Discussion Paper No.411
Is Inﬂation Fiscally Determined?

Lamia Bazzaoui

2019 / 07 / 31

TOHOKU ECONOMICS RESEARCH GROUP
Discussion Paper

GRADUATE SCHOOL OF ECONOMICS AND

MANAGEMENT TOHOKU UNIVERSITY

27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANIs Inflation Fiscally Determined?*
Lamia Bazzaoui
July 31, 2019
Abstract
The aim of the present paper is to study the relationship between
fiscal variables and the inflation rate for a sample of countries, overthe period 1960-2017, based on a linearized equation derived from ahouseholds ’budget constraint. This equation links the inflation rate
to both fiscal and monetary policy, in addition to the growth rate. We
follow the same approach as Cochrane (2019a) and analyze the impact
of shocks to these three variables. Impacts from fiscal and monetary
policy to the inflation rate are then decomposed between different
monetary policy regimes and fiscal space categories. Results indicatethat the short-term interest rate is the most significant determinantof inflation. Fiscal policy also affects the inflation rate negatively
through the fiscal balance, but this effect is not robust across all monetary policy frameworks (this relation only holds in unstructured orloosely structured discretionary monetary policy regimes). The variable of fiscal space on the other hand proves to be an important factor
as inflation appears to be more sensitive to both fiscal and monetary
policy when fiscal space is limited. Finally, we also find that, as predicted by Sargent & Wallace (1981), fiscal policy can cause inflation
only when the growth rate is lower than the interest rate.
Key words: Inflation, fiscal, monetary policy, public debt, Panel
VAR GMM* We gratefully acknowledge helpful comments by Prof. Jun Nagayasu (Tohoku Univer-sity), Professors Shin-Ichi Nishiyama (Kobe University), Ryuzo Miyao (Tokyo University),
Tsutomu Miyagawa (Gakushuin University), Ad Van Riet (United Nations University),
Jaimie W. Lien (Chinese University of Hong Kong) and Yang Li (Nankai University). This
paper also benefited from comments by seminar participants at the 5th HenU / INFER
Workshop on Applied Macroeconomics, the 2019 spring meeting of the Japan Society ofMonetary Economics and the WEAI 94th annual conference. We are also grateful to theSunaga Fund for financial support.
 Corresponding author- E-mail address: lamia@bazzaoui.com

1

Introduction

As public debt has been significantly increasing in the recent decades, ensuing macroeconomic imbalances, and more particularly the effects on theprice level, have become a source of public concern. Nonetheless, fears of an
inflation outbreak resulting from high indebtedness can only be justified if
the existence of a relationship between inflation and fiscal policy is verified.
In the prevailing literature, inflation is usually linked to monetary policy factors. For example, the monetarist view suggests that inflation is the resultof too much money chasing too few goods. But this view has been criticized as the relationship between money growth and inflation is not always
empirically verified (e.g. Teles et al. (2016)). In addition, the link between
inflation and the demand for money is becoming more questionable as weare moving towards electronic transactions and a moneyless economy while
prices remain stable.
Among other factors that are usually considered, we also find monetary
policy. Recent low levels of inflation are indeed thought to be the result ofchanging approaches to monetary policy, which, ever since the 1980s, takes
into account a broader range of factors aside from money supply and uses
other instruments in addition to open market operations. The list of nominal anchors has therefore been extended to include exchange rate targets,
inflation targets through the long-run CPI and short-term interest rates on
the short-run (based on the Fisher effect equation). The Taylor rule, as one
method of inflation targeting was found to express accurately the monetary
policy of several economies, even those such as the United States that have1

not officially adopted an IT approach (Clarida et al. (2000)). Still, recently,
this perspective has gotten challenged especially with the long-lasting zerobound episodes. Furthermore, the difficulty of some central banks to reach
their inflation targets and the inability of many dynamic stochastic generalequilibrium (DSGE) models to forecast the recent crisis called into question
their effectiveness to provide efficient policy tools. After the financial crisis, there is a large body of work under way to model financial frictions andinclude the zero lower bound (ZLB) within the baseline DSGE framework(Gust et al. (2017)).
Fiscal determinacy of the price level, on the other hand, has not always
been successfully demonstrated. The first pioneering theoretical contribution
in this respect is Sargent & Wallace (1981). These authors stressed the factthat governments running persistent deficits should sooner or later have tofinance those deficits through money creation (seigniorage), thus producing
higher inflation. This research paved the way for several works, such as
those on the fiscal theory of monetary policy. Although this theory does
not exclude the impact of monetary variables, it also posits that, if futuresurpluses fail to adjust to growing public debt (through more taxes or lessexpenditures), then the price level must jump to ensure satisfaction of thepresent value government debt equation. Empirical work, however, has had
little success in uncovering a strong and statistically significant connection
between fiscal policy and inflation.
The aim of this paper is to study the link between fiscal variables and theinflation rate for a sample of 46 countries, over the period 1960-2017, basedon a linearized equation derived from a household’s budget constraint. This2equation links the inflation rate to both fiscal and monetary policy variables,in addition to the growth rate. We follow the same approach as Cochrane(2019a) and analyze the impact of shocks to these three variables. Impact
from fiscal and monetary policy to the inflation rate is then decomposed
between different monetary policy regimes and fiscal space categories.
Results indicate that the short-term interest rate is the most significant
determinant of inflation. Fiscal policy also affects the inflation rate negatively through the fiscal balance, but this relation is not robust across all
monetary policy regimes. The variable of fiscal space also proves to be an
important factor as inflation appears to be more easily affected by both fiscal
and monetary policy when fiscal space is small. Finally, we also find that, aspredicted by Sargent & Wallace (1981), fiscal policy can cause inflation only
when the growth rate is lower than the interest rate.

2

Literature review

2.1Sargent & Wallace (1981)

The first pionnering contribution that established a link between fiscal policy
and the price level is that of Sargent & Wallace (1981). These authors derived fiscal determinacy of prices from the government intertemporal budget
constraint, expressed as follows:

Gt + it−1 Bt−1 = Tt + (Bt − Bt−1 ) + (Ht − Ht−1 )3(1)with Gt being government expenditures on goods, services, and transfers andit−1 Bt−1 expressing interest payments on the total outstanding debt, Tt the
tax revenues, Bt − Bt−1 new issues of interest-bearing debt.
This equation shows that government expenditures can be funded byeither taxes, new issued debt, or by printing new currency (expressed by thechange in outstanding stock of non-interest bearing debt Ht − Ht−1 ). Basedon this relation, Sargent & Wallace (1981) provided the following expressionof real government deficits Dt as

Dt = (Ht − Ht−1 )/pt + (Bt − Bt−1 )/(1 + Rt−1 )(2)with pt the price level at t, and Rt−1 the real interest rate on government
bonds between two periods.
From this relation, they derived an expression of the inflation rate depending on the stock of interest bearing government debt per capita. Thismodel is based on the assumption that fiscal policy dominates monetary policy. That is, the fiscal authority independently sets its budget, announcing
current and future deficits and surpluses. And the amount of revenue to raiseis then determined based on those decisions. The monetary authority thenfinances with seignorage any discrepancy between the revenue demanded by
the fiscal authority and the amount of bonds that can be sold to the public.
Also, using the assumption that interest rates on bonds are greater than theeconomy's growth rate, authors concluded the following: the real stock ofbonds will keep growing faster than the size of the economy. But since thedemand for bonds places an upper limit on the stock of bonds, eventually4

financing of principal and interest will be made through seignorage. In otherwords, decisions of the fiscal authority can induce more money printing andtherefore inflation.

2.2

The government debt valuation equation

As a basis of the fiscal determinacy of prices, the following government
debt valuation equation has been suggested by several authors (Sims (1994),
Woodford (1994), Woodford (1995),Woodford (1998)).
Nominal government debt/price level =Expected present value of future primary surplusesOr∞
Bt−1 X=Et (mt,t+j st+j )
pt
j=0

(3)

with Bt−1 = one-period nominal debt issued at t − 1 due at t, pt = price level,st =real primary government surplus including seignorage, mt,t+j = a discountfactor.
In some cases, this equation has been interpreted as an equilibrium condition resulting from the intertemporal government budget constraint. But
in other contributions, such as Cochrane (1998, 2005), this relation has been
derived as a valuation equation for government bonds seen as securities. The
underlying idea is that nominal debt, including the monetary base, is a claim
to future government primary surpluses (in the same way a stock is a claim
to future earnings). As underlined in Cochrane (2018), bonds prices are determined by the market depending on bond yields and on future streams of
expected primary surpluses.
52.3

Models of the fiscal theory of monetary policy

In a more recent contribution, Cochrane (2019a) provided a study of the fiscalroots of inflation based on a linearized identity, showing that unexpected
inflation less the unexpected nominal return on government bonds must equal
the innovation in the sum of futures surplus to GDP less the sum of future
real bond returns. The derived linearized flow identity is as followsnvt + rt+1− πt+1 − gt+1 = st+1 + vt+1

(4)

vt represents the market value of debt, rtn nominal returns on the portfolioof government debt, πt the inflation rate, gt GDP growth, st real primary
surplus, rtn −πt −gt the discount rate for the right-hand side terms. Variables
are expressed in log terms.
The main results of structural shocks analysis are as follows. The disinflation of a recession shock corresponds entirely to a decline in discount rates
leading to higher debt. A positive monetary policy shock (moving interest
rates but not PV of future primary surpluses) is super-Fisherian and raises
inflation immediately. A negative fiscal shock (moving PV of future primary
surpluses but not interest rates) induces a protracted inflation. Quoting theauthor’s conclusion (p. 31) regarding effects of a negative shock to primary
surpluses:
A fiscal shock sets off a protracted inflation. Three quarters of
the fiscal shock is transmitted to future inflation via a decline in6

long-term bond prices.
Finally, Sims (2011) suggested a comprehensive theoretical framework,
based on the idea that even in an active fiscal, passive monetary policy equilibrium, monetary variables still have a powerful effect on both output andinflation. This model includes assumptions of a long-term public debt, aninterest rate target rising with inflation and output, and fiscal surpluses defined by output (pro-cyclical). Different versions of this model were provided:
a simple flexible-price model of an endowment economy, a model with only
short-term government debt and a new-keynesian style model with long-term
debt and sticky prices. This latter version includes the following equations(the derivation and solution of this model are provided by Cochrane (2018)):

M onetary policy :ṙ = −γ (r − ρ̄) + θṗ + ∅ċ + εm

F isher∗ :
IS∗ :

(5)

r = ρ + ṗ

(6)λ̇
ρ = − + ρ̄ + εr
λ

(7)

ȧ
ḃ = −bṗ − b + ab − τ̄ − τaGovernment budget constraint :T erm structure∗ :P hillips curve∗ :F iscal policy :

r =a−

ȧ
a

(8)
(9)

p̈ = β ṗ − δc − εpc(10)τ̇ = wċ + ετ

(11)habit∗ : λ = e−σc + ψ (c̈ − ρ̄ċ) e−c(12)r is the nominal interest rate, ρ is the real interest rate, with steady state7and consumer discount rate ρ̄, p is the log of the price level, c is consumption
which equals output, λ is the marginal utility of consumption, b is the realmarket value of government debt (consisting of nominal perpetuities), a isthe consol rate, τ is the primary surplus, τ̄ is a parameter, it represents thevalue of τ around which the model was linearized. Starred equations areforward-looking, which implies that shocks are white noise with no effect.
Based on this framework, the requirement that the real value of debt
matches the present value of future primary surpluses can be met throughjumps in the interest rates which changes the value of outstanding debt evenin the case of sticky prices. Also, the author underlines the fact that effective
monetary measures to reduce inflation lead to a drop of inflation at first andthen produce exactly the opposite effect after a delay. This has been dubbed“the stepping on a rake“ phenomenon. Evidence was shown through impulse
response functions. On the other hand however, an expansionary fiscal shock
creates a boom in consumption and an upward unanticipated jump in the
inflation rate.
Cochrane (2018) provided the following explanation to reaction of prices
in this model: if the market value of bond prices decreases, following an increase in interest rates for example (due to monetary policy action), withouta change in future surpluses, the price level must adjust. If prices do not
adjust, then the real value of government debt to investors would be greaterthan its real market value. People will therefore try to buy more government
debt and thus less goods and services which will lead to lower aggregate demand and thereby lower prices. The same deflationary force would follow
in case of an increase of the present value of real future primary surpluses.
8A more recent trend in the literature establishes a link between the level of
public indebtedness and the fiscal space.

