidential investment in equipment from December 2008 through September 2015, but not the other way around.

Next let’s consider stock prices. Here is the natural log of DJIA and ANXAVS from December 2008 through September 2015.

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

Most information criteria suggest a maximum lag length of one for the VAR. The 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.

Then I re-estimated the level VAR with two extra lags 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 1, and declared the extra two lags of each variable to be exogenous variables. Here are the Granger causality test results.

Thus, I fail to reject the null hypothesis that private nonresidential investment in equipment does not Granger cause stock prices, but I reject the null hypothesis that stock prices does not Granger cause private nonresidential investment in equipment at the 10% significance level. In other words there is evidence that stock prices Granger causes private nonresidential investment in equipment from December 2008 through September 2015, but not the other way around.

Finally let’s consider the value of the US dollar. Here is the natural log of TWEXBPA and ANXAVS from December 2008 through September 2015.

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

Most information criteria suggest a maximum lag length of four for the VAR. The 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.

Then I re-estimated the level VAR with two extra lags of each variable in each equation. But rather than declare the lag interval for the two endogenous variables to be from 1 to 6, I left the intervals at 1 to 4, and declared the extra two lags of each variable to be exogenous variables. Here are the Granger causality test results.

Thus, I fail to reject the null hypothesis that private nonresidential investment in equipment does not Granger cause the value of the US dollar, but I reject the null hypothesis that the value of the US dollar does not Granger cause private nonresidential investment in equipment at the 1% significance level. In other words there is evidence that the value of the US dollar Granger causes private nonresidential investment in equipment from December 2008 through September 2015, but not the other way around.

The next step in this process is to determine the nature of these “correlations”. What do positive shocks to inflation expectations, positive shocks to stock prices, and negative shocks to the value of the US dollar lead to in terms of private nonresidential investment in equipment? Do they lead to a decline in investment as Mike Spence and Kevin Warsh are implicitly claiming?

Or might they cause investment to increase (counterfactually) as Monetarists claim? In order to determine this we will estimate properly specified bivariate VARs and generate appropriate Impulse Response Functions (IRFs).

For that, tune in next time.

QE and Business Investment: The VAR Evidence: Part 1

A Mark Sadowski post

Mike Spence and Kevin Warsh, writing in the Wall Street Journal on Wednesday said:

 “We believe that QE [Quantitative Easing] has redirected capital from the real domestic economy to financial assets at home and abroad. In this environment, it is hard to criticize companies that choose “shareholder friendly” share buybacks over investment in a new factory. But public policy shouldn’t bias investments to paper assets over investments in the real economy.”

To which Brad DeLong responded by saying:

“As I have said before and say again, weakness in overall investment is 100% due to weakness in housing investment. Is there an argument here that QE has reduced housing investment? No. Is nonresidential fixed investment below where one would expect it to be given that the overall recovery has been disappointing and capacity utilization is not high? No. The U.S. looks to have an elevated level of exports, and depressed levels of government purchases and residential investment. Given that background, one would not be surprised that business investment is merely normal–and one would not go looking for causes of a weak economy in structural factors retarding business investment. One would say, in fact, that business investment is a relatively bright spot.”

And Paul Krugman, who said:

“It is, indeed, kind of amazing. In the eyes of critics, QE is the anti-Veg-O-Matic: it does everything bad, slicing and dicing and pureeing all good things. It’s inflationary; well, maybe not, but it undermines credibility; well, maybe not but it it causes excessive risk-taking; well, maybe not but it discourages business investment, which I think is a new one.”

And Larry Summers, who said:

“Perhaps Spence and Warsh are on to something that I am missing. I’m curious whether they can point to any peer reviewed economic research, or indeed any statistical work, that backs up their views.”

And Joseph Gagnon, who said:

“…economies in which central banks were most aggressive in conducting QE early in the recovery (the United Kingdom and the United States) have been growing more strongly than economies that were slow to adopt QE (the euro area and Japan)…. Indeed, to the extent that QE has raised stock prices, it discourages businesses from buying back stock because it makes that stock more costly to buy.”

