The Financial Stability Case for a Nominal GDP Target · 2019. 5. 10. · 423 Case for a Nominal...

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The Financial Stability Case for aNominal GDP Target

David Beckworth

Ten years after the financial crisis there is a new appreciation forthe role household debt and financial fragility play in the businesscycle. Though some economists recognized their importance goinginto the crisis, many observers did not and were blindsided by theseverity of the Great Recession. Motivated by this experience, a spateof research over the past decade has refocused attention on theimpact household balance sheets and the financial system have onthe economy.

One line of this research has focused on household finance andhow it contributed to the economic downturn in the United States(Mian and Sufi 2010, 2014; Mian, Rao, and Sufi 2013). It documentshow the buildup of household debt, especially mortgage debt, duringthe housing boom made households susceptible to the decline inhousing prices starting in 2006. This decline precipitated deleverag-ing by households and, in turn, curtailed consumer spending andeconomic growth.1 Another vein of this research has looked at therole the financial crisis played in the U.S. economic slowdown(Brunnermeier 2009; Gorton 2012; Ricks 2016; Bernanke 2018).

Cato Journal, Vol. 39, No. 2 (Spring/Summer 2019). Copyright © Cato Institute.All rights reserved.

David Beckworth is Director of the Monetary Policy Project and a SeniorResearch Fellow at The Mercatus Center, George Mason University.

1Cross-country studies similarly find that those countries with rising householddebt to GDP ratios generally experience slower economic growth over themedium to long-term horizons (Mian, Sufi, and Verner 2017; Lombardi, Mohanty,and Shim 2017).

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It shows how a systemic run on institutional money assets caused acollapse in wholesale funding and triggered a severe credit crunch. Inturn, this breakdown in financial intermediation caused economicactivity to contract.2

While the household balance sheet and financial panic views aredistinct, they are also interrelated: household deleveraging affectedthe health of financial firms during the crisis while the reduction incredit supply exacerbated household financial problems. Along theselines, Gertler and Gilchrist (2018) and Aikman et al. (2018) showboth factors were jointly important to the emergence of the GreatRecession.3 Jordà, Schularick, and Taylor (2013, 2015, 2016), relat-edly, in their cross-country studies find countries with high house-hold debt levels tend to have a higher incidence of financial crises.Highly leveraged household sectors and financial crises, in otherwords, are often a jointly determined process.

This new appreciation for household debt and financial fragilitycan be seen from a broader perspective as the long-time coming con-sequence of the advanced economies credit regime that emerged inthe 1980s. Jordà, Schularick, and Taylor (2017) show that private sec-tor credit growth relative to GDP accelerated during that decade,creating a “financial hockey stick” pattern of leverage for advancedeconomies. They show this development has dampened businesscycle volatility overall while making advanced economies more sus-ceptible to spectacular financial crashes.

This renewed interest in household balance sheets and financialsystem stability has led to several different policy recommendations.First, the IMF, BIS, and policymakers in many advancedeconomies have called for macroprudential regulation. Thisapproach focuses on the stability of the entire financial system andworks by adjusting buffers—such as countercyclical capital require-ments and caps on loan to value ratios—to respond to aggregatefinancial shocks.

This approach, however, is not without its challenges. It is hard toknow what is a true financial vulnerability, what are the appropriate

2Cross-country analysis similarly finds that those countries with greater financialvulnerabilities leading up to the crisis experienced larger economic losses afterthe crisis (IMF 2018).3Bernanke (2018) makes the case that the severity of the Great Recession wasmostly due to the financial panic.

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Case for a Nominal GDP Target

indicators to follow, and how to define financial stability.4 In additionto these knowledge problems, macroprudential goals may conflictwith other policy goals and be subject to rent seeking by affected par-ties.5 For these reasons, macroprudential regulations, which havebeen implemented to varying degrees in different countries, are notyet fulfilling all of their desired goals (IMF 2018; BIS 2018).

A second policy recommendation put forth by some observers isthe need for state-contingent debt contracts (Shiller 2008; Mian andSufi 2014; Eberly and Krishnamurthy 2014; Piskorski and Seru2018). These are financial contracts whose payouts are contingent oncertain economic outcomes. In this context, the push has been formortgages whose principal and payments are indexed to local eco-nomic conditions. A weakening local economy would lower the realmortgage burden on households while a booming one would raise it.Such mortgages would resemble equity more than debt and lead tobetter risk sharing between debtors and creditors. In turn, thisimproved distribution of risk should improve the stability of thefinancial system. Shiller (2004), more generally, shows how these andother state-contingent contracts could radically transform our worldinto a more equitable and flourishing place.

Some progress has been made on this front with income-contingent student loans, contingent convertible corporate bonds,and a few state-contingent mortgages.6 Most debt, however, remainswritten in fixed nominal terms. The dearth of contingent debt con-tracts suggests that the cost of writing and enforcing them is prohib-itively expensive. For now, then, state-contingent contracts do notprovide a practical solution to the household debt and financial sta-bility concerns of advanced economies.

A third policy recommendation that addresses these concerns is touse monetary policy to create better risk sharing between debtors

4For more on the knowledge problem inherent to macroprudential regulationssee Salter (2014).5The recent delisting of Prudential as a significantly important financial institu-tion (SIFI) by the U.S. Financial Stability Oversight Council is seen by some asan example of rent seeking.6Companies like Unison, Patch, and Point have begun offering state contingent-like mortgages but remain a small part of the mortgage-origination market due,in part, to government-sponsored enterprises (GSEs) subsidizing traditionalmortgages. There has also been some limited use of state-contingent debtcontracts by sovereigns (see Anthony et al. 2017).