2.4

Public debt and fiscal space

The most widely used definition of fiscal space is the one provided by Heller(2005) and according to which fiscal space reflects the availability of budgetaryroom that allows a government to provide resources for a desired purpose
without any prejudice to the sustainability of a government’s financial position. These resources can be accessed through specific fiscal policy measures,
but also through various other channels such as borrowing, seignorage, grants
or institutional reforms. Therefore, the definition of fiscal space is closely
linked to a government’s fiscal sustainability and its potential to expand its
financing capacity. In other words, the availability of fiscal space implies
that a government is able to find resources to finance a desired expenditures
program and to service its debt obligations.
Even though fiscal space refers to the same concept in most macroeconomic policy discussions, concrete measures of fiscal space used in the empirical literature differ significantly across the existing studies. One commonlyused approach is the ’fiscal gap’ approach. It is based on the idea of estimating the difference between a given level of public debt or the fiscal balance
and a benchmark level considered as the sustainability level (Ostry et al.
(2010), Ghosh et al. (2013))

Debt sustainablity gap = d∗ − dt

9The sustainable debt level d∗ is estimated in different ways. In somecases, it is considered as the mean level of debt for a given group of countries.
Another approach for estimating d∗ is the signal approach (the debt thresholdthat maximizes the ratio of percentage of correctly classified debt crises intotal crises observations divided by the percentage of falsely classified crises
in non-crises observations), suggested by Kaminsky et al. (1998). In someother cases, a forward-looking approach is used by computing the present
value of future primary balances (International Monetary Fund (2012)), as
in the valuation equation. Such a methodology has been popularized by
Bohn (1998, 2007, 2008). In these studies, fiscal solvency is considered to be
fulfilled when the primary balance reacts positively to an increase in public
debt. This approach derives the relationship between debt and the primarybalance from the intertemporal government budget constraint:

dt+1 − dt = (rt − gt ) dt − pst+1

where dt is one-period debt (as a share of GDP) at the end of the period, g is
the growth rate of real GDP which is assumed to be exogenous and constant,
pst is the primary balance (in percent of GDP), and rt is the real interest
rate on debt contracted in period t and due in period (t+1). Based on thisrelation, the maximum sustainable level of the primary balance is determined
depending on the different interest rate-growth rate (rt − gt ) which differs
across countries.
Some of the criticisms expressed against Bohn’s methodology is that it
does not take into account the endogenous relation between debt and the10interest rate, and it also does not rule out the possibility of an infinitely
increasing debt. These issues are addressed in the models suggested by Ostry et al. (2010) and Ghosh et al. (2013) based on which response of the
primary balance to public debt is nonlinear and the interest payment schedule depends on the debt level. In these models, the interest rate becomes
infinite beyond the debt limit as there is no finite rate that would compensate creditors for the probability of default (which becomes equal to unity).
The level of the primary balance reaction function is determined by countryspecific factors (including trade openness, inflation, current and future age
dependency, commodity prices, fiscal instability, presence of fiscal rules, andinternational influence on fiscal behavior).
The use of the intertemporal government budget constraint is also foundin contributions such as Buiter (1985), Buiter et al. (1993), Blanchard (1990),
Auerbach & Gale (2011) where an index of fiscal sustainability is derived
based on projections on future balances, also based on the macroeconomic
outlook and forecasts of the discount rate. In addition, using the fiscal gap
approach, some publications by the IMF, such as the Fiscal Monitor, present
a measure of fiscal adjustment by country (inverse of fiscal space), defined as
the distance between the 2011 cyclically adjusted primary balance and thatneeded to reduce the general government debt ratio to a sustainable level.1
Finally, Aizenman & Jinjarak (2010) suggested an alternative fiscal spacemeasure called de facto fiscal space, defined as the inverse of the number of1

Equivalent to 60 percent of GDP in advanced economies and to 40 percent of GDP
in emerging economies and low-income countries by 2030 (or to 2012 levels, if these werelower than the 60 and 40 percent benchmarks). For Japan, a net debt target of 80 percent
of GDP is assumed.

11

tax-years needed to repay the debt. This ratio requires the estimation of thede facto tax base corresponding to the realized tax collection averaged acrossmultiple years to smooth for business cycle fluctuations. In Aizenman et al.
(2019), this fiscal space measure is used to examine fiscal cyclicality across alarge sample of countries. Authors find that lower fiscal space coincides withgovernment-spending procyclicality.

2.5

Empirical literature on the fiscal determinacy of
pricesResults of the existing studies about the fiscal policy-inflation relationshipare mitigated. No conclusive evidence was obtained so far in favor of, noragainst the existence of a fiscal determinacy of the price level. Table 1 summarizes some of these contributions. Some of these studies focused on the link
between fiscal deficits and inflation, while others examined the relationshipbetween fiscal surpluses and public debt or used a different approach.
For example, Catao & Terrones (2005) examined the existence of a relationship between inflation and fiscal deficits through a study covering 107
countries over 1960 to 2001, based on a dynamic non-linear model, but could
only verify it for the high-inflation, developing economies. Fischer et al.
(2002) also conducted a study on 94 economies, and concluded that fiscal
deficits are the main drivers of high inflation, but only in high-inflation countries. More specifically, they found out that a reduction in the fiscal balanceby 1 percent of GDP, leads to an increase in the inflation rate by 4.2 percent
in this specific group.

12Bohn (1998) followed a different approach. He examined the U.S. deficit
and debt processes and showed that the primary surplus responds positively
to the debt to GDP ratio. Since evidence was found that the surplus does
adjust, and in a sufficient manner to ensure that the intertemporal government budget constraint holds, the author concluded that the fiscal authority
acts in a ricardian fashion. As stated above, this methodology also implies
that a positive adjustment of primary surpluses to public debt indicates theavailability of sufficient fiscal space.
Following Bohn's approach, Bajo-Rubio et al. (2009) used cointegration
analysis between the primary surplus to GDP variable and the public debt
to GDP ratio, complemented with Granger causality tests for a sample of 11
EU countries, over the period 1970-2005. The authors concluded that there
was no clear evidence supporting the fiscal theory of the price level (FTPL),
as primary surpluses responded positively to the debt to GDP ratio in almost
all cases. Other studies, such as Canzoneri et al. (2001), Creel & Le Bihan(2006), reached the same conclusion. Conversely, some others (for example
Favero & Monacelli (2005)) showed the presence of alternated ricardian andnon-ricardian regimes.
There are also empirical studies that led to the validation of the FTPL.
For instance, Loyo (1999) argued that Brazilian policy in the late 1970s andearly 1980s was non-Ricardian and that the FTPL provides a persuasive
explanation for Brazil's high inflation during that time. Tanner & Ramos(2003) found evidence of fiscal dominance for the case of Brazil for some
important periods. Fan et al. (2013) investigated whether the FTPL can
explain UK inflation in the 1970s. They found evidence that fiscal policy
13was non-Ricardian and money growth entirely endogenous in this period.
Their conclusion was that government expenditures were the only driving
force for inflation and therefore that the 1970s inflation outburst resulted
from an increased level of expenditures, unmatched in the previous decades.
Most of these previous studies used the government’s intertemporal budget constraint as a foundation for the choice of variables. For instance, Bohn
(1998) and Canzoneri et al. (2001) based their verifications on the relationship between public debt and the primary balances, using the assumptionthat an adjustment between these two variables would be an indication ofa ricardian regime. In these studies, a regression model between these twovariables served as a basis. However, as pointed out by Bajo-Rubio et al.
(2009), the presence of such a relationship is compatible with both a MD
and a FD regime, since a price increase could be responsible for a fall in the
public debt ratio, indirectly by causing a decrease in the expected value of
future surpluses. To address this issue, the author included Granger causality
tests between primary surpluses and debt, in addition to the cointegration
analysis.
Another way to account for effects of the price level would be using thegovernment debt valuation equation. This is a more direct way of verifying
the fiscal theory of the price level. However it would be difficult to draw
conclusions based on this method, for many reasons: first, the variable ofthe present value of future primary surpluses would be hard to measure andto analyze.2 Second, the choice of the discount rate would have a significant
2

Judgment could be biased by business cycle fluctuations as primary surpluses are likely
to fall during recessions and increase afterwards

14

impact on the obtained results. And in addition, as indicated by Cochrane(2019b), a change in inflation could be the reflection of a change in the discount rate, not a movement in fiscal variables. Finally, another critique toprevious approaches in the empirical literature is that most of them examined fiscal variables exclusively without taking into account interactions with
monetary policy, in a single model,3 as in Sims (2011). In this respect, theapproach used by Cochrane (2019a) is innovative in the sense that it uses
one single linearized relation to examine links between inflation and bothfiscal and monetary variables, in addition to the business cycle (based on USdata).
In the present paper, we use an approach similar to Cochrane (2019a),but with a different derivation of the linear relation of inflation, using a
consumer’s budget constraint, and variables expressed in real terms. Also,
as opposed to the methodology used in that study, we do not derive the valueof primary surpluses from the linear identity but instead use actual data toconstruct a panel VAR Model. We then examine effects of a fiscal policy
shock, a monetary policy shock and a recessionary shock on our variables,
for a sample of 46 countries over the period 1960-2017. Finally, we further
extend our study by examining how the choice of a monetary policy regime,
and how the level of fiscal space affect the links between our variables.
3

In studies related to switching regimes, often two separate models are considered.

15

3Theoretical model

Based on a theoretical framework similar to Woodford (1994), we use thefollowing consumer’s budget constraint (in nominal terms):

pt ct + Bt − Bt−1 = Rt−1 Bt−1 + pt yt − Tt

(13)where pt is the price level, ct consumption, Bt the portfolio of assets (assumed to be consisting of government bonds only), Rt the interest rate on
government bonds, yt the household’s income, and Tt the amount of taxes.
This relation states that the sum of consumption and assets purchases isequal to the sum of interest earnings on assets held from the previous period
in addition to income less tax payment. Assuming that the economy’s global
income yt =gt + ct with gt being government expenditures, we get

Bt − Bt−1 − Rt−1 Bt−1 = Gt − Tt

(14)

For a being the % change of the value of debt, we have Bt = (1 + a) Bt−1 .
Therefore
Bt−1 (a − Rt−1 ) = Gt − Tt = −P St(15)

Because the change in public debt also depends on government bonds yields,
we suppose that the following expression is valid (for a given coefficient k):
a = kRt−1
Therefore
Bt−1 =

P St
Rt−1 (1 − k)
16

(16)

Rescaling by real GDP yt , and with θt being GDP growth, we have
Bt−1
P St
=
yt−1 θtyt Rt−1 (1 − k)(17)

For Byt−1 representing the term Bt−1 rescaled by GDP and P Syt the term
P St rescaled by GDP, we get

Byt−1 =

θt P Syt
Rt−1 (1 − k)(18)

Because the term representing bond yields Rt−1 depends on debt maturity,
if we suppose that there is only one category of bonds with maturity n, theterm structure equation implies that: Rtn = it it+1 it+2 . . . it+n−1 (for it theannualized short term interest rate).
Replacing in Equation (18)θt P Syt
1/n(1 − k)j=t ijByt−1 = Qn

(19)

Dividing by pt
Byt−1
θt
P Syt
= Qn
1/n
pt
(1 − k) pt
j=t ij(20)