About the only economist who rose to Spence and Warsh’s defense was Robert Waldmann, who said:

“The argument is that the duration risk in long term Treasuries is negatively correlated with the risk in fixed capital. I think this is clearly true. The risk of long term Treasuries is that future short term rates will be high. This can be because of high inflation or because the FED considers high real rates required to cool off an overheated economy. Both of these are correlated with high returns on fixed capital (someone somewhere keeps arguing that what the economy needs is higher inflation).

This means that a higher price for long term treasuries should make fixed capital less attractive — the cost of insuring against the risk in fixed capital is greater.”

It just so happens that there is Vector Auto-Regression (VAR) evidence on the relationship between QE and business investment.

This summer we showed that, in the age of zero interest rate policy (ZIRP), from December 2008 to present, the monetary base Granger causes inflation expectations, stock prices and the value of the US dollar, and that positive shocks to the monetary base (QE) lead to

statistically significant increases in inflation expectations,

statistically significant increases in stock prices,

and statistically significant decreases in the value of the US dollar.

In this series of posts we are going to show that in the Age of ZIRP, inflation expectations, stock prices and the US dollar all have an effect on investment in equipment, a component of business investment.

Private nonresidential investment in equipment is only available at a quarterly frequency. So since this analysis requires data at a monthly frequency, it is necessary to find a proxy variable for investment in equipment.

In applied macroeconomics, proxy variables typically satisfy two main requirements. First, the proxy variable should measure the equivalent characteristic of a reasonable subset of the variable being proxied. Secondly, the contemporaneous growth rates of the proxy variable and the variable being proxied should be correlated (i.e. have a relatively high Pearson’s r value).

Value of Manufacturers’ Shipments for Capital Goods: Nondefense Capital Goods Excluding Aircraft Industries (ANXAVS) overlaps in content considerably with US private nonresidential investment in equipment, and is available at a monthly frequency back to January 1992. Here is a graph of the natural log of ANXAVS and the natural log of Gross Private Domestic Investment: Fixed Investment: Nonresidential: Equipment (Y033RC1Q027SBEA) since 1992Q1.

ANXAVS overlaps in content with private nonresidential investment in equipment, and it ranges from 77.4% to 116.2% of Y033RC1Q027SBEA from 1992Q1 through 2015Q3. So it would appear that the first proxy variable requirement is well satisfied.

Now we must check to see if the two variables are correlated. Here are the results of regressing the logged difference (i.e. the growth rate) of Y033RC1Q027SBEA on the logged difference of ANXAVS and the corresponding scatterplot with the Ordinary Least Squares (OLS) regression line.

The R-squared value is approximately 0.633. Since the growth rates are positively correlated, the Pearson’s r value is +0.796, which is above average for a macroeconomic proxy variable. So it would appear that the second proxy variable requirement is well satisfied. Thus we conclude that ANXAVS is a suitable monthly frequency proxy for private nonresidential investment in equipment.

In Part 2 we’ll check to see if inflation expectations, stock prices or the value of the US dollar are “correlated” with private nonresidential investment in equipment in the age of ZIRP.

Stephen Williamson Discovers VAR Analysis

A Mark Sadowski post

In his most recent post Stephen Williamson states the following:

“The modern version of the Monetary History approach is VAR (vector autoregression) analysis. This preliminary version of Valerie Ramey’s chapter for the second Handbook of Monetary Economics is a nice survey of how VAR practitioners do their work. The VAR approach has been used for a long time to study the role of monetary factors in economic activity. If we take the VAR people at their word, the approach can be used to identify a monetary policy shock and trace its dynamic effects on macroeconomic variables – letting the data speak for itself, as it were…But, should we buy it? First, there are plenty of things to make us feel uncomfortable about VAR results with regard to monetary policy shocks. As is clear from Ramey’s paper, and to anyone who has read the VAR literature closely, results (both qualitative and quantitative) are sensitive to what variables we include in the VAR, and to various other aspects of the setup. Basically, it’s not clear we can believe the identifying assumptions…”

As Chris Sims (1996) noted in response to similar criticism of VAR by Glenn Rudebusch, issues of “variable selection are universal in macroeconomic modeling” (pp. 9). And as for the issue of identification, the Ramey paper to which Williamson links lists ten different approaches, all of which are associated with VAR modeling to some degree or another.