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and creditors. Specifically, a monetary regime that targets the growthpath of nominal GDP (NGDP) can be shown to reproduce the dis-tribution of risk that would exist if there were widespread use ofstate-contingent debt securities (Koenig 2013; Sheedy 2014;Azariadis et al. 2016; Bullard and DiCecio 2018). The basic idea isthat the countercyclical inflation created by an NGDP target willcause real debt burdens to change in a procyclical manner. As aresult, debtors will benefit during recessions and creditors will bene-fit during booms. Fixed nominal-priced loans will act more likeequity than debt and therefore promote financial stability.

This policy recommendation has the potential to be the mosttractable of the above three proposals since it only requires a NGDP-targeting monetary regime. While switching to such a monetaryregime is a nontrivial task, it would accomplish the same goals ofstate-contingent debt contracts and complement the efforts ofmacroprudential regulations. However, of the three proposals thisone has received the least attention. This may be due to the fact thatthe recent work on this proposal been largely theoretical since nocountry explicitly targets NGDP. This policy proposal, consequently,is ripe for further attention and development.

This article attempts to shed more light on this proposal by provid-ing the first empirical assessment of it. It does so by exploiting animplication of the theory: those countries whose NGDP stayed closestto its expected precrisis growth path during the crisis should haveexperienced the least financial instability. Put differently, some coun-tries experienced more stability in aggregate nominal spending thanothers and these differences should be systematically related to finan-cial stability if the theory is true. So even though no countries were tar-geting NGDP during the crisis, there is still a way to test the theory.

This article uses this understanding to provide an empirical test ofthe third policy proposal. It does so by outlining a method for esti-mating the expected growth path of NGDP for advanced economiesand then seeing whether the gap between it and actual NGDP is sys-tematically related to various measures of financial stability. Thisexercise is only a first look and is not the final word, but the resultsindicate more attention should be given to this third proposal. Thefindings strongly suggest that a stable NGDP growth path supportsfinancial stability. These findings, therefore, lend support to the exist-ing arguments for why advanced economies should consider adopt-ing an NGDP level target.

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In the sections that follow, the article further outlines the argu-ments of Koenig (2013), Sheedy (2014), Azariadis et al. (2016), andBullard and DiCecio (2018). It then derives the expected growthpath of NGDP for 21 advanced economies using IMF data and the“sticky forecast” approach of Beckworth (2018). Next, the article usesthis measure to create an NGDP gap that is used in some scatter-plots, a panel vector autoregression, and a panel local projectionmodel to determine the relationship between the NGDP gap andvarious economic variables. The article then concludes with somepolicy considerations.

Better Risk Sharing through NGDP TargetingThe key insight of Koenig (2013), Sheedy (2014), Azariadis et al.

(2016), and Bullard and DiCecio (2018) is that in a world of incom-plete markets where there is nonstate contingent nominal contract-ing, an NGDP target can reproduce the risk distribution that wouldoccur if there were complete markets and state contingent nominaldebt contracting.7 An NGDP target, in other words, can make up forthe lack of insurance against future risks that could affect debtors’ability to repay their debts. Conversely, an NGDP target can alsomake up for the lack of insurance against potential returns creditorsmight miss out on because their funds are locked up in fixed-pricenominal loans. Bullard and DiCecio (2018) show that this result holdseven when the modeled heterogeneity among debtors and creditorsapproximates that of the actual income, financial wealth, and con-sumption inequality in the United States. They note this makesNGDP targeting “monetary policy for the masses.”

The intuition behind these formal findings is that debtors andcreditors who have committed to fix-nominal debt contracts andtherefore to fixed money payments can be subject to both price leveland real income shocks. The former shocks have long been under-stood and generally seen as bolstering the case for a price-level orinflation target. Most famously, Irving Fisher (1933) made the casefor price level stability as a way to avoid unexpected deflation and arise in real debt burdens that could trigger a cascade of loan defaults.As Koenig (2013) notes, however, Fisher’s “debt deflation” scenario

7The ideas in these formal papers date back to Bailey (1837: 111–33) as shown bySelgin (2018: 57–70).

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is incomplete because it only looks at price level shocks. Debtors mayalso face financial stress from negative real income shocks. Bothtypes of shocks make it harder for debtors to service fixed money pay-ments since both shocks lower nominal income flows relative toexpectations. In both cases, the debtor is bearing the additional riskof these negative shocks relative to the creditor.