With the output-adjusted real primary surplus being psyt = PpStyt and inflationpt
corresponding to πt = pt−1
we getByt−1
θt psyt= Qn
1/n
πt pt−1(1 − k)j=t ij17(21)

Considering that real debt over GDP byt−1 = Byt−1
pt
Then
byt−1
θt psyt= Qn
1/n
πt(1 − k)j=t ij(22)

Taking logs!
nY1log (byt−1 ) − log (πt ) = log(θt ) + log (psyt ) − log
ij − log (1 − k) (23)nj=t
In this equation, the level of inflation appears to be affected by bothfiscal and monetary policy, in addition to growth. As in Cochrane (2019a),
we expect a positive relationship between inflation and public debt, anda negative relationship with the fiscal balance. According to the author’s
model and empirical findings, higher fiscal deficits should indeed lead tohigher inflation. Also response of inflation to the interest rate is assumed to
be positive, consistent with the neo-Fisherian view and conclusions in Sims(2011) and Cochrane (2018).
Finally, we expect a negative relationship between inflation and the GDP
growth rate. This result contradicts the conventional view based on the
keynesian and neo-keynesian framework and according to which this relation
should be positive (AD-AS model, Phillips Curve). However, our assumption
is consistent with the findings of several empirical studies and theoreticalframeworks. First, the model developed by Stockman (1981) establishedthat an increase in the inflation rate results in a lower steady state level of
output, by reducing the purchasing power of money balances, and thereby
the demand for goods and for capital (assuming that a part of investment

18

projects is financed through cash). Second, most money and endogenous
growth models concluded that the inflation rate reduces both the return on
capital and the growth rate on the long-run (see Aratawari et al. (2016),
Haslag (1998), Vaona (2012)).
Empirically, several studies revealed an overall negative effect of inflation
on growth, and many of them also detected the presence of a non-linearity
in this relationship. Kormendi & Meguire (1985) was among the first contributions to shift common beliefs about this relationship (from a positive
to a negative one), based on a study using data for 47 countries over theperiod 1950-1977. Fischer (1993) found that inflation negatively affects output growth through the channels of investment and productivity growth.
These effects were found to be particularly prominent for a high inflation
rate. De Gregorio (1992) also found a negative relationship between the twovariables, using pooled cross-section time series regressions for a large set ofcountries.
Gomme (1993), applied Lucas Jr (1988)’s endogenous growth framework
combined with a cash-in-advance exchange technology. The conclusion of thepaper is that a rise an inflation reduces the marginal value of the last unit ofconsumption of a given period. And because this value is equivalent to thecost of the last unit of work, people are induced to work less. Reduced labor
results in a slower rate of capital accumulation.
Barro et al. (1996) found that a 10 percentage point increase in inflation
results in a 0.2 to 0.3 percentage points reduction in GDP growth (for adataset of over 100 countries). This relationship was however obtained only
after high inflation cases were included in the sample (above 20%). Similarly,
19

based on a dataset of 145 countries over the period 1960-1996, and using a
multivariate panel regression analysis along with numerous robustness checks,
Ghosh & Phillips (1998) found a negative and statistically significant relation
between inflation and growth. Authors noted that on average, a rise of
inflation from 10% to 20% reduces growth by 0.3%-0.4%, and from 20% to
40% by 0.8%. Finally, Andres & Hernando (1997) found a significant negative
effect of inflation on economic growth. They also found that there exists
a nonlinear relationship. Their main policy message stated that reducing
inflation by 1 percent could raise output by between 0.5 and 2.5 percent.

4

Variables of study and general overview ofdataThis study is conducted on a sample of 46 countries (Table 2), for a periodranging from 1960 to 2017. We use yearly data from several sources for thevariables of study specified in the model.

4.1

Variables of study

Because log (1 + x) ≈ x for small values of x, in some cases, variables wereconsidered at level:
Level of Primary Balance/GDP (%): data for primary balances are retrieved from the dataset of Mauro et al. (2013). Missing data are completed from various databases such as the OECD, the World Bank andthe website http://moxlad-staging.herokuapp.com/home/es for Latin
20

American economies.
Log of public debt to GDP: we use the underlying dataset of the paper
Mauro et al. (2013). Missing data are then completed from various
sources such as the website ”tradingeconomics.com” and the Reinhart& Rogoff (2009) database.

GDP growth (%): data of GDP per capita are extracted from the WorldBank database

Short term interest rates: data are collected from several sources, more
specifically from the IFS, Eurostat, the OECD database and in somecases from central banks’ websites. For very large values, log(1+i) is
used.



Inflation rate: data are calculated from the Consumer’s Price Index(with 2010 as the base year), taken from the World Bank database.
Missing data are completed based on the Reinhart & Rogoff (2009)
database. For double-digit values, log(1+inflation) is used instead.

4.2

General overview of data

Yearly evolution of data shows that the level of indebtedness has overallbeen increasing over the recent years. By revenue groups (using the IMF
classification), economies affected the most by this increase are advanced
economies (see Figure 1); whereas the most extreme levels are observed for
low-income developing economies in the 1980s (scaled on the right axis).
Both developing and emerging economies have been deleveraging in the recent
21decade. All sample countries suffered from increasing budget deficits after
the 2007 crisis (Figure 2). But advanced economies have on average beenable to reduce those deficits after 2016.
Inflation levels became very low for all countries after 1995 (see Figure 3,
emerging economies and total average are modeled on right axis). The mostextreme levels have been observed in the 1970s hyperinflation episode, especially for advanced economies. In emerging economies, the maximum levelof inflation is observed for the year 1990 due to very high values noted forArgentina, Brazil and Peru. At the same time, public debt to GDP reached
its highest level in the 1980s. However, in the recent decades, the level of
Public debt to GDP is lower than the high peaks of the 1980s or the year
2002 (Argentina’s debt crisis). Regarding developing economies, excessively
high levels of debt accompanied by high inflation were observed in the 1980s;
whereas in the recent years, the levels of both public debt to GDP and inflation are relatively low.
Correlation coefficients between the five main variables of study for thewhole sample is provided in Table 3. The highest coefficient is the correlation
between interest rates and inflation, with a positive sign (+45%). On theother hand, correlation with the fiscal variables is very weak, negative withthe fiscal balance and positive in the case of public debt. Finally, GDP
growth does not appear to be strongly correlated with any of the variables ofstudy. The highest coefficient is -17% with the variable of public debt overGDP.
Using the model described in the previous section, forecast error variance decomposition of inflation is generated using Cholesky decomposition22

based on an active fiscal policy/passive monetary policy setting. Therefore,
the following ordering of the variables is adopted: the primary balance overGDP (most exogenous), public debt over GDP, GDP growth, short-term interest rates, Inflation. Results clearly indicate that a significant share of
the forecast error variance of inflation can be explained by exogenous shocks
to the interest rates (between 10% at the beginning and 21% at the end of
the observation period) or past inflation (88% at period 1) (see Figure 4).
The share of shocks to the primary balance, although not null, is very small(varies between 1.4% and 2.7%). Therefore fiscal policy does affect inflation
but not to the same extent as monetary policy. The impact of public debt
on inflation is almost absent (around 0.6%).
Using the same model to generate forecast error variance decompositionof public debt (Figure 5), we also do not find evidence of a contribution of
inflation to public debt (limited to 0.5%). Public debt is significantly affected by past debt (around 70%) and a growing contribution of the primarybalance shocks (as expected) (reaching 28.8%), but other variables are insignificant. Interestingly, the variance decomposition of a one-period lagged
public debt shows a more significant contribution of growth (close to 10% at
the 20th period).

4.3

Monetary policy frameworks

As stated in the introduction, some analysts attribute the generalized low
level of inflation to a major change in monetary policy frameworks. This shift23

in monetary policy thinking by setting up rules or targets (money, credit, exchange rate, interest rates, and inflation) aimed at addressing the dynamic
inconsistency issue (Kydland & Prescott (1977), Calvo (1978)). Dynamic inconsistency refers to changes in the decisions of monetary authorities and theabsence of commitment to a single optimal policy. Especially when output
is below the optimal level, monetary authorities have an incentive in moving
away from an announced target in order to generate a “surprise“ inflationand thereby a short-term increase in growth. So it is usually based on theunderlying idea that the relationship between inflation and GDP growth ispositive.
A consensus in this literature is that commitment is a better policy because it generates lower average inflation in the long run (Barro & Gordon(1983), Rogoff (1985)). The gains from commitment are a direct consequenceof the role that expectations play in shaping economic conditions. Morespecifically, Inflation Targeting regimes are thought to have contributed toreduce inflation levels and increase monetary policy credibility.
To verify this assumption we use the classification provided by Cobham(2018) for our sample. The author defines a monetary policy framework as
a combination of objectives, constraints and conventions for monetary authorities. Constraints and conventions include “rules or disciplines to whichauthorities are subject (voluntarily or involuntarily), the nature of the financial and monetary markets and institutions, the understandings of key
macroeconomic relationships, and the political environment“. Among thecriteria used to distinguish between different frameworks is the verification ofwhether monetary authorities publish targets for some objective and whether
24such targets exist for monetary aggregates, exchange rates, inflation or othervariables. Based on this definition and other additional criteria, the author
identifies 32 different categories, aggregated into 9 broad categories based ontarget variables.
Direct controls: multiple exchange rates and/or controls on direct lending, interest rates, etc

Fixed exchange rates: exchange rate fixed by intervention, some or nomonetary instruments in use or pure currency board (domestic currency
100% backed by foreign currency, no monetary instruments in use)Exchange rate target

Monetary target

Inflation target



Mixed targets: monetary targets and exchange rate fixes or targets,monetary dominant or use of three full targets (or fixes) (money, ER
and IT), whichever dominant

Unstructured, loosely structured discretion: ineffective set of instruments and incoherent mix of objectives

Well structured discretion

No national framework: this category encompasses cases where a different sovereign currency is used (such as dollarization) in addition tomembership in a currency union (euro)25

Some of these categories are not represented in our sample and Cobham(2018)’s database does not include some of our sample countries (we aggregated them within the ”No national framework” category).
Examining the data (Figure 6), we can see that the IT regime has spreadsignificantly after the 1990s, while monetary target frameworks disappeared
and exchange rate targets and discretionary regimes became less frequent.
Data shows that levels of inflation are low in all cases in the recent decades.
However, during the 1980s, exceptionally high levels of inflation are noted formixed targets regimes (France, Germany and Italy). If there is no fiscal dominance in our sample then the choice of monetary policy frameworks should
affect how inflation reacts to fiscal variables. More specifically, if monetary
policy is the major driver of inflation, we expect that the inflation-fiscal policy relation will be weakened in commitment monetary policy regimes, andmore particularly inflation targeting.

