Fiscal dominance of brazil

 

Fiscal dominance: |αβ| <1 and |β–1– γ | >1
In this region, monetary policy responds weakly to inflation (|αβ|<1) while fiscal policy does not respond
strongly to debt (|β-1—γ |>1). This regime also generates a single equilibrium. This is equivalent to the
situation described by Sargent and Wallace (1981) when they coined the expression “unpleasant
monetarist arithmetic”. In this case, monetary policy is passive and fiscal policy active. Now, the monetary
authority obeys the constraints imposed by fiscal policy.
There are two possible cases. The first arises when the two roots have modulus less than 1, i.e.
each authority is acting passively. Without an additional constraint imposed by one of the authorities to
take an active stance, there are many equilibrium-compatible money supply increase processes that
lead to price level indeterminacy, an outcome remarked by Sargent and Wallace (1975). In the second
case, the two roots have modulus greater than 1, so that both authorities are acting actively. Unless
shocks ψt
and μt
are supposed to be correlated, there is no process of increase in the money supply
that ensures that agents will finance government securities.
In light of the foregoing, some brief remarks are warranted on coordination between monetary,
fiscal and indeed exchange-rate policies, although the Leeper model (1991) does not address the
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 • December 2021 85
Tito Belchior S. Moreira, Mario Jorge Mendonça and Adolfo Sachsida
last of these. Bearing in mind the trade-off between inflation and unemployment and the fact that in
1999 Brazil adopted an inflation-targeting system that implicitly admitted the prevalence of a monetary
dominance regime, a number of conjectures may be made.
If the central bank pursues lower inflation, even at the cost of higher unemployment, and the
Ministry of Finance pursues long-term public debt sustainability, the central bank will respond by raising
the Selic rate if inflation expectations rise. Similarly, fiscal policy will always seek to maintain a large
enough primary surplus to maintain public debt sustainability. This is an indication of coordination
between fiscal and monetary policymakers.
In other words, this is the policy that can ensure monetary dominance. Any other case must
correspond to a lack of coordination or, worse, a conflict of interest between policies. For example, if
the central bank tries to raise the base rate in order to keep inflation close to the target and fiscal policy
prioritizes increasing employment (a smaller surplus to boost aggregate demand) even at the expense
of inflation, the policy aims are contradictory. The opposite case, and cases in which both policies are
active or both passive, speak to policy conflicts, suggesting a lack of coordination between policymakers.
It may also be said that a deliberate policy of excessive build-up of foreign reserves can
generate adverse effects: on the one hand, it increases the monetization of the economy as a result of
foreign-exchange buying by the central bank, which pushes up inflation. On the other hand, to maintain
price stability, the central bank will be forced to issue repurchase agreements, which increases public
debt. The pass-through effect must also be borne in mind, whereby part of the exchange-rate variation
—whether rise or fall— is passed on to the rate of inflation.
III. Response function modelling
with regime-switching
The previous section presented the Leeper model (1991), which may be used to obtain the conditions to
determine whether policy is active or passive. From a practical point of view, it is necessary to ascertain
the fiscal and monetary policy rules, and on that basis to verify the stability of the model. On the basis
of the Leeper model (1991), Moreira, Souza and Almeida (2007) found evidence that Brazil underwent
a regime of fiscal dominance from 1995 to 2006.
The present study takes that literature further, taking as a basis the hypothesis that monetary and
fiscal policies may have undergone different regimes during the sample period analysed. The existence
of different regimes makes the conventional econometric techniques unsuitable to address the problem,
even working with different subsamples. A specific model is therefore used to treat supposed structural
breaks. That model allows the different stages undergone by monetary and fiscal policies since 2003
to be determined more clearly and accurately (Davig and Leeper, 2011). The model used to estimate
fiscal and monetary policy rules is discussed briefly below.
1. Markov-switching model
When a linear relationship undergoes a structural break —which can occur in the coefficients of the
variables, in the intercept and also in the variance of this relationship— the relevant parameters of the
regression model vary over time, producing non-linearities and, usually, violations of the stationarity and
normality hypotheses of the errors of conventional models. An alternative approach in this case is to
treat structural breaks (and, thus, “regime switches”) as exogenous, by introducing dummy variables
into the conventional linear models. However, this procedure requires advanced knowledge of the
86 CEPAL Review N° 135 • December 2021
Fiscal and monetary policy rules in Brazil: empirical evidence of monetary and fiscal dominance
precise moment at which breaks occurred, which in practice is rarely known. Even in the unlikely case
that the researcher “correctly guessed” the exact date of the relevant break or break, as well as their
respective durations, by itself the introduction of dummy variables does not resolve the problems
related to regime changes in the variance of the model errors. As Sims (2001) stated, it is a serious
error to disregard these or any other cause of residual non-normality when considering changes in the
parameters of the variables.
Markov-switching models explicitly assume that at any time there may be a finite (and generally
small) number of “regimes” or “states”, without knowing with certainty which obtains at that time. To cite
an intuitive example, it appears reasonable to suppose that an economy in recession will behave differently
(or have different parameters) to an economy that is growing rapidly. In this case, two “regimes” with
quite different characteristics —one “recessionary” and the other “fast-growing”— could be considered
to exist and to alternate every so often, without certainty as to which is occurring at each specific period.
Accordingly, Markov-switching models do not presume that “state switches” 