So what is the alternative to VAR modeling?

Presumably, from his later criticism of Christiano, Eichenbaum and Evan’s (2005) approach to DSGE modeling, which matches the impulse responses from the model to those of actual data, Williamson would prefer models achieve identification by imposing structure based on theory. But DSGE identification is even less straightforward than VAR identification. Canova and Sala (2009), Komunjer and Ng (2011), and others have pointed out some of the many problems with identification in DSGE models.

And, in the final analysis, this criticism of VAR on the basis of identification is ironic given it is no exaggeration to say that it was in fact the “incredible identification” of large scale models that was the primary motivation for Chris Sims (1980) to introduce VAR analysis to macroeconomics.

Williamson goes on to provide another objection to VAR modeling:

“…Second, even if you take VAR results at face value, the results will only capture the effect of an innovation in monetary policy. But, modern macroeconomics teaches us that this is not what we should actually be interested in. Instead, we should care about the operating characteristics of the economy under alternative well-specified policy rules. These are rules specifying the actions the central bank takes under all possible circumstances. For the Fed, actions would involve setting administered interest rates – principally the interest rate on reserves and the discount rate – and purchasing assets of particular types and maturities.”

I think the best response to this is to turn to page 24 of the Ramey paper which Williamson cites in his post:

”Before beginning, it is important to clarify why we are interested in monetary policy shocks. Because monetary policy is typically guided by a rule, most movements in monetary policy instruments are due to the systematic component of monetary policy rather than to deviations from that rule. Why, then, do we care about identifying shocks? We care about identifying shocks for a variety of reasons, the most important of which is to be able to estimate causal effects of money on macroeconomic variables. As Sims (1998) argued in his discussion of Rudebusch’s (1998) critique of standard VAR methods, because we are trying to identify structural parameters, we need instruments that shift key relationships. Analogous to the supply and demand framework where we need demand shift instruments to identify the parameters of the supply curve, in the monetary policy context we require monetary rule shift instruments to identify the response of the economy to monetary policy.”

Failure to correctly identify the effect of an innovation in monetary policy might for example allow us to theorize that raising interest rates causes inflation to increase.

Fortunately, partly thanks to VAR analysis, most economists don’t consider that to be very plausible (just as most people don’t think that it is the act of opening umbrellas that causes it to rain).

Addendum

In comments Williamson says the following:

“In the quote, you can see roughly what Friedman and Schwartz were up to. They looked at turning points in money supply and turning points in what he calls “general business.” I haven’t read the Monetary History in a long time, but I think “general business” is the NBER “reference cycle,” which is roughly an index of aggregate economic activity – not aggregate output, but presumably highly correlated with it. Basically, Friedman and Schwartz showed that money leads aggregate economic activity. It’s the informal counterpart of what Sims (1972) is about. Sims showed that money Granger-causes output in the the U.S. time series. Of course Granger causation need not imply economic causation. That was part of Tobin’s critique of Friedman and Schwartz. Money could in fact be endogenously responding to output, but appear to lead output in the time series. The endogeneity could come from policy, or from the banking sector, if we’re measuring money as M1 or M2, say.”

Williamson is referring to Tobin’s famous Post Hoc, Proctor Hoc (“after this, therefore because of this”) critique of Friedman.

Williamson implies that Granger causality tests are subject to the very same criticism, when in fact Chris Sim’s 1972 paper was specifically intended as a rebuttal to Tobin’s invocation of the fallacy (which is incredibly clear if one actually bothers to read the paper). Williamson’s comment on the endogeneity of money is even more ironic when one realizes that Granger causality testing is the primary econometric tool of the Post Keynesian empirical literature on endogenous money (e.g. Basil Moore, Thomas Palley, Robert Pollin, Peter Howells etc.).

Blanchflower Baloney

A Mark Sadowski post

In a recent post James Alexander caught Danny Blanchflower tweeting that he thought “NGDP totally impractical due to data revisions”.

This is a familiar complaint, voiced for example by Goodhart, Baker and Ashworth in January 2013.

There are numerous problems with this line of thinking.