4.4

Fiscal space

Observing levels of debt alone is not sufficient to draw conclusions about agovernment’s solvency. Therefore we introduce the notion of fiscal space in
our analysis to account for a government’s capacity to repay contracted debt.
We use a definition that is closer to that of Aizenman & Jinjarak (2010). We
first calculate the ratio of public debt divided by total government revenues.
This measure reflects the number of years of revenue needed to repay theoutstanding of public debt at a given date. Fiscal space is then defined as
the inverse of that ratio. The most recent available value (2016), the average

26and some descriptive statistics of the calculated measure are given for all
sample countries in Table 4.4
As shown on Figure 7, countries with the lowest fiscal space in the 1980s
are Colombia and Nicaragua. In 2016, Japan has the lowest fiscal spacewith the greatest number of years required to repay the debt (see Figure 8).
Simple observation of those two figures does not show an obvious link between
the fiscal balance and fiscal space, since both groups of countries running
substantial budget deficits and those with high surpluses can have either
low or high fiscal space. This implies that fiscal policy does not necessarily
depend on this ratio.
However, correlation coefficients appear to be significant in many countries (Table 5), even though the correlation sign varies (it is negative in cases
like Japan, Australia, Brazil and the USA, and positive in some other cases,
such as Belgium, Mexico and Pakistan, etc). After the 2007 crisis, the fiscal
space measure has worsened for advanced economies in particular (for example Japan, from 3.4 to 6.9 or the USA, from 1.8 to 2.9), reflecting the useof fiscal stimulus after the recession. On the other hand, emerging and developing economies, which suffered from a deteriorating fiscal stance during
the 1980s debt crisis, have had a better fiscal space indicator over the last
decade.
4

To reduce gaps in data, missing values have been filled using the closest available
values27

5

Results and discussion

We estimate the following VAR modelzt = ∅1 zt−1 + . . . + ∅j zt−j + wt

where





ps t
 y 




 by t 





 j 


zt = 
 θt  ∅ = 






 it 




πt

∅j11

∅j12

∅j13

∅j14

∅j15

∅j21 ∅j22 ∅j23 ∅j24 ∅j25
∅j31 ∅j32 ∅j33 ∅j34 ∅j35
∅j41 ∅j42 ∅j43 ∅j44 ∅j45
∅j51 ∅j52 ∅j53 ∅j54 ∅j55



(24)





v

 1t 

 v2t  and wt =  v  3t 







 v4t 
v5t

The estimation is made using the methodology suggested by Sigmund &Ferstl (2017) for panel VAR GMM (details provided in Appendices 1 and2), and after applying forward orthogonal transformation to the variables ofstudy (see Appendix 3). The methodology of Sigmund & Ferstl (2017) isuseful to eliminate the Nickell Bias (Nickell (1981)), according to which thecorrelation between error terms and regressors present in panel VAR OLS
estimations leads to inconsistent estimators. The resulting model is shownon Table 6).
Results indicate that public debt is persistent with a significant autoregressive coefficient. The variables of growth, interest rate and inflation also
have a significant own coefficient. The inflation equation shows that the
only significant variable in determining inflation is the interest rate, with apositive and high coefficient. Fiscal variables’ coefficients are negative but

28not statistically significant. OLS estimation, on the other hand (Table 7),
shows that both fiscal policy and monetary policy variables significantly affect the variables of study. Also, the primary balance reacts positively todebt (consistent with our expectations), while debt is affected negatively by
the primary balance (as cumulating budget deficits lead to a growing debt).
The variable of growth has a negative effect on both the fiscal balance andpublic debt.

5.1

Inflation’s interdependence with the variables of
study

Using generalized impulse response functions to analyze the impact on inflation of a one-standard deviation innovation in all variables (Figure 9), we notea positive and very significant response to interest rates (a 1 to 1 response).
A positive shock in the primary balance also induces a positive response of
inflation (higher surplus implies higher inflation), but of a smaller magnitude(0.3 units). These first results indicate that inflation is affected by both fiscal
and monetary policy. However, response of inflation to public debt is verysmall and of a negative sign (less than -0.1 at period 1). Similarly, a shock
to growth also leads to a very limited, negative response of inflation.
On the other hand (see Figure 10), we also observe responses of all variables to a one-standard deviation innovation in inflation. We note that thisshock induces a positive and very significant response in short-term interest
rates (0.05 units). Public debt to GDP (in log terms) responds negatively
over the long-run (but with a magnitude limited to -0.01). Responses of29other variables are very small.

5.2

Fiscal policy shock

A one standard deviation positive innovation in the primary balance (Figure 11) induces a positive but poor response of growth and the interest rates(less than 0.02 units). This suggests that fiscal policy does not affect monetary policy. On the other hand, a significant and negative response of public
debt over GDP (in log terms) is generated over the long-run (reaching -0.1
at the end of the period). However, response of the inflation rate is the mostnotable one. After a positive jump, inflation gets negative immediately inthe second period (which is consistent with findings of the previous subsection) reaching the value of -0.15. Therefore a policy that increases taxation
or reduces government expenditures would eventually lead to lower future
debt and lower inflation. Similarly, an expansionary fiscal policy induces the
opposite effect as applying a negative shock to the primary balance leads
to a positive jump in the inflation rate, suggesting that fiscal deficits are
inflationary.
Nevertheless, when we split data based on monetary policy frameworks’classification provided by Cobham (2018)(for the period 1980-2016), we findthat the relationship between inflation and the fiscal balance varies acrossmonetary policy regimes. First, the correlation coefficients, although notvery high, are very different. The highest level of correlation is +23% in
the case of discretionary regimes (Figure 12). Second, when we decompose

30

generalized impulse response functions of inflation to a fiscal shock based onmonetary policy frameworks (Figure 13), we notice that the negative responseof inflation is only seen in unstructured or loosely structured discretionary
regimes (and with a magnitude lower than the response obtained for thewhole sample), while no response is noted for other monetary policy frameworks. Mixed targets regimes do show a positive response in the long-run,but due to the small number of observations (3 countries in the 1980s), it isdifficult to draw any conclusions. We find the same result when estimating a
GMM model for inflation based on the different frameworks (Table 8). Interestingly, short-term interest rates do not appear to be statistically significant
in inflation targeting and exchange rate targets regimes.
In the following step of the study, in order to see how fiscal space affects
the relationship between inflation and the fiscal variables, we split our sample into two fiscal space categories based on the middle quartile. S1 is the
subsample of higher fiscal space countries, for which few years are needed to
repay the debt and S2 is the subsample of economies with lower fiscal space,
for which many years are needed to repay the debt burden. Results (Table 9)
indicate that correlation between inflation and the fiscal balance is positive
with higher correlation for economies with the lowest levels of fiscal space.
Using generalized impulse response functions (Figure 14), a strong positive (up tp +0.6 units), then negative (to -0.4), response of inflation to a positive shock to the primary balance is noted for the S2 subsample (economies
with low fiscal space) while in the case of S1, response of inflation is very
weak and positive (+0.2). The implication is that countries with lower levelsof fiscal space are more at risk of inflationary pressures in case of increasing31

deficits. Focusing on the upper and lower 25th quartiles also corroborates
this conclusion, as no response is obtained for the upper quartile (see Figure 15). Decomposing impulse response functions of S2 (economies with low
fiscal space) by monetary policy regime shows once again that (even for theisolated case of S2) the relationship between inflation and fiscal policy only
holds for discretionary regimes (Figure 16).
Table 10 shows an estimated regression model of inflation using GMM(with lagged variables as instruments) and OLS. We can see that for subsamples with high fiscal space, the coefficient of the fiscal balance is positive
but not vey high (around 5 in the GMM specification and 1 based on OLS).
In the case of subsamples with low fiscal space, that coefficient is negative,
statistically significant and of a much higher magnitude (-103.8 for the 25thlower quartile in the GMM model).
Because both discretionary monetary policy regimes and low fiscal space
are factors that determine the existence of a relationship between fiscal policy
and inflation, we plot the number of observations by fiscal space depending
on monetary policy frameworks (Figure 17). Difference between fiscal space
categories in terms of number of observations for discretionary regimes is
not very notable. This implies that discretionary regimes are not necessarilyreflective of cases of low fiscal space. Therefore, both factors are important.
Finally, focusing on the public debt variable, the correlation coefficient ofinflation with public debt (Table 9) is negative in the case of S1 (meaningthat higher debt for countries with enough revenues has a negative effecton inflation), and positive and significant for S2 (public debt is associatedwith high inflation in countries with low fiscal space). Generalized impulse
32response functions show a positive response of inflation to a shock to publicdebt(Figure 18 and Figure 19). By fiscal space categories, this relationshipappears to hold only for lower fiscal space countries.

5.3

Monetary policy shock

A positive one SD innovation in short-term interest rates induces a positive
and notable response of the inflation rate, of 0.9 (Figure 20). No response is
obtained for primary balances which implies that monetary policy does notaffect fiscal policy. Response of other variables is very small and negative.
The correlation coefficient between inflation and short-term interest rates isalso positive and very important (as opposed to fiscal policy)(see Figure 21
and Table 9). The most significant values are observed for discretionary
monetary targets and mixed targets regimes.
As in the previous subsection, we decompose generalized impulse response
functions of inflation following monetary policy shocks using the monetary
policy frameworks’ classification (Figure 22). This time, the most significant
responses are those of both discretionary and exchange rate regimes. As in
the previously estimated GMM model (Table 8), response of inflation to theinterest rate is weaker in inflation targeting regimes.
Impulse response functions by categories of fiscal space show that the
response on inflation is stronger in lower fiscal space countries (Figure 23).
The same result is obtained by focusing only on the 25th upper and lower
quartiles (Figure 24). This result is also corroborated by the regression model
discussed previously (Table 10) where coefficient of the interest rate appears

33

to be much more significant in the case of countries with lower fiscal space.

5.4

Recessionary shock

In a final step, we apply a -1 unit shock to growth to see the impact of recessionary episode on the variables of study (Figure 25). Response of inflation(modeled on the right axis) is strong and gets negative in the second period(to -0.8), but quickly resumes back to its level (consistent with a Phillips
curve as the growth level also adjusts). No response is shown for the primary balance and the interest rate. Public debt responds positively over the
medium-term (which is consistent with our expectations). Therefore while
the relationship between debt and growth is visible, growth does not appear to affect the other variables, which confirms findings of the previous
subsections.
The growth rate could however affect the existence of a relationship between fiscal variables and inflation. As pointed by Sargent & Wallace (1981),
fiscal policy affects the price level when interest rates are greater than thegrowth rate, and thereby the cumulating burden of debt gradually surpasses
the demand for bonds by investors. To check this assumption, we split data
between observations where growth is greater than the interest rate and observations with growth lower than the interest rate (for which we expect tosee a stronger relationship between fiscal policy and inflation). Results (Figure 26) clearly corroborate this idea as no response from inflation is obtainedin the case where growth is greater than the interest rate.

34

5.5

Absence of fiscal dominancePrevious empirical studies, notably Bohn (1998), Bajo-Rubio et al. (2009)
and Canzoneri et al. (2001), rejected fiscal determinacy of the price level andconcluded that governments act in a ricardian fashion because of the presenceof an adjustment of primary surpluses to changes in public debt. We also finda similar result when we examine the response of fiscal balances to a shock
to public debt. And reciprocally, as in Canzoneri et al. (2001), public debt
reacts negatively to a positive shock to primary balances (Figure 11). Such afinding is an indication of the absence of fiscal dominance since, as stated by
Tanner & Ramos (2003), the primary surplus adjusts to limit debt growthwhen the regime is monetary dominant (MD), so that monetary policy can be
conducted independently of fiscal financing requirements. Moreover, we areunable to obtain a strong direct relation between public debt and inflation,
when considering the whole sample (see FE variance decomposition of both
debt and inflation in section 4).
However, when examining the relationship between fiscal balances andinflation, we find a significant negative relationship between both variables(section 5.2). This result suggests that fiscal deficits are inflationary, as
in Fan et al. (2013). Still, further analysis shows that this relation is notrobust across all sample countries and periods. It only holds in cases when
unstructured or loosely structured discretion is applied. In monetary regimes
based on targets, our results indicate that inflation is not sensitive to fiscal
policy (and is also less sensitive to other shocks).
Such an outcome is not surprising and does not contradict conclusions

35

from previous discussions on monetary policy (Kydland & Prescott (1977)
and Barro & Gordon (1983)) according to which inflation’s sensitivity toshocks is reduced when central banks operate under commitment. The main
justification is that such regimes require independence of monetary authorities. Therefore, in these particular cases, governments cannot force central
banks to finance their deficits by generating excess liquidity or pressure central banks to keep low interest rates in order to lower governments’ borrowing
costs. In the language of Leeper (1991), fiscal policy is ’passive’ and monetary policy is ’active’, so that there is no fiscal dominance. As a consequence,
we conclude that fiscal determinacy of the price level is only possible whencentral banks practice unstructured/loosely structured discretion.