These two scenarios are illustrated in Table 1 as (1) and (2), where.pt, .yt, and .(py)t represent changes in the log of the price level,real income, and nominal income. Note, that in general, any combi-nation of these shocks that lowers nominal income relative to expec-tations puts a strain on debtors. It follows, then, that stabilizing .(py)t

via an NGDP target serves a useful insurance function for debtors.For a central bank, that means allowing changes in price level to off-set real income shocks so that actual nominal income equals expectednominal income. That is, in order for the following nominal incomeequality to hold

(1) .(py)t W .(py)tEt^1,

it must be the case that innovations to real income be offset by inno-vations to the price level:

(2) (.yt ^ .ytEt^1) W ^(.pt ^ .pt

Et^1)

or, equivalently:

(3) .pt W .ptEt^1 ^ (.yt ^ .yt

Et^1)

TABLE 1Risk Bearing by Household Type

Household Type Bears More Risk If

(1) .pt 3 .ptEt^1

Debtor or .(py)t 3 .(py)tEt^1

(2) .yt 3 .ytEt^1

(3) .pt 2 .ptEt^1

Creditor or .(py)t 2 .(py)tEt^1

(4) .yt 2 .ytEt^1

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Another way to understand equation (3) is that under an NGDPtarget a negative real income shock leads to an unexpectedly higherprice level and, for a given stock of fixed-price nominal debt, anunanticipated lower real debt burden for the debtor. The creditor,consequently, receives a lower real debt payment than expected andshares in the real income loss. In short, the risk of a real income lossis shared more evenly between the debtor and creditor under anNGDP target than under a price stability target.

Equation (3) also implies that under an NGDP target a positive realincome shock will lead to an unexpectedly lower price level and, for agiven stock of fixed-price nominal debt, an unanticipated higher realdebt payment from the debtor to the creditor. This feature can be seenas providing insurance to a creditor against having their funds lockedup in a fixed nominal loan with a constant yield while real earnings inthe rest of the economy rise due to the positive real income shock.

Imagine, for example, there is a positive total factor productivity(TFP) shock that raises real returns in the economy. If a creditorknew this productivity innovation was going to occur ex-ante, hewould have required an equivalent risk-adjusted return on a loan toa debtor. But the creditor cannot know this outcome ex-ante since itis a shock. Under a price stability target, the creditor bears this riskand would miss out on the gain from the TFP shock. An NGDP tar-get, on the other hand, forces the debtor to share some of the “wind-fall gain” with the creditor through a higher real debt payment.Again, risk is shared more evenly between the debtor and creditorunder the NGDP target and therefore mimics a world of state-contingent debt contracts.

Finally, if there are no real income shocks then an NGDP targeteffectively defaults to a price stability target so that .pt W .pt

Et^1.8

An NGDP target, consequently, also avoids the “bad” price level sur-prises depicted in scenarios (1) and (3) in Table 1.9

In practice, a central bank targeting NGDP does not need to man-ually adjust the price level to offset real income shocks. Instead, the

8This can be seen in equation (3) by noting that if there are no real income shocksthen .yt W .yt

Et^1 and .pt W .ptEt^1.

9The “bad” price level surprises should be distinguished from the “good” pricelevel surprises that an NGDP target creates when there are real income shocks.As noted above, in the latter case these price level surprises act as a form ofinsurance.

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central bank simply aims to keep aggregate nominal spending on itstargeted growth path and the price level will by default adjust to thereal income shocks. The insurance benefits from the countercyclicalinflation are therefore produced automatically (Beckworth 2017).

No central bank has ever attempted a NGDP target, but the Bankof Israel (BoI) has unintentionally provided an example of what sucha monetary regime might look like. The BoI officially targets an infla-tion range of 1–3 percent, but as Figure 1 shows, NGDP in Israel hasbeen growing on a fairly stable trend since 2008. As a consequence,real income shocks have led to almost mirror opposite movements inthe inflation rate as measured by the GDP deflator. This inverse rela-tionship is not perfect, but it is strong enough that the GDP deflatorinflation has been allowed to temporarily move outside the inflationtarget range when there have been large real income shocks. Forexample, in 2009 during the Great Recession the inflation rate justtopped 5 percent. Despite this inflation flexibility, inflation on aver-age over the entire period in Figure 1 has been near the center of itstargeted range at a rate of 1.9 percent. An explicit NGDP targetwould arguably result in a similar outcome.

FIGURE 1Stable NGDP Growth and Countercyclical

Inflation in Israel

Nominal GDPTrend Real GDP

Target Inflation RangeGDP Deflator

2015

2014

2013

2012

2018

2017

2016

2011

2010

2009

2008

2015

2014

2013

2012

2018

2017

2016

2011

2010

2009

2008

Billio

nsof

Shek

els

–101234567

Perc

entC

hang

efro

ma

Year

Ago

170190210230250270290310330350

Stable Nominal GDP Growth: Israel Countercyclical Inflation: Israel

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Measuring NGDP ExpectationsThe main objective of this paper is to empirically assess the policy

proposal that NGDP targeting will result in better risk sharingbetween debtors and creditors. An obvious challenge to doing so isthat no country has targeted NGDP so there is no track record toevaluate.10

There is, however, an indirect way to test this proposal by exploit-ing an implication of the theory. It predicts that those countrieswhose NGDP stayed closest to its expected precrisis growth pathshould have experienced the least financial instability during the cri-sis. Put differently, some countries experienced more stability inaggregate nominal spending than others during the crisis and thesedifferences should be systematically related to financial stability if thetheory is true. A cross-country sample over this period of NGDPdeviations from expected growth paths should reveal whether thisprediction is borne out in the data.

This possibility raises another challenge: how best to measurethe expected growth path of NGDP? One wants to avoid using sim-ple, naïve precrisis trends since expectations and nominal contract-ing do eventually adjust. Figure 2 illustrates the problem with suchtrends for the United States and Spain. If they were taken seriously,then there would be a 15 percent shortfall of aggregate nominalexpenditures in the United States and a 45 percent shortfall inSpain as of 2018:Q2. Figure 2 reports another measure, the “stickyforecast” path of NGDP outlined in Beckworth (2018) and it showsa gradual adjustment so that the expected path of NGDP and actualNGDP eventually converge. This measure is more consistent withthe notion of expectations and nominal contracting eventuallyadjusting to sustained changes in NGDP. This sticky forecast isused in this paper as the expected growth path of NGDP for21 advanced economies and its motivation and construction is out-lined below.