—for example, the
passage from the “fast-growing” to the “recessionary” regime— are deterministic events. The hypothesis
is rather one of “probabilities of transition” from one regime to another, which are endogenous estimated
using Markov-switching models.3 There is nothing to prevent regime switches from being “once and
for all”, in other words after a switch a given regime may remain indefinitely.
Non-linear time series modelling has been gaining increasing importance for some time now
(Hamilton, 1989 and 1994; Krolzig, 1997; Kim and Nelson, 1999; Sims, 1999 and 2001; Franses and
Van Dijk, 2000; Lütkepohl and Kratzig, 2004). In the present study we use the Markov-switching model
to estimate fiscal and monetary response functions. We thus propose to specify each of these models
as follows:
y b S b S S t t m m t
P
0 mt t t 1
= + | v+ f = R R W W / R W (7)
with N S 0, t t 2 f v + S R WX;
where St
is an unobserved stochastic variable which determines the state k that the model assumes
in each period t.
Note that, ex hypothesis, the “latent variable” St
is governed by a stochastic process known as
the ergodic Markov chain and defined by matrix of transition probabilities whose elements are given by:
p S Pr j S i p 1 1 i j, , , k ij t t ij j
k
1 1 = = + ;

 = = 0 d = R W,/ F ... I
pij ≥ 0 for i,j = 1, 2, ..., K (7.1)
Here, pij represents the probability that, in t + 1, the chain will switch from regime i to regime j.
The idea is thus that the probability of any regime St
occurring in the present depends solely on the
regime existing in the previous period, i.e. St–1. With k existing regimes, the probabilities of transition
between states may be represented by the matrix of transition probability P, with the dimension (k x k).
The parameters of this model are estimated via maximization of the model’s likelihood function
by means of the expectation–maximization (EM) algorithm (Dempster, Laird and Rubin, 1977), an
iterative technique for models with omitted or unobserved variables. It may be shown that the relevant
likelihood function increases with each iteration of this process, which ensures that the final result will be
3 More technically speaking, Markov-switching models fall within what Chib (1996) denominates “hidden Markov models”. For a
broad variety of these models, see Kim and Nelson (1999).
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Tito Belchior S. Moreira, Mario Jorge Mendonça and Adolfo Sachsida
close enough to the maximum likelihood in the relevant vicinity.4 However, it must be recalled that the
likelihood function of a Markov-switching models has no global maximum (Hamilton, 1991 and 1994;
Koop, 2003). Fortunately, the EM algorithm often yields a “reasonable” local maximum and pathological
cases are relatively rare (Hamilton, 1994).