6

Conclusion

In this paper, we attempted to verify the presence of a relationship between
fiscal policy and inflation. We used a theoretical and analytical framework
similar to the one suggested by Cochrane (2019a). This approach allowed us
to examine simultaneously the links between inflation, fiscal and monetary
variables, and GDP growth; in contrast to previous empirical studies which
only focused on the relationship between debt and the primary balance, or
between inflation and some fiscal variables.
We followed a slightly different approach than Cochrane (2019a) in deriving the linearized identity that served as a basis for our analysis. This
relation was obtained from a household’s budget constraint, using variables
expressed in real terms, as opposed to the government debt valuation equa36

tion in which the variable of debt alone is expressed in nominal terms. Also,
we did not attempt to compute the value of primary surpluses based on thelinear identity and instead we used actual data to construct a panel VAR
model.
For our sample of 46 countries, over the period 1960-2017, we found aclear and positive relation between inflation and the interest rate throughout
all steps of the study (GIRF showed a 1 unit response of inflation to a 1 SD
innovation in interest rates). We also found that the inflation rate was negatively affected by the fiscal balance, suggesting that an expansionary fiscal
policy is inflationary. These findings are consistent with those of Cochrane(2019a). In Cochrane’s study (which covered US data), the relationship between interest rates and inflation was also found to be strong and positive
and the one between fiscal balances and inflation to be negative.
On the other hand, we could not draw conclusions about the existenceof a direct link between public debt over GDP and inflation. While impulse
response functions showed a positive response of inflation to increasing debt,
this connection did not appear clearly in the variance decomposition analysis.
We also could not find a significant impact from growth to most variables.
Nevertheless, we were able to verify that, as predicted by Sargent & Wallace(1981), the relationship between fiscal policy and inflation is only present
when growth is lower than the interest rate.
We then further extended our analysis by examining how the choice ofa monetary policy regime, and how the level of fiscal space affect the links
between our variables. Interestingly, we found out that the relationship between fiscal balances and inflation only holds in cases when unstructured
37

or loosely structured discretion is applied. In monetary regimes based ontargets, our results indicated that inflation was not sensitive to fiscal policy(and was also less sensitive to other shocks). We thereby concluded on theabsence of fiscal dominance. Based on these findings, fiscal determinacy ofthe price level is only possible when central banks do not operate under commitment. The most significant implication is that running persistent deficits
will not lead to unexpected higher inflation, if monetary policy is resilientand credible enough to generate well-anchored inflation expectations.
Finally, we examined how fiscal space (defined as the inverse of the number of years of revenue needed to repay the outstanding of public debt at
a given date) affects the relationship of inflation with fiscal and monetary
policy. We found that inflation is more sensitive to variations in both thefiscal balance and the interest rate when fiscal space is low.

References
Aizenman, J. & Jinjarak, Y. (2010), De facto fiscal space and fiscal stimulus:
Definition and assessment, Technical report, National Bureau of Economic
Research.
Aizenman, J. & Jinjarak, Y. (2012), ‘The fiscal stimulus of 2009–2010: trade
openness, fiscal space, and exchange rate adjustment’, NBER International
Seminar on Macroeconomics 8(1), 301–342.
Aizenman, J., Jinjarak, Y., Nguyen, H. T. K. & Park, D. (2019), ‘Fiscal

38

space and government-spending and tax-rate cyclicality patterns: A crosscountry comparison, 1960–2016’, Journal of Macroeconomics 60, 229–252.
Anderson, T. W. & Hsiao, C. (1982), ‘Formulation and estimation of dynamic
models using panel data’, Journal of Econometrics 18(1), 47–82.
Andres, J. & Hernando, I. (1997), ‘Inflation and economic growth: some evidence for the OECD countries’, Monetary Policy and the Inflation ProcessBIS Conference Papers (4), 364–383.
Aratawari, R., Hori, T. & Mino, K. (2016), On the nonlinear relationshipbetween inflation and growth: a theoretical exposition. Kyoto University
Discussion papers.
Arellano, M. & Bond, S. (1991), ‘Some tests of specification for panel data:
Monte Carlo evidence and an application to employment equations’, Review of Economic Studies 58(2), 277–297.
Arellano, M. & Bover, O. (1995), ‘Another look at the instrumental variable estimation of error-components models’, Journal of Econometrics
68(1), 29–51.
Auerbach, A. J. & Gale, W. G. (2011), Tempting Fate: The Federal Budget
Outlook, Brookings Institution Washington, DC.
Auerbach, A. J., Gale, W. G. & Orszag, P. R. (2002), ‘The budget outlook
and options for fiscal policy’, Available at SSRN 308562 .
Bajo-Rubio, Ó., Dı́az-Roldán, C. & Esteve, V. (2009), ‘Deficit sustainability

39and inflation in EMU: An analysis from the fiscal theory of the price level’,
European Journal of Political Economy 25(4), 525–539.
Baksa, D., Benk, S. & Jakab, M. (2010), ‘Does the fiscal multiplier exist?’,EcoMod2010 .
Barro, R. J. & Gordon, D. B. (1983), ‘Rules, discretion and reputation in amodel of monetary policy’, Journal of Monetary Economics 12(1), 101–
121.
Barro, R. J. et al. (1996), ‘Inflation and growth’, Review-Federal Reserve
Bank of Saint Louis 78, 153–169.
Bassetto, M. & Cui, W. (2018), ‘The fiscal theory of the price level in aworld of low interest rates’, Journal of Economic Dynamics and Control
89, 5–22.
Bernanke, B. S., Gertler, M. & Gilchrist, S. (1999), ‘The financial accelerator
in a quantitative business cycle framework’, Handbook of Macroeconomics
1, 1341–1393.
Blanchard, O., Dell Ariccia, G. & Mauro, P. (2010), ‘Rethinking macroeconomic policy’, Revista de Economı́a Institucional 12(22), 61–82.
Blanchard, O. J. (1990), ‘Suggestions for a new set of fiscal indicators’, OECD
Economics Department Working Papers .
URL: https://ideas.repec.org/p/oec/ecoaaa/79-en.html
Blundell, R. & Bond, S. (1998), ‘Initial conditions and moment restrictions
in dynamic panel data models’, Journal of Econometrics 87(1), 115–143.
40

Bohn, H. (1998), ‘The behavior of US public debt and deficits’, Quarterly
Journal of Economics 113(3), 949–963.
Bohn, H. (2007), ‘Are stationarity and cointegration restrictions really necessary for the intertemporal budget constraint?’, Journal of Monetary Economics 54(7), 1837–1847.
Bohn, H. (2008), ‘The sustainability of fiscal policy in the United States’,
CESifo Working Paper Series: Sustainability of Public Debt (1446), 15–49.
Buiter, W., Corsetti, G. & Roubini, N. (1993), ‘Excessive deficits: sense andnonsense in the treaty of Maastricht’, Economic Policy 8(16), 57–100.
Buiter, W. H. (1985), ‘A guide to public sector debt and deficits’, Economic
policy 1(1), 13–61.
Buiter, W. H. (2017), ‘The fallacy of the fiscal theory of the price level-once
more’, CEPR Discussion Paper No. DP11941 .
Calvo, G. A. (1978), ‘On the time consistency of optimal policy in a monetary
economy’, Econometrica 46(6), 1411–1428.
Calvo, G. A. (1983), ‘Staggered prices in a utility-maximizing framework’,
Journal of Monetary Economics 12(3), 383–398.
Canzoneri, M. B., Cumby, R. E. & Diba, B. T. (2001), ‘Is the price level
determined by the needs of fiscal solvency?’, American Economic Review
91(5), 1221–1238.

41

Carlstrom, C. T. & Fuerst, T. S. (1997), ‘Agency costs, net worth, and business fluctuations: A computable general equilibrium analysis’, American
Economic Review 87(5), 893–910.
Catao, L. A. & Terrones, M. E. (2005), ‘Fiscal deficits and inflation’, Journalof Monetary Economics 52(3), 529–554.
Clarida, R., Gali, J. & Gertler, M. (2000), ‘Monetary policy rules and macroeconomic stability: evidence and some theory’, Quarterly journal of Economics 115(1), 147–180.
Cobham, D. (2018), ‘A comprehensive classification of monetary policy
frameworks for advanced and emerging economies’, MPRA Paper available at https://mpra.ub.uni-muenchen.de/id/eprint/90141 .
Cochrane, J. H. (1998), ‘A frictionless view of US inflation’, NBER Macroeconomics Annual 13, 323–384.
Cochrane, J. H. (2005), ‘Money as stock’, Journal of Monetary Economics
52(3), 501–528.
Cochrane, J. H. (2011), ‘Understanding policy in the great recession: Some
unpleasant fiscal arithmetic’, European Economic Review 55(1), 2–30.
Cochrane, J. H. (2018), ‘Stepping on a rake: The fiscal theory of monetary
policy’, European Economic Review 101, 354–375.
Cochrane, J. H. (2019a), ‘The fiscal roots of inflation’, Working paper available at https://faculty.chicagobooth.edu/john.cochrane/research/ .

42

Cochrane, J. H. (2019b), The Fiscal Theory of the Price Level, Chicago
Booth.
URL:

https://faculty.chicagobooth.edu/john.cochrane/research/papers/

fiscal theory book.pdf
Coenen, G., Erceg, C. J., Freedman, C., Furceri, D., Kumhof, M., Lalonde,
R., Laxton, D., Lindé, J., Mourougane, A., Muir, D. et al. (2012), ‘Effects of fiscal stimulus in structural models’, American Economic Journal:
Macroeconomics 4(1), 22–68.
Corsetti, G. & Mackowiak, B. (2001), ‘Nominal debt and the dynamics of
currency crises’, CEPR Discussion Paper (No. 2929). Available at SSRN:
https://ssrn.com/abstract=281674.
Creel, J. & Le Bihan, H. (2006), ‘Using structural balance data to test the
fiscal theory of the price level: Some international evidence’, Journal of
Macroeconomics 28(2), 338–360.
Daniel, B. C. (2001a), ‘A fiscal theory of currency crises’, International Economic Review 42(4), 969–988.
Daniel, B. C. (2001b), ‘The fiscal theory of the price level in an open economy’, Journal of Monetary Economics 48(2), 293–308.
De Gregorio, J. (1992), ‘Economic growth in Latin America’, Journal of
Development Economics 39(1), 59–84.
Fan, J., Minford, P. & Ou, Z. (2013), ‘The fiscal theory of the price level-43

identification and testing for the UK in the 1970s’, Cardiff Economics
Working Papers, Cardiff University E2013/12.
Favero, C. A. & Monacelli, T. (2005), ‘Fiscal policy rules and regime (in)stability: evidence from the US’, IGIER Working Paper .
Fischer, S. (1993), ‘The role of macroeconomic factors in growth’, Journal ofmonetary economics 32(3), 485–512.
Fischer, S., Sahay, R. & Végh, C. A. (2002), ‘Modern hyper-and high inflations’, Journal of Economic literature 40(3), 837–880.
Friedman, M. (1968), ‘The role of monetary policy’, American Economic
Review 58, 1–17.
Galı́, J., López-Salido, J. D. & Vallés, J. (2007), ‘Understanding the effects ofgovernment spending on consumption’, Journal of the European Economic
Association 5(1), 227–270.
Ghosh, A. & Phillips, S. (1998), ‘Warning: Inflation may be harmful to yourgrowth’, IMF Staff Papers 45(4), 672–710.
Ghosh, A. R., Kim, J. I., Mendoza, E. G., Ostry, J. D. & Qureshi, M. S.
(2013), ‘Fiscal fatigue, fiscal space and debt sustainability in advanced
economies’, Economic Journal 123(566), F4–F30.
Gomme, P. (1993), ‘Money and growth revisited: Measuring the costs ofinflation in an endogenous growth model’, Journal of Monetary Economics
32(1), 51–77.