10Niskanen (2001) and Hendrickson (2012) make the case that the Fed was effec-tively targeting a stable growth path for nominal demand during the post–PaulVolcker period up until the early 2000s. This period of stable nominal demandgrowth and relative financial stability lends support to the arguments of thisarticle.

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Sticky Forecast Path for NGDPThe idea behind the sticky forecast path for NGDP is twofold.

First, the public makes many economic decisions based on a forecastof their nominal incomes. For example, households may take out a30-year mortgage based on an implicit forecast of their nominalincome over this horizon. The actual realization of nominal incomemay turn out to be very different than expected, but the householdsmay not be able to quickly adjust their plans given sticky debt con-tracts and other commitments that constrain them. Therefore, theconsequences of previous forecasts are often binding on them andslow to change even if their nominal income forecasts have beenupdated. Second, in addition to these old forecasts and decisionswhose influence lingers, new forecasts and new decisions are beingmade each quarter for subsequent periods that will also have linger-ing effects. Together, this means future periods have many overlap-ping and different forecasts applied to them that only graduallyadjust.

To capture this sticky forecast idea, a five-year forecast is cre-ated that gradually updates over time. Five years are chosen sinceit is assumed that all constraints created by decisions based on the

FIGURE 2Simple NGDP Trends versus Sticky Forecast

NGDP Paths

NGDP Sticky Forecast1995–2017 Trend

2015201020052000

2015

2012

2009

2018

2006

2003

2000

Trilli

onso

fDoll

ars

Billio

nsof

Euro

s

8

2422201816141210

U.S. Nominal GDP Spain Nominal GDP

100

200250300350400450500550600

150

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forecast can be fully unwound within five years. The data for thisexercise come from the IMF’s World Economic Outlook (WEO)forecast database. Every spring and fall there are WEO forecastspublished for member countries that extend six years out. Thesebiannual forecasts are interpolated to a quarterly frequency andused here to construct a sequence of five-year overlapping fore-casts for every period between 2000:Q1 to 2018:Q2. This processis done for 21 advanced economies: Australia, Austria, Belgium,Canada, Denmark, Finland, France, Germany, Greece, Israel,Italy, Japan, Netherlands, New Zealand, Portugal, South Korea,Spain, Sweden, Switzerland, United Kingdom, and the UnitedStates.11

The exact steps are as follows. First, for every quarter beginning in1995, a five-year forecast (20 quarters) is created using the IMF’sforecasts of NGDP growth.12 Second, for a given starting period,these NGDP growth forecasts are then used to create a 20-quarterforecast path of the NGDP level in national currency form. Theseforecasts are created for every period up to 2008:Q2.

Third, the next step is to recognize that starting with 2000:Q1there are 20 overlapping NGDP level forecasts in national currencyfor every quarter. All of these 20 forecasts are averaged into oneNGDP level value for each period as follows:

(4) NGDPtsticky forecast W .

This process is repeated for every forecasted period so that a newNGDP level forecast time series is created. This new time series isused as the sticky forecast NGDP growth path. Figure 3 shows theactual and sticky forecast paths for NGDP in the 21 advancedeconomies in their national currency. There is a diverse set of NGDPexperiences in Figure 3, but it is misleading to compare across coun-tries the actual and sticky forecast NGDP levels since absolute sizematters. This paper, consequently, looks at the percentage point dif-ference between the actual and sticky forecast NGDP levels, calledhereafter the “NGDP Gap.”

�iW120 NGDPt^i

IMF forecast (t)

20

11For South Korea and Japan, the forecast is set at 2.5 years since these two coun-tries’ forecasts were found to converge much faster.12The IMF provides forecasts for real GDP growth and inflation. These are com-bined to create an NGDP growth forecast.

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Figure 4 reports the average NGDP gap for the crisis years of2008:Q1–2013:Q4 ranked by size. Most countries had a negativeNGDP gap during this time, indicating NGDP was on averagebelow expected values in most advanced economies. Greece had thelargest average NGDP gap at ^14.9 percent, followed by Spain at^8.7 percent and Portugal at ^6.5 percent. The best performersturned out to be Israel and Australia both with an NGDP gap of1.6 percent followed by Switzerland at 0.2 percent. The risk sharingtheory of NGDP outlined by Koenig (2013), Sheedy (2014),Azariadis et al. (2016), and Bullard and DiCecio (2018) impliesthese NGDP Gap differences among the 21 countries should be sys-tematically related to financial stability. This claim is tested in thenext section.