First of all, central banks shouldn’t be targeting past values of economic variables anymore than one should attempt to drive a vehicle on a superhighway by looking in the rearview mirror. Arguably the world’s major central banks tried doing that in 2008, and we are still living with the results. Since central banks should only be targeting the expected values of economic variables, bringing up the issue of data revisions reveals a level of obtuseness that borders on the ridiculous.

And as irrelevant as the issue of data revisions is to the proper conduct of monetary policy, although NGDP levels tend to be revised, that certainly should not imply that inflation rates are not revised. In fact the personal consumption expenditure price index (PCEPI), the inflation rate of which is the official target of the Federal Reserve, often undergoes significant revisions.

There’s two main ways of measuring the size of the revisions of the components of national income and product accounts: 1) Mean Revision (MR) and 2) Mean Absolute Revision (MAR). For rate targeting MAR is the more appropriate measure, and in fact the MAR of inflation is usually smaller than the MAR of NGDP. However, for level targeting MR is more appropriate.

Interestingly, at least in the US (Page 27):

“The MRs for the price indexes for GDP and its major components are generally not smaller than those for real GDP and current-dollar GDP and its major components.”

In fact, over 1983-2009 the MR for the final revision to quarterly NGDP is 0.14, whereas over 1997-2009 the MR for the final revision to the GDP deflator and the PCEPI is 0.20 and 0.12 respectively. And over time the revisions have trended downward.

So I suspect that the MR for NGDP is smaller than the MR for PCEPI over 1997-2009.

Which means the claim you frequently hear that NGDP revisions are larger than inflation revisions is pure grade A horse manure. You will never see any evidence supporting this mindlessly repeated spurious claim, because no such evidence exists.

And, finally, inflation is a totally artificial construct requiring that we come up with an estimate of the extraordinary abstraction known as the “aggregate price level.” To see how preposterous this is imagine equating the aggregate price level between what it is now and what it was in say 14th century England. The goods and services are so different it requires the complete suspension of one’s disbelief.

In particular, PCEPI inflation is the difference between nominal PCE and real PCE, meaning PCEPI inflation is nothing more than the estimated residual between a truly nominal variable, which is relatively straightforward to measure, and a real variable, which is fundamentally an exercise in crude approximation.

It’s high time that central banks moved beyond the near medieval practice of targeting real variables and/or their residuals, and started targeting truly nominal variables, which according to the accepted tenets of monetary theory is their proper domain.

What Monetary Policy Can and Cannot Do: Japan Edition

A Mark Sadowski post

Here’s Scott Sumner discussing the Japanese economy as an example of the Great Stagnation.

1. In the 1st 4 quarters of Abenomics (i.e. 2013), RGDP grew by 2.4%, which we now know was a flat out boom.  Inflation rose into positive territory and the unemployment rate fell from 4.3% to 3.7%.
2.  From the 4th quarter of 2013 to the 2nd quarter of 2015 the Japanese economy grew by a grand total of 0.1%.  And the unemployment rate continued to fall, from 3.7% to 3.4%. That’s right, over the past 6 quarters the Japanese economy has been growing at above trend.  But that blistering pace can’t go on forever.  The unemployment rate is down to 3.4%, and unless I’m mistaken there is a theoretical “zero lower bound” on unemployment that is even more certain than interest rates. The Japanese economy is like a Galapagos tortoise that has just sprinted 20 meters, and needs a long rest.”

Let’s take a look at the ten quarter moving average of RGDP growth in Japan. All data comes from the Japanese Cabinet Office.  (Pre-1994 data using 68SNA values has been chained to post-1994 data using 93SNA values.)

It’s true, RGDP growth has only averaged 0.86% in the last ten quarters, which is nothing remarkable, even when compared to the low bar set by the average performance of the Japanese economy since 1993. (Also, keep in mind that virtually all of that real growth took place in the first four quarters of Abenomics.)

However, let’s look at the GDP implicit price deflator.

Hmm, the GDP implicit price deflator has averaged 1.42% in the last ten quarters which is the highest rate since 1993Q2. Of course this might be due to the fact that the consumption tax was raised from 5% to 8% in April 2014, except that the consumption tax was also raised from 3% to 5% in April 1997 and that didn’t lead to a sustained increase in GDP implicit price deflator inflation.