44

Gordon, R. J. (1982), Why stopping inflation may be costly: Evidence fromfourteen historical episodes, in ‘Inflation: Causes and Effects’, Universityof Chicago Press.
Gust, C., Herbst, E., López-Salido, D. & Smith, M. E. (2017), ‘The empiricalimplications of the interest-rate lower bound’, American Economic Review
107(7), 1971–2006.
Haslag, J. H. (1998), ‘Monetary policy, banking, and growth’, Economic
Inquiry 36(3), 489–500.
Hayat, Z. (2013), ‘The pros and cons of a discretionary moetary policy strategy: An empirical assessment’, Available at SSRN 2270210 .
Heller, M. P. S. (2005), Understanding fiscal space, International Monetary
Fund.
Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988), ‘Estimating vector autoregressions with panel data’, Econometrica 56(6), 1371–1395.
International Monetary Fund (2012), ‘Fiscal monitor: Balancing fiscal policy
risks’, World Economic and Financial Surveys .
Ismailov, S., Kakinaka, M. & Miyamoto, H. (2016), ‘Choice of inflation targeting: Some international evidence’, North American Journal of Economics and Finance 36, 350–369.
Jiang, Z. (2018), ‘Fiscal theory, price level, and exchange rate’, SSRN Electronic Journal . http://dx.doi.org/10.2139/ssrn.3059009.

45

Johansen, S. (1988), ‘Statistical analysis of cointegration vectors’, Journal of
Economic Dynamics and Control 12(2-3), 231–254.
Kaminsky, G., Lizondo, S. & Reinhart, C. M. (1998), ‘Leading indicators ofcurrency crises’, IMF Staff Papers 45(1), 1–48.
King, R. G. & Plosser, C. I. (1984), ‘Money, credit, and prices in a realbusiness cycle’, American Economic Review 74(3), 363–380.
Koop, G., Pesaran, M. H. & Potter, S. M. (1996), ‘Impulse response analysis
in nonlinear multivariate models’, Journal of Econometrics 74(1), 119–147.
Kormendi, R. & Meguire, P. (1985), ‘Macroeconomic determinants ofgrowth:Cross-country evidence’, Journal of Monetary Economics

16(2), 141–163.
URL:

https://EconPapers.repec.org/RePEc:eee:moneco:v:16:y:1985:

i:2:p:141-163
Krugman, P. & Eggertsson, G. B. (2011), Debt, deleveraging and the liquidity trap, in ‘2011 Meeting Papers’, number 1166, Society for Economic
Dynamics.
Kydland, F. E. & Prescott, E. C. (1977), ‘Rules rather than discretion: The
inconsistency of optimal plans’, Journal of Political Economy 85(3), 473–
491.
Kydland, F. E. & Prescott, E. C. (1982), ‘Time to build and aggregate
fluctuations’, Econometrica 50(6), 1345–1370.

46

Laffer, A. (2009), ‘Taxes, depression, and our current troubles’, Wall Street
Journal (2009-09-21) .
URL:

https://www.wsj.com/articles/SB1000142405297020344010

4574402822202944230
Leeper, E. M. (1991), ‘Equilibria under active and passivemonetary and fiscal
policies’, Journal of Monetary Economics 27(1), 129–147.
Loyo, E. (1999), ‘Tight money paradox on the loose: a fiscalist hyperinflation’, manuscript, Harvard University .
Lucas Jr, R. E. (1976), ‘Econometric policy evaluation: A critique’, CarnegieRochester Conference Series on Public Policy 1(3), 19–46.
Lucas Jr, R. E. (1988), ‘On the mechanics of economic development’, Journalof Monetary Economics 22(1), 3–42.
Mauro, M. P., Romeu, R., Binder, M. A. J. & Zaman, M. A. (2013), A Modern History of Fiscal Prudence and Profligacy, number 13-15, International
Monetary Fund.
Minford, P., Theodoridis, K. & Meenagh, D. (2009), ‘Testing a model of
the UK by the method of indirect inference’, Open Economies Review
20(2), 265–291.
Mundell, R. (1963), ‘Inflation and real interest’, Journal of Political Economy
71(3), 280–283.
Nickell, S. (1981), ‘Biases in dynamic models with fixed effects’, Econometrica
49(6), 1417–1426.
47

Ostry, J. D., Ghosh, A. R., Kim, J. I. & Qureshi, M. S. (2010), Fiscal space,International Monetary Fund, Research Department.
Pesaran, H. H. & Shin, Y. (1998), ‘Generalized impulse response analysis inlinear multivariate models’, Economics letters 58(1), 17–29.
Reinhart, C. M., Reinhart, V. R. & Rogoff, K. S. (2012), ‘Public debt overhangs: advanced-economy episodes since 1800’, Journal of Economic Perspectives 26(3), 69–86.
Reinhart, C. M. & Rogoff, K. S. (2009), This Time is Different: Eight Centuries of Financial Folly, Princeton University Press.
Reinhart, C. M. & Rogoff, K. S. (2014), ‘Recovery from financial crises:
Evidence from 100 episodes’, American Economic Review 104(5), 50–55.
Rogoff, K. (1985), ‘The optimal degree of commitment to an intermediate
monetary target’, The Quarterly Journal of Economics 100(4), 1169–1189.
Romer, D. & Alexander, R. (2001), Advanced Macroeconomics, Vol. 2,
McGraw-Hill, New York.
Sargent, T. & Wallace, N. (1981), ‘Some unpleasant monetarist arithmetic’,
Federal Reserve Bank of Minneapolis Quarterly Review .
Sbordone, A. M., Tambalotti, A., Rao, K. & Walsh, K. J. (2010), ‘Policy
analysis using DSGE models: an introduction’, FRBNY Economic Policy
Review 16(2).
Sigmund, M. & Ferstl, R. (2017), ‘Panel vector autoregression in R with the
package panelvar’, SSRN Electronic Journal . DOI: 10.2139/ssrn.2896087.
48

Sims, C. A. (1994), ‘A simple model for study of the determination of theprice level and the interaction of monetary and fiscal policy’, Economic
theory 4(3), 381–399.
Sims, C. A. (2011), ‘Stepping on a rake: The role of fiscal policy in the
inflation of the 1970s’, European Economic Review 55(1), 48–56.
Stockman, A. C. (1981), ‘Anticipated inflation and the capital stock in a cash
in-advance economy’, Journal of Monetary Economics 8(3), 387–393.
Tanner, E. & Ramos, A. M. (2003), ‘Fiscal sustainability and monetary versus fiscal dominance: evidence from Brazil, 1991–2000’, Applied Economics
35(7), 859–873.
Taylor, J. B. (1980), ‘Aggregate dynamics and staggered contracts’, Journalof Political Economy 88(1), 1–23.
Teles, P., Uhlig, H. & Valle e Azevedo, J. (2016), ‘Is Quantity Theory Still
Alive?’, Economic Journal 126(591), 442–464.
Vaona, A. (2012), ‘Inflation and growth in the long run: A New Keynesian
theory and further semiparametric evidence’, Macroeconomic Dynamics
16(1), 94–132.
Walsh, C. E. (2017), Monetary Theory and Policy, MIT press.
Woodford, M. (1994), ‘Monetary policy and price level determinacy in a
cash-in-advance economy’, Economic theory 4(3), 345–380.

49

Woodford, M. (1995), ‘Price-level determinacy without control of a monetary
aggregate’, Carnegie-Rochester Conference Series on Public Policy 43, 1–
46.
Woodford, M. (1998), ‘Public debt and the price level’, Manuscript, Princeton University .

50

51

France, Germany,
Italy, the UK
and the US data
1963-2001

The U.K. in the
1970s

Fan &
Minford (2013)

Brazil 1991-2000

US 1960-2002

11 EU countries
1970-2005
postwar U.S. data
1951-1995

US data
1916-1995

Sample107 countries over
1960-2001

Markov-switching
regression methods
VAR, IRF and Granger
causality tests
VAR, IRF (same approachas Canzoneri)+ separation between
structural/cyclical PB
ARIMA model for inflation,
ADF
and cointegration tests

VAR, IRF

OLS regressions by periods+
a non linear model for PB(based on debt)
Cointegration analysis andGranger causality tests

ARDL model

Methodology

Behaviour of inflation can be explainedby the FTPL (gov. expenditures)

FTPL non valid

fiscal dominance for the case of Brazil
for some important periods

Alternated ricardian and non-ricardian regime

Evidence of a ricardian regime

No evidence of FTPL (PB adjust to debt)

The fiscal authority acts in a ricardian
fashion (PB adjusts)

Conclusion
Inflation and fiscal deficits are only correlated
in the case of high-inflation, developing economies

Table 1: Some related empirical studies

Creel &Le Bihan (2006)

Bajo-Rubio
et al. (2009)
Canzoneri
et al. (2001)
Favero,Monacelli (2005)
Tanner &Ramos (2003)

Bohn (1998)

Paper
Catao &
Terrones (2005)

TablesTable 2: Sample countries
Group

Advanced

Emerging and Middle-income

Low-income developing

Countries
Australia
Belgium
Denmark
FranceGreeceIrelandJapanNew ZealandPortugal
Spain
Switzerland
United States

Austria
CanadaFinlandGermany
Iceland
Italy
Netherlands
NorwaySouth Korea
SwedenUnited Kingdom

Argentina
Chile
Costa Rica
India
MoroccoPanamaPeru
South Africa
TurkeyBrazilColombia
Dominican Republic
MexicoPakistan
Paraguay
Philippines
Thailand
Uruguay

Bolivia
Haiti
Nicaragua

Ghana
Honduras52

Table 3: Correlation coefficients between main variablespsytlog(byt )

π

i

psyt

100%

log(byt )

-15%

100%

π

-3%

5%

100%

i

4%

1%

45%

100%

θ

15%

-17%

-1%

-1%

θ

100%

Notes: psyt : primary surplus over GDP, log(byt ): log of public debt over GDP,
π: inflation rate, i: short-term interest rate, θ: growth rate of GDP per capita

53

Table 4: Inverse of fiscal space ratio (in years): descriptive statistics bycountry

Advanced
economies
Australia
Austria
Belgium
CanadaDenmark
FinlandFranceGermany
GreeceIceland
Ireland
Italy
Japan
Netherlands
New Zealand
NorwayPortugal
South Korea
Spain
SwedenSwitzerland
United Kingdom
United States
Emergingand Middle-incomeArgentina
BrazilChile
Colombia
Costa Rica
Dominican
Republic
India
MexicoMoroccoPakistan
PanamaParaguay
Peru
Philippines
South Africa
ThailandTurkeyUruguay
Low-income
developing
Bolivia
Ghana
Haiti
Honduras
Nicaragua
TOTAL

2016
Value

AverageMin

Max

Stand.
Number of
Deviation observations

2.00

1.55

0.25

7.38

1.01

851

1.13
1.69
2.08
2.28
0.71
1.16
1.82
1.52
3.64
0.90
2.70
2.80
7.09
1.41
0.65
0.65
3.01
1.11
2.62
0.83
0.84
2.12
3.22

0.64
1.31
2.23
1.90
0.97
0.71
1.11
1.29
2.67
1.20
2.05
2.42
4.33
1.38
0.97
0.71
1.64
0.74
1.35
0.92
1.53
1.44
2.14

0.26
0.81
1.58
1.18
0.50
0.25
0.45
0.74
1.04
0.55
0.67
1.74
1.75
0.91
0.40
0.52
0.70
0.31
0.35
0.63
0.84
1.02
1.29

1.13
1.70
2.80
2.36
1.45
1.16
1.82
1.93
3.82
2.47
3.52
2.84
7.38
1.68
1.71
1.04
3.01
1.13
2.62
1.29
2.18
2.23
3.30

0.22
0.21
0.35
0.31
0.25
0.30
0.42
0.32
0.73
0.52
0.81
0.27
2.02
0.20
0.35
0.16
0.61
0.26
0.60
0.18
0.37
0.37
0.57