FIGURE 3Actual and Sticky Forecast Paths for NGDP

(In National Currency)

2012 20152009200620032000100

140

180

Netherlands

Bill

ions

2012 20152009200620032000150

250

200

300

Spain

Bill

ions

2012 20152009200620032000110

130

150

170

Switzerland

Bill

ions

2012 2015200920062003200030.0

40.0

50.0

35.0

45.0

Portugal

Bill

ions

2012 20152009200620032000500700

1100900

1300Sweden

Bill

ions

2012 2015200920062003200020

40

60

80New Zealand

Bill

ions

2012 201520092006200320008

12

16

20

United States

Tril

lions

2012 20152009200620032000480

520500

540560

Japan

Tril

lions

2012 20152009200620032000250

350

450

550United Kingdom

Bill

ions

Actual NGDP Sticky Forecast Path

2012 20152009200620032000150

250

350

450

Australia

Bill

ions

2012 20152009200620032000250

350

450

550

Canada

Bill

ions

2012 20152009200620032000350

450

550

France

Bill

ions

2012 20152009200620032000125

275225175

325

Israel

Bill

ions

2012 20152009200620032000300

400

500

600Denmark

Bill

ions

2012 2015200920062003200030

40

50

60Finland

Bill

ions

2012 20152009200620032000304050

7060

Greece

Bill

ions

2012 2015200920062003200060

80

100

120Belgium

Bill

ions

2012 2015200920062003200050

70

90

Austria

Bill

ions

2012 20152009200620032000500

600

700

800

Germany

Bill

ions

2012 20152009200620032000300

340

380

420

Italy

Bill

ions

2012 20152009200620032000150

250

350

450

Korea

Tril

lions

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Empirical Evidence for NGDP and Financial StabilityThis section gets to the main objective of this article: to empir-

ically assess the policy proposal to use NGDP as a way toimprove financial stability. As noted earlier, it does so by exploit-ing an implication of the theory: those countries whose NGDPstayed closest to its expected precrisis growth path—and there-fore kept risk more evenly spread between debtors andcreditors—should have experienced the least financial instabil-ity. This section of the article tests this claim in two parts. First,it looks at series of scatterplots to see if there is any systematicrelationship between the NGDP gap and measures of financialstability. Second, it then uses the same variables in a panel vec-tor autoregression (VAR) and panel local projection model tobetter test for causality.

FIGURE 4Average NGDP Gap

(2008:Q1–2013:Q4)

0%–15% –10% –5%–20%

Israel

SpainPortugal

ItalyUnited States

NetherlandsUnited Kingdom

FinlandFrance

DenmarkSwedenCanada

JapanSouth Korea

New ZealandAustria

BelgiumGermany

SwitzerlandAustralia

Greece

5%

Note: The NGDP Gap is the percentage point difference between theactual and sticky forecast paths for NGDP.

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Scatterplot Analysis

As a first look at the potential relationship between NGDP andfinancial stability, this section plots in Figure 5 the average NGDPgap over the crisis period of 2008:Q1–2013:Q4 against six financialmeasures over the same period: private credit growth, M3 moneysupply growth, stock price growth, home price growth, the nonper-forming loan (NPL) rate, and the equity risk premium. Details on thesources of these measures are found in the data appendix. To beclear, these scatterplots are not intended to establish causality.Instead, they are provided to establish whether there is any system-atic relationship between NGDP forecasting errors and the financialvariables.

One issue is whether to treat Greece as a legitimate observationor an outlier given the severity of its experience during this time.On one hand, Greece can be viewed as part of the same data-generating process as other countries but it just happened to receivethe largest “treatment” of NGDP forecasting errors. In this case,including Greece is important since its absence could result inbiased estimates. On the other hand, maybe Greece does comefrom a different data-generating process and should be consideredan outlier. To account for this possibility, the scatterplots are shownwith fitted lines and R2 for the full sample and for the sampleexcluding Greece.

The first scatterplot in the figure shows the change in the averageyear-over-year growth rate of credit to the private nonfinancial sector(PNFS) against the NGDP gap. The change is the differencebetween the average PNFS credit growth rate in 2003:Q1–2007:Q4and in 2008:Q1–2013:Q4. That is, the change in the average creditgrowth rate between the boom and crisis years. The first scatterplotshows there is a fairly strong and positive relationship with an R2 of48 percent when all countries are included. Without Greece, the R2

is still a robust 38 percent. These results mean the larger the declinein the NGDP gap, the greater the decline in the average growth rateof PNFS credit during the crisis years.

The second scatterplot reveals a similar positive relationship forthe year-over-year M3 money supply growth rate. Here the R2 is58 percent for the full sample and 56 percent without Greece. Heretoo, then, the figure indicates a strong positive relationship betweenthe NGDP gap and the growth in the M3 money supply.

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–20%

–15%

–10% –5

% 0% 5%

.Av

g.Pr

ivate

Cred

itGro

wth

Rate

2008

–201

3Av

g.m

inus2

003–

2007

Avg.