There’s one way to settle this. Let’s look at NGDP growth.

Yep, NGDP growth has averaged 2.27%, which is the highest rate since 1993Q3. That’s clearly not due to the consumption tax increase (that is, unless your model predicts that tax increases will be expansionary).

So it looks like the current round of Japanese QE has led to the greatest sustained increase in NGDP growth and GDP implicit price deflator inflation in 22 years, but it has only had an ephemeral effect on RGDP growth.

Where have I heard something like this before? Here’s Scott Sumner in 2011.

Just to be clear, it is quite possible (likely in my view) that Japan could get another 2% of RGDP by switching to a 3% NGDP target.  But it would be a one-time gain, as their labor market got less rigid.  Unemployment might fall to 2% or 3%, but trend growth shouldn’t change.

That sounds like an astute prediction to me.

Fiscal and Monetary Policy Interaction during the Age of ZIRP: Part 3

A Mark Sadowski post

In Part 3 we will add general government consumption and investment and general government net taxes to the trivariate baseline Vector Auto-Regression (VAR). Parts 1 and 2 can be found here and here.

With Government Wage and Salary Disbursements and Total Public Construction Spending (GWSDTPC) and Net Personal Taxes (monthly frequency proxies for general government consumption and investment and general government net taxes respectively) added to the baseline VAR model, most information criteria suggest a maximum lag length of four. The 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 two cointegrating equation at this lag length. But this is expected, since we have evidence that the the monetary base is cointegrated with industrial production, and that net personal taxes is cointegrated with both PCEPI and industrial production. 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 (Cholesky decomposition). Such a strategy means that the order of the variables affects the results. I will follow the practice of Fatas and Mihov (2001) in ordering general government consumption and investment spending first, the output level second, the price level third, general government net tax revenue fourth and the monetary policy instrument last.

This ordering assumes that government consumption and investment spending is not affected contemporaneously by shocks originating in other sectors of the economy, and that changes in government consumption and investment spending, unlike changes in net tax revenue, are largely unrelated to the business cycle. Ordering the output level and the price level before net tax revenue can be justified on the grounds that shocks to these two variables have an immediate impact on the tax base and, thus, a contemporaneous effect on net tax revenue.

Thus this ordering of variables captures the effects of automatic stabilizers on net tax revenue. This ordering also 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.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 of the output level and the price level to government spending, net taxes and the monetary base in the five-variable VAR.

The response of the level of industrial production and the price level to a positive shock to general government consumption and investment spending is statistically insignificant in every month. Similarly, the response of the level of industrial production and the price level to a positive shock to general government net tax revenue is statistically insignificant in every month.

In contrast, a positive shock to the monetary base in month one leads to a statistically significant positive response in the level of industrial production in months five through 12. Furthermore, a positive shock to the monetary base in month one leads to a statistically significant positive response in the price level in month three.

The IRFs show that a positive 2.1% shock to the monetary base in month one (first line above) leads to a peak increase in the level of industrial production of 0.19% in month ten (second line above). They also show that a positive 2.1% shock to the monetary base in month one (second line) leads to a peak increase in the price level of 0.050% in month three (first line).

The bottom line is that there is no evidence that general government consumption and investment spending and general government net tax changes have had a statistically significant effect on either the output level or the price level from December 2008 through May 2015, or during the Age of Zero Interest Rate Policy (ZIRP). This is consistent with the Granger causality and bivariate VAR evidence presented in Part 2. On the other hand there is evidence that monetary base changes have had a statistically significant effect on both the output level and the price level in the Age of ZIRP. This is consistent with the evidence which I presented in the twelve part series on the Monetary Base and the Channels of Monetary Transmission which concluded here.

Since we can conclude that fiscal policy has had no statistically significant effect on the output level or the price level in the Age of ZIRP, and that monetary policy has, it might be useful to investigate the effect that fiscal policy changes have had on monetary policy. Here are the responses of the government spending, net taxes and the monetary base to each other in the five-variable VAR.