37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37
37

2.16

3.16

0.16

236.11

11.32

666

1.53
1.66
0.93
1.99
4.25

2.33
1.49
1.21
12.27
4.63

0.50
0.85
0.16
0.61
1.60

8.06
2.23
4.00
236.11
8.88

1.46
0.36
1.05
46.64
2.36

37
37
37
37
37

3.18

3.32

1.19

10.20

2.28

37

3.45
1.96
2.49
4.29
1.86
1.28
1.59
2.78
1.39
1.88
0.86
1.45

2.67
2.28
3.41
4.97
2.84
2.38
2.69
3.78
1.20
1.75
1.27
2.31

1.36
1.50
1.45
3.64
1.57
0.67
1.04
2.23
0.78
0.21
0.72
0.79

3.78
3.73
6.27
6.85
5.14
5.58
5.50
6.62
1.68
3.39
2.46
5.39

0.92
0.59
1.30
0.77
0.87
1.47
1.18
0.93
0.22
0.78
0.41
1.15

37
37
37
37
37
37
37
37
37
37
37
37

2.07

6.51

0.77

53.23

8.44

185

1.31
4.25
1.81
1.18
1.77
2.07

5.05
3.99
7.78
3.19
12.56
2.72

0.83
1.45
0.81
0.77
1.77
0.16

45.89
10.12
35.02
6.76
53.23
236.11

7.85
2.03
7.79
1.57
13.02
7.79

37
37
37
37
37
1702

Notes: Fiscal space is defined as the sum of total government revenues divided by public debt. The
inverse of this measure reflects the number of years of revenue needed to repay the outstanding of public
debt at a given date54

Table 5: Correlation coefficient between the fiscal balance and the inverse of
the fiscal space ratio (number of years of revenue needed to repay the debt)
Countries
Argentina
Australia
Austria
Belgium
Bolivia
BrazilCanadaChile
Colombia
Costa Rica
Denmark
Dominican Rep.
FinlandFranceGermany
Ghana
GreeceHaiti
Honduras
Iceland
India
IrelandItaly
Japan
MexicoMoroccoNetherlands
New Zealand
Nicaragua
NorwayPakistan
PanamaParaguay
Peru
Philippines
Portugal
South Africa
South Korea
Spain
SwedenSwitzerland
ThailandTurkeyUnited Kingdom
United States
Uruguay

Inverse of fiscal space ratio (years)
1980-2016 1980-2006 2007-2016
2.3
2.6
1.6
0.6
0.6
0.7
1.3
1.2
1.5
2.2
2.3
2.0
5.1
6.5
1.0
1.5
1.4
1.7
1.9
1.8
2.1
1.2
1.5
0.5
12.3
16.3
1.4
4.6
5.4
2.5
1.0
1.1
0.7
3.3
3.7
2.3
0.7
0.6
0.9
1.1
0.9
1.6
1.3
1.2
1.7
4.0
4.5
2.7
2.7
2.4
3.4
7.8
9.9
2.0
3.2
3.9
1.3
1.2
1.0
1.8
2.7
2.4
3.3
2.0
1.9
2.6
2.4
2.4
2.6
4.3
3.4
6.9
2.3
2.5
1.8
3.4
3.9
2.0
1.4
1.4
1.4
1.0
1.1
0.7
12.6
16.2
2.8
0.7
0.7
0.8
5.0
5.2
4.4
2.8
3.3
1.7
2.4
2.9
1.0
2.7
3.2
1.3
3.8
3.9
3.4
1.6
1.3
2.5
1.2
1.2
1.1
0.7
0.6
1.0
1.4
1.1
2.0
0.9
1.0
0.8
1.5
1.6
1.2
1.7
1.7
2.0
1.3
1.3
1.1
1.4
1.3
1.9
2.1
1.8
2.9
2.3
2.5
1.8

55

Correlation coefficient
1980-2016 1980-2006 2007-2016
31%
18%
67%
-47%
-41%
-41%
-47%
-51%
16%
63%
61%
-57%
-71%
-73%
-8%
-65%
-64%
-10%
-3%
40%
-84%
15%
8%
-71%
-1%
-3%
-87%
57%
47%
-6%
29%
10%
-90%
26%
25%
-48%
-62%
-45%
-86%
-49%
-27%
-18%
-3%
-6%
-77%
10%
-6%
28%
-21%
10%
19%
-2%
10%
18%
45%
41%
-82%
-60%
-42%
-87%
35%
56%
-56%
-37%
-12%
-24%
9%
17%
-14%
-84%
-75%
-92%
66%
54%
-35%
-29%
-33%
-58%
-11%
19%
-79%
-29%
-72%
-94%
-1%
19%
74%
52%
43%
60%
55%
63%
-52%
12%
-11%
-21%
19%
19%
23%
-43%
-50%
-24%
22%
13%
-58%
-42%
3%
9%
-10%
4%
-88%
-63%
-49%
-73%
-34%
58%
-52%
7%
9%
-37%
-1%
-33%
88%
-44%
-42%
-34%
46%
49%
-26%
-69%
-15%
-64%
-66%
-6%
-49%
8%
1%
89%

Table 6: Panel VAR GMM Estimation

Lag 1 psyt

Lag 1 log(byt )Lag 1 θLag 1 (i)Lag 1 πpsytlog(byt )θiπ

8.1820-1.0367

0.2123-2.2192-11.4538(10.7477)(0.6839)(0.4580)(3.4129)(8.5006)-0.3483

0.9448***0.00040.1029-0.1038(0.4910)(0.0284)(0.0248)(0.1571)(0.1534)-3.3278-0.0786

0.4696*

0.9481

0.9424(4.7177)(0.2211)(0.2077)(1.4744)(0.6138)-0.3201

0.0947-0.0026

0.8783**3.8592**(1.8159)(0.2080)(0.1238)(0.2925)(1.2843)

0.033-0.0176

0.0027-0.0005

0.1168*(0.2301)(0.0277)(0.0152)(0.0319)(0.0501)

Notes: *** p<0.001, **p<0.01, *p<0.05
psyt : primary surplus over GDP, log(byt ): log public debt/GDP, θ: growth rate,(i): interest rate, π: inflation rate.
Forward orthogonal transformation is applied to the variables.
Hansen test of overidentified restrictions: chi2(1375)=35.2, proba >chi2=1

56

Table 7: Panel VAR OLS Estimates

Lag 1 psyt

Lag 1 log(byt )Lag 1 θLag 1 (i)Lag 1 πpsytlog(byt )

θ

i

π

0.825

-0.7040.271-0.154-8.726(71.7)(-7.5)(4.13)(-1.817)(-5.77)-8.6E-05

1.007

0.00130.0056-0.073(-0.604)(859)(15.4)(5.3)(-3.899)-0.007-0.4080.244-0.01360.83(-2.054)(-14.71)(12.56)(-0.54)(1.85)-0.001

0.021

0.043

0.848

3.06

(-0.61)(1.567)(4.47)(68)(14.047)0.000650.00056-0.0013-0.0020.136(4.223)(0.445)(-1.5)(-1.89)(6.7)

Notes: t-statistics in brackets
psyt : primary surplus over GDP, log(byt ): log public debt/GDP, θ: growth rateof GDP per capita, (i): short-term interest rate, π: inflation rate
R-squared: 0.6797, Adj R-squared: 0.6791

57

Table 8: Estimated GMM model for inflation by Monetary Policy framework
Whole

Monet.

Infl.

Discretion.
ERT

sampleTargets

Targeting

-18.6

0.71

-0.145

10.3

-14.5

0

0.18

0.24

0.76

0.07

-0.2

-0.001-0.002

0.16

-0.24

0

0.82

0.5

0.74

0.013

9.3

0.022

0.62

-10.82

7.5

0

0.92

0.005

0.76

0.38

4.3

0.72

0.008

-0.97

4.6

0

0

0.907

0.79

0

J-stat (p-value)

0.35

0.02

0.79

0.98

0.37

R-squared

0.10

0.24

-8.49

-804

0.45

0.09

0.17

-8.58

-816

0.45

psyt

log(byt )θ(i)regimes

AdjustedR-squared
Notes: psyt : primary surplus over GDP, log(byt ): log public debt/GDP,θ:Growth rate of GDP per capita, (i): short-term interest rate, π: inflation rate
(dependent variable)

58

Table 9: Correlation coefficients by fiscal space categories
Correlation

S1

S2

GlobalInflation/ fiscal balance

7%

21%

23%

Inflation/ public debt

-13%

42%

47%

Inflation/short-term interest rate

90%

71%

76%

Notes: S1: High fiscal space (50th upper percentile); S2: low fiscal space (50thlower percentile)

Table 10: Estimated inflation regression model by fiscal space categories
Indep.
Var.
psytlog(byt )θ(i)J-stat(p-val.)R-squ.
Adj.
R-squ.

GMM estimation
25th
S1
S2
up q.

OLS estimation
25th
S1
S2
up q.

25th
low q.

Global25th
low q.

-18.6

5.4

-43

4.84

-103.8

-4.55

1.22

-10

1.46

-17.5

(0)

(0.04)(0)(0.02)(0)(0.03)(0.05)(0.02)(0.02)(0.02)

-0.2

0.08

-0.4

0.04

-1.3

-0.10

-0.1

-0.2

-0.1

-0.26

(0)

(0.22)(0)(0.58)(0.01)

(0)

(0)

(0)

(0)

(0.005)

9.3

-11

20.3

-9.7

47.4

-0.27

-0.1

-0.3

-0.4

1.23

(0)

(0.02)(0.03)(0.03)(0.13)(0.67)(0.57)(0.79)(0.07)

(0.6)

4.3

3.8

5.04

5.23

14.75

4.99

3.9

6.3

4.6

8.03

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

(0)

0.35

0.21

0.75

0.28

0.97

0.10

0.13

-0.11

0.29

-0.72

0.21

0.80

0.16

0.85

0.19

0.09

0.12

-0.12

0.28

-0.73

0.20

0.80

0.16

0.85

0.18

GlobalNotes: (P-value between brackets)
S1: subsample of the upper 50th percentile (High fiscal space); S2: subsample ofthe lower 50th percentile (Low fiscal space); 25th up q.:subsample of the upper
25th percentile (Highest fiscal space quartile); 25th low q.:subsample of thelower 25th percentile (Lowest fiscal space quartile).
Variables psyt : primary surplus over GDP, log(byt ): log public debt/GDP,θ:growth of GDP per capita, (i): short-term interest rate, π: inflation rate

59

Figures
Figure 1: Average Public debt to GDP evolution(Low-income developing series plotted on the right axis)

Figure 2: Average Primay balance to GDP evolution(Low-income developing series plotted on the right axis)

60

Figure 3: Average Inflation(Emerging middle-income and Total average series plotted on the right axis)

Figure 4: Variance Decomposition of Inflation using Cholesky (d.f. adjusted)
Factors

psy : primary surplus over GDP, by : log of public debt over GDP, π: inflation rate, i: short-term interest
rate, θ: growth rate of GDP per capita

61

Figure 5: Variance Decomposition of Public Debt Using Cholesky (d.f adjusted) Factors

psy : primary surplus over GDP, by : log of public debt over GDP, π: inflation rate, i: short-term interest
rate, θ: growth rate of GDP per capita

Figure 6: Number of countries by monetary policy framework Cobham (2018)

62

Figure 7: Primary balance and fiscal space by country 1980

(Average primary balance plotted on the right axis)

Figure 8: Primary balance and fiscal space by country 2016

(Average primary balance plotted on the right axis)

63

Figure 9: Response of inflation to one SD shock to all variables(Inflation series plotted on the right axis)

Figure 10: Response to one SD innovation in the inflation rate

(Inflation series plotted on the right axis)

64Figure 11: Response to one SD innovation in primary balances(Inflation series plotted on the right axis)