–22%

3%

–2%

–7%

–12%

–17%

NGDP Gap and Private Credit Growth

Average NGDP Forecast Error:2008–2013

–20%

–15%

–10% –5

% 0% 5%

Aver

age

M3

Grow

thRa

te20

08–2

013

Aver

age

0%0%0%0%0%

1%1%1%

1%

NGDP Gap and M3 MoneySupply Growth


ESPPRT

AUS

AUS

GRC

AUS

ISRCHE

GBR

FIN

SWE

NLD

ITA

DEUDNKAUT

FRAESP

JPNUSA

JPNJPN

USA

USAAUS

BEL

GRCGRC

GRC

PRTPRT

ITA JPN

JPNFRA

GBRSWE

FRA

GBR

KOR

KOR

BELBEL

ISRISR NZL

NZL

DEU

DEU

AUSAUSCAN

CHECHE

AUT

AUT

CANDNK

DNK

FINFIN USA

USA

NLDNLD

ESPESP

ITA

ISR

DEU

AUT

AUT

FRA

FRA

BEL

PRT

ITA FIN

FIN

PRTPRTESP

ESP GRC

DNKGBRNZL

NZL

USAGRC

AUTCAN

CAN NZL

JPN DEU CHE

CHE

ISR

ISRDEU

SWEDNK

KOR

KOR

KOR

GBR

GBR

NLD

NLD

CHESWE

SWE

DNK

NZL

KORCANCAN

SWEBEL

BEL

NLD

ITA

ITA

FINFRA

–20%

–15%

–10% –5% 0% 5%

Aver

age

Stoc

kPric

eGr

owth

Rate

2008

–201

3Av

erag

e

–20%

10%

0%

5%

–5%

–10%

–15%

NGDP Gap and Stock Price Growth


–20%

–15%

–10% –5% 0% 5%

Aver

age

Hom

ePr

iceGr

owth

Rate

2008

–201

3Av

erag

e

0%

–5%

–10%

–15%

10%

5%

15%NGDP Gap and Home Price Growth


–20%

–15%

–10% –5

% 0% 5%

NPL

as%

ofGr

ossL

oans

2008

–201

3Av

erag

e

–2%

16%14%12%10%

8%6%4%2%0%

NGDP Gap and NonperformingLoans (NPLs)


–20%

–15%

–10% –5

% 0% 5%

Equit

yRisk

Prem

ium20

08–2

013

Aver

age

10%9%8%7%6%5%4%

13%12%11%

14%

NGDP Gap and Equity Risk Premium


All CountriesR2 = 55.98%

Without GreeceR2 = 9.73%











FIGURE 5NGDP Gap and Financial Conditions

(2008:Q1–2013:Q4)

Note: Data sources listed in the Appendix.

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The third scatterplot displays the relationship between the year-over-year growth rate in stock prices and the NGDP gap. Here again,there is a strong positive relationship between the size of the NGDPgap and the growth in stock prices in the full sample with an R2 of54 percent. The relationship weakens a bit, but remains nontrivial insize with an R2 of 23 percent in the absence of Greece. In general,the larger the decline in the NGPD gap the greater the decline in thisasset price.

The fourth scatterplot shows the relationship between the year-over-year growth in home prices and the NGDP gap. This relation-ship is also a strong positive one with an R2 of 64 percent for the fullsample. Excluding Greece actually leads to a stronger fit with an R2

of 69 percent. This is another asset price that is strongly related to theNGDP gap during the crisis.

The fifth scatterplot reveals the relationship between nonperform-ing loans as a percent of gross loans against the NGDP gap. Nowthere is a strong negative relationship, indicating that as the NGDPgap declines the rate of nonperforming loans increases. The R2 hereis 61 percent for the full sample and 30 percent excluding Greece.Nonperforming loans also appear to be robustly related to theNGDP gap.

Finally, the sixth scatterplot displays the relationship betweenthe equity risk premium and the NGDP gap. Here, there is astrong negative relationship for the full sample with an R2 of56 percent, indicating that as the NGDP gap gets larger the equityrisk premium rises. The R2, however, shrinks to 10 percent whenGreece is excluded. This may be the one case where Greece is anoutlier.

Figure 6 shows the NGDP gap was also systematically related toyear-over-year real GDP growth and the unemployment rate duringthis time. It was less related, however, to the year-on-year inflationrate. There is a stronger fit, though, between the NGDP gap and thechange in trend inflation between the 2008–2013 and 2003–2007periods.

These scatterplots, therefore, indicate that in most cases there wasa strong systematic relationship between the NGDP gap and finan-cial and economic instability. Moreover, since most countries experi-enced persistent NGDP forecast errors during this period, one canview macroeconomic policy as failing to provide on a sustained basissufficient nominal demand growth and therefore was an exogenous

435


FIGURE 6NGDP Gap and Macroeconomic Indicators

(2008:Q1–2013:Q4)

Note: Data sources listed in the Appendix.

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contributor to this relationship. Put differently, it seems plausiblethat a meaningful portion of causality flowed from NGDP forecasterrors to financial variables in these scatterplots. Still, the scatterplotsonly establish a relationship. The next section attempts to establishcausality.

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Panel VAR

To better tease out causality, this section estimates a panel vectorautoregression. A VAR is an estimated system of endogenous vari-ables that provides a dynamic forecast. The forecast can be used toidentify nonforecasted movements or innovations to variables in thesystem. These innovations coupled with identification restrictions onthe data create exogenously identified shocks to variables of interest.Here, that variable of interest is the NGDP gap.

The VAR is estimated on the data for all 21 countries using quar-terly data over the entire sample of 2000:Q1 to 2018:Q2. This largersample is used to avoid degrees of freedom problems that arise usingthe shorter sample period of the crisis. Moreover, the theory appliesto boom periods as much as it does to bust periods since any devia-tion of NGDP from its expected growth path should affect the distri-bution of risk between debtors and creditors.

Since this is panel data, a panel VAR is estimated that controls forindividual country fixed-effects. This feature means unobservedcountry-specific heterogeneity that is fixed over the sample will notaffect the estimates. Greece, therefore, should not be a problem forthese estimates.

A parsimonious panel VAR is estimated that has three core macro-economic variables—the NGDP gap, real GDP growth, and theunemployment rate—and a financial variable as its endogenousvariables:

(5) zi,t W ( (py) i,tgap, .yi,t, ui,t, fi,t)�.