The response of the monetary base to a positive shock to general government consumption and investment spending is statistically insignificant in every month. However, the response of the monetary base to a positive shock to general government net tax revenue is statistically significant in months three through eight. The IRFs show that a positive 4.0% shock to general government net tax revenue in month one leads to a peak increase in the monetary base of 1.7% in month six.

In other words, there is evidence that general government net tax changes have had a statistically significant effect on the monetary base in the Age of ZIRP.

Given the events surrounding the implementation of QE3 in particular, this shouldn’t be too surprising.

In mid-2012, several FOMC members (e.g. Evans, Rosengren and Williams) specifically mentioned the then forthcoming “fiscal cliff” as a motivation for additional monetary stimulus. An increase in income tax rates applicable to high income tax payers, and an increase in payroll taxes went into effect on January 1, 2013. These tax increases constituted approximately 70% of the budgetary effect of going over the “cliff” and are clearly visible in the graph of Net Personal Taxes in Part 2 of this post.

Between April 2012 and April 2013, Net Personal Taxes increased from $726.5 billion to $1,021.0 billion at an annual rate, a staggering 40.5% increase year-on-year. Given the above elasticity, and the five month lag to peak effect, this suggests that a substantial proportion of the increase in the monetary base under QE3 from September 2012 to September 2013 was in response to the fiscal cliff tax increase.

As Scott Sumner pointed out, recent estimates of Real GDP (RGDP) growth rates, Q4 over Q4, show that RGDP growth was only 1.7% and 1.3% in 2011 and 2012 respectively, before QE3 and the fiscal cliff, and was a more substantial 2.45% and 2.525% in 2013 and 2014, after QE3 and the fiscal cliff. It’s not hard to draw a connection between the improvement in RGDP growth rates after QE3 and the fiscal cliff and the VAR evidence that net tax changes have had no statistically significant effect on either the output level or the price level, that positive monetary base changes have had a statistically significant positive effect on both the output level and the price level, and that positive net tax changes have had a statistically significant positive effect on monetary base changes.

And the slow RGDP growth in 2011 and 2012 can itself be connected to this phenomenon.

Prior to the fiscal cliff, the largest discretionary changes in net taxes were those implemented as part of the “fiscal stimulus”. According to BEA estimates (via FRED), between 2008Q4 and 2010Q1, due to the American Reinvestment and Recovery Act (ARRA) Federal personal current taxes were reduced by $130.9 billion at an annual rate, and refundable tax credits to persons were increased by $29.7 billion at an annual rate. Thus Net Personal Taxes were reduced by $160.6 billion at an annual rate. This persisted through 2010Q4, and most of this reduction in Net Personal Taxes was maintained in the form of the 2% “payroll tax holiday” until the advent of the 2013 fiscal cliff.

The $160.6 billion discretionary cut in Net Personal Taxes meant that Net Personal Tax revenue was only $444.1 billion in March 2010, instead of $604.7 billion, a reduction of 26.6%. March 2010 also happened to be the month that QE1 was concluded. Thus the VAR estimates suggest that QE1 was probably substantially smaller in response to the cut in Net Personal Taxes enacted under the 2009 fiscal stimulus.

I suspect that the FOMC had expected a much larger economic response to the tax changes than actually occurred in 2010, and this is the main reason why they grudgingly acquiesced to the ad hoc $600 billion in monetary stimulus under QE2. It wasn’t until the specter of the 2013 fiscal cliff that a substantial amount of open ended quantitative easing was finally unleashed, and in turn it wasn’t until this act that RGDP growth was anything other than pathetically stagnant during the recovery from the Great Recession.

The upshot of all this is that the FOMC has behaved as though taxes have a substantial fiscal multiplier. They have reacted to tax cuts by reducing monetary stimulus, and to tax increases by increasing monetary stimulus. Since the time series econometric evidence shows that the tax cut multiplier is not statistically different from zero, and that quantitative easing has statistically significant effects on the output level and the price level, this means that tax cuts have had the perverse effect of leading to less economic growth and, similarly, that tax increases have led to increased economic growth.

Thus, in the Age of ZIRP, the monetary offset of fiscal policy has effectively meant that the fiscal multiplier has not only been zero, it has been negative.