Figure 12: Correlation coefficient Inflation/Fiscal Balance65Figure 13: Response of inflation to a positive fiscal policy shock

Figure 14: Response of inflation to a positive fiscal policy shock66

Figure 15: Response of inflation to a positive fiscal policy shock (25th upperand lower quartile)

Figure 16: Response of inflation to a positive fiscal policy shock for S2 subsample (by MPF)

67

Figure 17: Number of observations by fiscal space category and monetary
policy framework

Figure 18: Response of inflation to a positive shock to public debt

68

Figure 19: Response of inflation to a positive shock to public debt (25thupper and lower quartile of fiscal space)

Figure 20: Response to one SD innovation in ST interest rates(Inflation series plotted on the right axis)

69

Figure 21: Correlation coefficient Inflation /Interest Rates

Figure 22: Response of inflation to a monetary policy shock(Discretionary frameworks series plotted on the right axis)

70

Figure 23: Response of inflation to a positive monetary policy shock

Figure 24: Response of inflation to a positive monetary policy shock (25thupper and lower quartile)71

Figure 25: Response to -1 unit shock to growth(Inflation series plotted on the right axis)

Figure 26: Response of inflation to a positive fiscal policy shock72

Appendix 1: Panel VAR GMM models (Methodology of Sigmund & Ferstl, 2017)
In this paper, we use the methodology provided by Sigmund & Ferstl (2017)forpanel var models. As classical OLS-based regression methods cannot be applied for panel data models because of the Nickell bias (Nickell (1981)), authors suggested the use of generalized method of moments for panel VAR
models. More specifically, they provided an extension to the work of Anderson & Hsiao (1982), for the first difference GMM estimator, using lagsof the endogenous variables as instruments (Holtz-Eakin et al. (1988) and
Arellano & Bond (1991)) and for the system GMM estimator (Blundell &Bond (1998)).
Sigmund & Ferstl (2017) use the PVAR model of Holtz-Eakin et al. (1988)
and extend it to allow for p lags of m endogenous variables, k predeterminedvariables and n strictly exogenous variables, such that:PP
yi,t = (Im − pl=1 Al )µi + pl=1 Al yi,t−l + Bxi,t + Csi,t + εi,twith Im an m x m identity matrix, yi,t an m x 1 vector of endogenous variables,
yi,t−l an m x 1 vector of lagged endogenous variables,xi,t a k x 1 vector of
predetermined variables, si,t a n x 1 vector of strictly exogenous variables, εi,tthe idiosyncratic error vector. Al (m x m), B (m x k) and C (m x n) areparameter matrices. µi is an individual error component representing the
fixed effects.
First, fixed effects are removed by transforming this relation into its firstdifference or applying the forward orthogonal transformation (suggested by
Arellano & Bover (1995) to minimize data losses resulting from data gaps).
Based on the first difference representation, the derived first difference GMM
moment conditions are as follows:TE(∆εi,t yi,j)=0 j ∈ {1,. . . , T-2}

E(∆εi,t xTi,j )=0 j ∈ {1,. . . , T-1}
E(∆εi,t ∆sTi,t )=0
Considering that
T
T
T
qi,t
:= (yi,t−p−1, . . . ., yi,1
, xTi,t−1 , . . . , xTi,1 , ∆sTi,t )Then, after stacking the model over time, moment conditions for each i are:73

E[QTi (∆Ei )] = 0
Considered as equivalent to the sample average g(Φ) = N1PN

i=1 gi (Φ) where:gi (Φ) = (Qi ⊗ Im×m )(vec(∆Ei ))The number of moment conditions depends on the value of p, m, k and n.
Two solutions are suggested to reduce this number: one is fixing a maximallag length after which no further instruments are used, or starting with a different minimal lag. This idea can be applied to lagged endogenous variables
and to predetermined variables. The other is collapsing the instruments suchthat the first difference GMM moment conditions become:P −2
T
E[ Tj=1(∆εi,t yi,j
]= 0
P −1
(∆εi,t xTi,j ]= 0
E[ Tj=1E(∆εi,t ∆sTi,t )=0
And Qi is reduced to a (T-2) x (T-2) matrix. The GMM estimator isderived by minimizing the function:P
Z T vec(∆Yi − [∆Yi , −1∆Xi ∆Si ]Φ))T Λ−1Π(Φ) = ( NZ
PN i=1 T i( i=1 Zi vec(∆Yi − [∆Yi , −1∆Xi ∆Si ]Φ))where Φ is defined as [A B C], an m x (p+k+n) matrix of parameters, Zi
is equal to Qi ⊗ Im×m and ΛZ is the GMM weighting matrix. This matrix isdefined, as proposed in the relevant literature through a one-step or a twostep estimation procedure. In the one-step estimation procedure, it is defined
as in Binder et al. (2005)
P
T
TΛZ = [ Ni=1 Qi DD Qi ] ⊗ Im×m
D serves as a (T-1) x T linear transformation matrix such that for any
matrix Vi , DVi= ∆V i The two-step estimation uses the residuals of the onestep estimation as ∆Ei
In addition, Sigmund & Ferstl (2017) address the case of a system GMM
estimator, which is considered to perform better as the first difference GMM
estimator. The additional moment conditions in this case are:E[εi,t + (I −Pp

E[εi,t + (I −T
j=1 Aj )µi (yi,t−1 − yi,t−2 ) ]= 0 ,PpT
j=1 Aj )µi (xi,t − xi,t−1 ) ]= 0 ,74

t ∈ 3, 4, . . . Tt ∈ 2, 3, . . . T

E[εi,t + (I −Pp

T
j=1 Aj )µi si,t ]= 0 ,t ∈ 2, 3, . . . T

The estimator is then derived similarly as in the previous case. Authors
also provide a framework of structural analysis functions for PVAR models
such as orthogonal and generalized impulse response functions, with a GMMspecific bootstrap method for estimating confidence intervals, and forecast
error variance decomposition.

Appendix 2: Generalized Impulse Response
FunctionsSuppose we have the following standard VAR(2) model:
Zt = θ0 + θZt−1 + εt
(25)








bt
θb0
θ11 θ12εbt
With Zt =, θ0 =,θ=and εt =pt
θp0
θ21 θ22
εpt
Backward iteration yields the following expression:
Zt = θ0 + θ(θ0 + θZt−2 + εt−1 ) + εtZt = θ0 + θ × θ0 + θ2 Zt−2 + θ × εt−1 + εt
Similarly
Zt = θ0 + θ × θ0 + θ2 (θ0 + θZt−3 + εt−2 ) + θ × εt−1 + εtZt = θ0 + θ × θ0 + θ2 × θ0 + θ3 Zt−3 + θ2 εt−2 + θ × εt−1 + εtZt = θ0 (1 + θ + θ2 ) + θ3 Zt−3 + (θ2 εt−2 + θ × εt−1 + εt )More generally:
Zt = θ0 (1 + . . . + θn ) + θn+1 Zt−(n+1) +Pni
i=0 θ εt−i

If the stability condition holds, then An+1
would tend towards 0 as n
1
approaches infinity. This leads to the MA representation of the VAR model:P
Zt = µ + ni=0 θi εt−i
Impulse response functions express the response of one endogenous variable to an impulse in another endogenous variable. Based on the MA representation of the VAR model, IRF can be stated as follows:
IRF (k, r) =∂Zt+k
= θk er∂(εt )r75(26)

Where k is the number of periods after the shock to the r-th componentof and er is a 2x1 vector with 1 in the r-th column and 0 otherwise. In
the VAR model used so far, the disturbances terms are correlated since they
incorporate the contemporaneous effects of all the endogenous variables. The
corresponding structural VAR model can be expressed as follows:




 
 
 

1 A12
bt
φb0
φ11 φ12
bt−1
wbt
=
+
+
(27)
A21 1
pt
φp0
φ21 φ22
pt−1
wpt






1 A12
φb0
φ11 φ12Considering that A=, φ0 =
, φ =
and
A21 1
φp0
φ21 φ22


wbt
Wt =
wpt
Then
AZt = φ0 + φZt−1 + Wt
(28)
In this case, Wt is a vector of uncorrelated white-noise disturbances.
Also
Zt = A−1 φ0 + A−1 φZt−1 + A−1 Wt
Such that:θ0 = A−1 ϕ0 , θ = A−1 ϕ and εt = A−1 Wt
Thus

−1 
 
1
1 A12
wbt
−1
12 A21εt = A Wt =
= 1−A
−A21
A21 1
wpt
1−A12 A21And
bt −A12 wpt
εbt = w1−A
12 A21
21 wbt +wpt
εpt = −A1−A
12 A21−A12
1−A12 A21
1
1−A12 A21



(29)

wbt
wpt



It follows that these error terms are composites of the two shocks fromthe two variables of the model. Because of this feedback effect, it is difficult to estimate the structural model. But after estimating the reducedform VAR model, it is possible to decompose εt by finding a matrix A
such that εt = A−1 Wt Since the objective is to isolate a disturbances vector that would have a diagonal variance covariance matrix, one way is using the Cholesky decomposition on the variance covariance matrix of εt tofind a lower triangular matrix A, such that AA0 = E(εε0 ) Conversely, thevariance covariance matrix of Wt will be diagonal because and therefore0
E(ww0 ) = AE(0 )A0 = ASS 0 A0 = AA−1 A−1 A0 = I
Using the structural model, we obtain the orthogonal impulse response func∂Zt+k
tion: OIRF (k, r) = ∂(W= Bk er Such that Bk = θk A−1 The main limitt )rof using the Cholesky-decomposition is its dependence on the ordering of76

the variables. To remedy this issue, Pesaran & Shin (1998) suggested analternative approach which is the Generalized IRF. They described IR as
the outcome of a conceptual experiment in which the effect over time of ahypothetical vector of shocks δ hitting the economy at time t is comparedwith a base-line profile at time t+k given the economy’s history. Therefore
the disturbances vector comprises both the shocks expected to hit the economy before t and the vector δ. Formally: GIRF (k, δ, Ωt−1 ) = E[Zi,t+k |εi,t =δr , Ωt−1 ] − E[Zi,t+k |Ωt−1 ]With Ωt−1 being the set of available information about economic history attime t-1. The idea of Pesaran & Shin (1998)approach is choosing to shock
only one element (the r-th element) and integrate the effects of other shocks
using the historically observed distribution of the errors. The Σε being thevariance covariance matrix of εt , GIRF are thus expressed as:
GIRF (k, r, Σε ) = E[Zi,t+k |εi,t,r = δr , Σε ] − E[Zi,t+k |Σε ](30)

As pointed out by Koop et al. (1996), if the vector of random shocks is
considered as jointly normally distributed, then the conditional expectationof the shocks is a linear function of δ:0−1−1
E(εt εt,r = δr ) = (σ1r , σ2r , . . . , σmr ) σrr
δr = Σε σrr
δr

(31)

The generalized impulse response of the effect of a shock to the r-th equation
at time t on Zt+k is given by:kθ Σεθk E(εt εt,r = δr ) = ( √)( √δσrrr )σrr√
By setting δr = σrr , and considering that σr,r is the r-th diagonal elementof Σε , we obtain the following expression
GIRF (k, r, Σε ) = θk Σε (σr,r )−1/2(32)

Appendix 3: Forward Orthogonal Transformation
The forward orthogonal transformation was suggested by Arellano & Bover(1995) to minimize data losses due to data gaps. Based on the following VAR
model
yi,t =p
X

Al yi,t−l + Bxi,t + Csi,t + i,t

l=1

77

(33)

When the first difference transformation is used, we get∆yi,t =pX

Al ∆yi,t−l + B∆xi,t + C∆si,t + ∆i,t

(34)

l=1

When forward orthogonal transformation is applied, variables are replacedby the following expression
!
X
L
yi,s
(35)
yi,t+1= ci,t yi,t − 1/Ti,ts>tWhere ci,t =qTi,t/Ti,t + 178