Here, zi,t is the vector of endogenous variables, (py)i,tgap is the

NGDP gap, .yi,t is the year-over-year growth rate in real GDP, ui,t

is the unemployment rate, and fi,t is one of the six financial variables.The subscripts i and t and represent country i and time period t. Thismodel is estimated six times with a separate financial variable fillingthe fi,t slot each time. The model is also estimated an additional timewith inflation filling the fi,t slot.13 Four lags are used in the estimatedmodel and a Choleski identification scheme is imposed on the data.

13The impulse response functions (IRFs) of the core macroeconomic variables donot materially change with the change in the financial variables.

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Given the ordering of the variables, the Choleski identificationmeans the (py) i,t

gap shock is exogenous to all other variables in theshort-run. This allows for impulse response functions (IRFs), whichshow the typical dynamic response of the variables in the VAR to anexogenous shock to the NGDP gap. The shock to the NGDP gap isset to a negative 1 unit shock. The resulting IRFs are reported inFigure 7.

The top row of Figure 7 shows the IRFs for the credit to the pri-vate nonfinancial sector and the M3 money supply both in year-over-year growth rate form. The negative 1 unit shock to the NGDP gapcauses both to respond in a similar fashion: they slowly decline fornine quarters and then slowly begin recovering. They are still recov-ering 14 quarters after the shock. The maximum decline in the pri-vate credit growth rate is 0.94 percent and for the M3 growth rate itis 0.89 percent.

The second row of Figure 7 reveals the IRFs for stock and homeprice year-over-year growth rates. The stock price growth ratedeclines through three quarters and hits a peak decline of 3.9 per-cent. The home price growth rate stays depressed over the entireIRF and averages a 0.58 percent decline.

The third row of Figure 7 displays the IRFs for the nonperform-ing loan rate and the equity risk premium. They both slowly rise overthe entire IRF. The nonperforming loan rate tops out at 0.49 percentand the equity risk premium reaches 0.57 percent. Unlike the scat-terplots, the equity risk premium remains significant in the IRFs.

The last two rows of Figure 7 show the response of the macro-economic variables. The real GDP growth rate declines to about^1 percent through quarter four and then recovers relatively quickly.The unemployment rate, on the other hand, rises through quartereight, peaking with a 0.57 percent gain, and begins a slow recovery.The inflation IRF indicates there is no link between it and theNGDP gap shock. This is consistent with the weak relationship inscatterplots and may reflect the successful anchoring of inflation bycentral banks. Finally, the NGDP gap is shown to start recovering inquarter three from its own shock.

The panel VAR IRFs, therefore, collectively point to a strongcausal role for NGDP shocks in creating financial and economicinstability. These findings, therefore, provide empirical support forthe proposal to use NGDP targeting as a means to deal with concernsover household debt and financial volatility.

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FIGURE 7Panel VAR IRF from Negative Unit Shock

to the NGDP Gap(2000:Q1–2018:Q2)

10 12 148642–1.20

–0.80

–0.40

0.00Private Credit Growth

Perc

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10 12 148642–1.20

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Perc

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–3.00

1.00

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Perc

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–0.40

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0.05

0.25

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0.35Equity Risk Premium

Perc

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0.20

0.60Unemployment Rate

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Note: The impulse response functions (IRFs) are based on an estimatedfixed effect panel VAR model of the 21 advanced-economy countries notedin the text.

FIGURE 7Panel VAR IRF from Negative Unit Shock

to the NGDP Gap(2000:Q1–2018:Q2) (Continued)

Panel Local Projection Model

One criticism of the Panel VAR is that it applies some structure tothe data via the Choleski identification scheme. The data is thereforenot strictly “speaking for itself.” As a cross check, then, this sectionreports IRFs from Jordà’s (2005) local projection method that are notsubject to this critique. Moreover, the local projection method allowsfor nonlinearities and provides a more direct estimate of dynamiccausal effects. In addition, the local projection like the VAR is appliedusing panel data and fixed effects so that unobserved country-specificheterogeneity is controlled for in the regressions. Greece, therefore,should not be a problem for these estimates.

The panel local projection approach entails estimating h regres-sions of the form,

(6) fi,t_h W ! _ �h(py) i,tgap

_ �j,h (py)gapi,t^j _ �j,h .yi,t^j

_ πj,h ui,t^j _ lj,h fi,t^j _ �i,hDi _ <i,t,h

J

�jW1

J

�jW1

J

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J

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Quarters after Shock Quarters after Shock10 12 148642

–0.25

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–0.15

0.15

0.05

Inflation

Perc

ent

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–0.50

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–0.25NGDP Gap

Perc

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Point Estimates 95% SE Bands

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where h is the number of quarters ahead, j is the number of lags,�i,hDi are country fixed effects, and �,�,π,l are parameter estimateson the same lagged control variables used in the panel VAR. Likebefore, the fi represents a placeholder for the financial and inflationvariables.14 Also like before, four lags are used for J.

This panel local projection regression is estimated for all the vari-ables for h W 0,. . .,14. That is, regressions at each h horizon are esti-mated with the parameter of interest being �h. This parameterestimates the direct effect of the NGDP gap at time t on the othervariables at time t _ h. Unlike the panel VAR, the local projectionregression imposes no structure on the data and allows the data tospeak for itself.

The lagged control variables are included to help keep �h esti-mates unbiased. However, in the regressions with small h there maystill be some simultaneity bias. But as h gets larger it is harder toargue endogeneity is a problem.15

The local projection IRFs are created by plotting the point esti-mates for �h and the accompanying 95 percent clustered standarderror (SE) bands. These IRFs are reported in Figure 8 for all thevariables following a negative 1-unit shock to the NGDP gap.

Figure 8 reveals the local projections IRFs are very similar to thePanel VAR IRFs. The top row of Figure 8 shows the IRFs for thecredit to the private nonfinancial sector growth rate and the M3money supply growth rate similarly decline for nine quarters beforeslowly recovering. The magnitudes are also similar with a maximumdecline in the private credit growth rate of 0.93 percent and a declinein the M3 growth rate of 0.78 percent.

The second row of Figure 8 also shows similar IRFs for stock andhome price growth rates. The stock price growth rate declinesthrough three quarters and hits a peak decline of 3.6 percent. Thehome price growth rate also stays depressed over the entire IRF andaverages a 0.83 percent decline.

14Here, fi also serves as placeholder for the core macroeconomic variables whenthey are run as the dependent variable. When this happens, the l control vari-ables fall away since the lagged macroeconomic variables are provided in the �,�,and π control variables.15It seems implausible, for example, that the NGDP gap shock at period t—whichitself is a forecast error—is caused by the year-on-year growth rate of stock prices14 quarters in the future.

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FIGURE 8Local Projection IRF from

Negative Unit Shock to the NGDP Gap(2000:Q1–2018:Q2)

10 12 148642–1.50

–1.00

–0.50

0.00

Private Credit Growth

Perc

ent

10 12 148642–1.20

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Perc

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4.00Stock Price Growth

Perc

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–0.60

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0.20

Real GDP Growth

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–0.25

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0.50Equity Risk Premium

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10 12 1486420.00

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0.80Unemployment Rate

Perc

ent

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The third row of Figure 8 displays the IRFs for the nonperform-ing loan rate and the equity risk premium. The point estimates areagain very similar to the panel VAR IRFs, though the standard errorbands are much larger for the local projection IRFs.

The fourth row reveals very similar IRFs for the macroeconomicvariables. Real GDP growth and the unemployment rate change bysimilar amounts, inflation remains insignificant, and the NGDP gaprecovers rather briskly.

The local projection IRFs, therefore, tell the same story as thePanel VAR IRFs: a negative NGDP gap shock appears to causallyaffect the financial and macroeconomic variables in an adversemanner. Only inflation is left unscathed. Once again, then, the evi-dence points to a strong causal role for NGDP in promoting financialand economic stability.

ConclusionNGDP level targeting (NGDPLT) has received increased atten-

tion over the past decade for various reasons. Some see it as the nextstep in the evolution of monetary policy regimes since it avoids much

FIGURE 8Local Projection IRF from

Negative Unit Shock to the NGDP Gap(2000:Q1–2018:Q2) (Continued)

Note: The impulse response functions (IRFs) are based on an estimatedfixed effect panel VAR model of the 21 advanced-economy countries notedin the text.

Quarters after Shock Quarters after Shock10 12 148642

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–0.20

0.20

0.00

Inflation

Perc

ent

10 12 148642–1.60

–1.20

–0.40

–0.80

0.00NGDP Gap

Perc

ent

Point Estimates 95% SE Bands

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of the confusion inherent to inflation targeting (Frankel 2012;Beckworth 2014; Sumner 2011, 2014; Garin, Lester, and Sims 2016).Others have made the case for NGDPLT based on the desirablecommitment properties its creates in the face of a zero lower bound(ZLB) environment (Woodford 2012; Summers 2018). NGDPLTcan similarly be seen as a velocity-adjusted money supply target thatis effective in escaping the ZLB (Belongia and Ireland 2015, 2017).Finally, some see NGDPLT as a workaround to the knowledgeproblem in monetary policy. There is no need to have real-timeknowledge of natural-rate variables in this framework (McCallum2011; Beckworth 2017; Beckworth and Hendrickson 2019).

These more traditional cases being made for NGDPLT can nowbe bolstered by the risk-sharing argument for it. That is, a monetaryregime that targets the growth path of NGDP can be shown to repro-duce the distribution of risk that would exist if there were widespreaduse of state-contingent debt securities (Koenig 2013; Sheedy 2014;Azariadis et al., 2016, Bullard and DiCecio 2018). The idea behindthis view is that the countercyclical inflation created by an NGDPLTwill cause real debt burdens to change in a procyclical manner. Thistendency, in turn, will cause debtors to benefit during recessions andcreditors to benefit during booms. Put differently, an NGDPLT willcause fixed nominal-priced loans to act more like equity than debt.

This article provided an indirect empirical assessment of this risk-sharing view of NGDP. It did so by first constructing an NGDP gapmeasure and checking whether it was systematically related to vari-ous measures of financial stability. The paper then used a panel VARand a panel local projection model to determine if causality ran fromNGDP shocks to financial stability. The results from these empiricalexercises strongly suggest that there is a meaningful causal role forNGDP in promoting financial and economic stability.

These findings are only a first look at the NGDP–financial stabil-ity relationship. Hopefully, they will spur further research on thisissue and help inform the discussion at the Federal Reserve and else-where on the best monetary policy regime for advanced economies.

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The Financial Stability Case for a Nominal GDP Target · 2019. 5. 10. · 423 Case for a Nominal...

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Transcript of The Financial Stability Case for a Nominal GDP Target · 2019. 5. 10. · 423 Case for a Nominal...