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Monetary Policy Regime Changes and South Africa’s
Macroeconomic Fluctuations
February 14, 2020
Abstract
We investigate the impact of economic shocks on South Africa’s macroeconomic fluctuations,
within the context of monetary policy regime changes and changes in the volatility of economic
shocks. Thus, for our purposes, we use a Markov regime-switching small open economy dynamic
stochastic general equilibrium (MS-DSGE) model. Our model allows for regime switches in the
monetary policy rule parameters and the volatilities of structural shocks that impact the econ-
omy. By incorporating mineral commodity exports in a regime dependent framework, we find that
external shocks in the form of exports, import-cost inflation, risk premium, preferences and tech-
nology shocks, account for a large proportion of macroeconomic fluctuations in our model. This
contrasts with monetary policy shocks, that only accounts for a smaller proportion of fluctuations
in our model.
Keywords: Markov regime-switching DSGE model, Monetary policy regimes, Structural
shocks, Macroeconomic dynamics
JEL classification: C32, C51, E32, E52
1
1 Introduction
One of the primary objectives of macroeconomics research, is to understand what are the main sources
of inflation and output fluctuations. Identifying these sources remains a challenge because of the
changing structure of an economy, changes in the volatility of macroeconomic variables, policy regime
changes and changes in the expectations formation of agents. Despite this, monetary policy regime
changes are well documented; along with their associated macroeconomic performance after a mone-
tary policy regime change. The macroeconomic performance after a monetary policy regime change,
is mainly characterized by two prominent views, namely the good policy view and the good luck view.
According to the good policy view, institutional changes associated with good monetary policy
frameworks such as inflation targeting, are responsible for stabilizing inflation and output (Fuhrer
and Olivei, 2010; Canova and Ferroni, 2012; Baxa et al., 2014). On the other hand and according
to the good luck view, periods of stable inflation and output, coincide with periods of a favourable
and stable macroeconomic environment and within an environment of trade openness (Bernanke and
Mishkin,1997; Sims and Zha, 2006; Mishkin and Schmidt-Hebbel, 2007; and Boivin et al., 2010).
Within this context, policy changes can impact the expectations and decision making of rational
agents. Thus to account for this, Blake and Zampolli (2006), Liu and Mumtaz (2011), Davig and Doh
(2014) and Foerster (2014) use Markov-switching rational expectations models to examine the impact
of multiple regime shifts on macroeconomic outcomes. The key and common finding of these studies
is that expectations of future policy regime shifts have significant effects on macroeconomic outcomes.
Following this set-up, we examine the impact of a wide array of shocks on South Africa’s macroeco-
nomic fluctuations, within the context of monetary policy regime changes and changes in the volatility
of (structural) shocks. Our sample period is over 1981:Q1 - 2016:Q3 and this corresponds with dif-
ferent South African monetary policy regimes in the form of a monetary-aggregate targeting regime,
exchange rate targeting regime and an inflation targeting (IT) regime. For our purposes, we use a
Markov regime-switching small open economy dynamic stochastic general equilibrium (MS-DSGE),
with switches in the monetary policy rule parameters and switches in the volatility of shocks. This
approach allows us to use a structural model that may capture monetary policy regime changes and
changes in the volatility of economic shocks. As a result, this may allow us to appropriately char-
acterize South Africa’s macroeconomic fluctuations following a wide array of economic shocks. The
relevance of our study concerns identifying which factors are important for South Africa’s macroeco-
nomic fluctuations because this may allow us to argue about which factors monetary policy authorities
may consider in their policy conduct, especially if these factors substantially influence monetary policy
target variables such as inflation and the output gap.
By incorporating mineral commodity exports in a regime dependent framework, we find that
external shocks in the form of exports, import-cost inflation, risk premium, preferences and technology
shocks, account for a large proportion of fluctuations in the macroeconomic variables in our model.
2
This contrasts with monetary policy shocks that only account for a smaller proportion of fluctuations
in our model. Thus, our findings suggest that external shocks, along with changes in the volatilities
of these shocks, have a larger role to play in South Africa’s macroeconomic fluctuations and may
influence South Africa’s monetary policy conduct because these factors influence target variables -
such as inflation and the output gap - associated with monetary policy conduct.
Our results are in line with Nimark (2009), who uses a structural small open economy model for
Australia, and finds that external factors are responsible for a large proportion of the volatility of
output and inflation. Similarly, Baxa et. al. (2014) also find that external factors are important
for understanding the monetary policy conduct of a group of IT economies (Australia, Canada, New
Zealand, Sweden and the United Kingdom). We also find that our MS-DSGE model with regime
switches outperforms a DSGE model without regime switching monetary policy rule parameters and
volatility of shocks. However, when estimating both models, we find that the South African Reserve
Bank (SARB) consistently allocates the largest weight towards inflation stabilization, a lower weight
towards output gap stabilization and the lowest weight towards the exchange rate.
The remainder of this paper is organized as follows: Section 2 provides further motivation and
background analyses on key South African macroeconomic variables; along with literature related to
our approach. Section 3 presents our log-linearised small open economy DSGE model. Section 4
presents a regime-switching DSGE environment that includes a generic framework, stability solution
and estimation methods; along with outlining the data, priors and number of Markov switches in this
model. Section 5 presents our empirical results and section 6 concludes.
2 Further motivation, background and related literature
We examine the impact of shocks, and in particular export, import-cost inflation, risk premium,
preferences and technology shocks, on the South African economy. We put emphasis on the impact of
external shocks - for example export shocks - because South Africa is a small open IT economy that is
dependent on its mineral commodity exports. For example, over 2016, South African mineral exports
constitute about 21 percent of total exports of goods and services; along with mining contributing
7.30 percent to GDP over 2016. The concentration in mineral exports over 2016 is in gold, platinum
group metals, diamonds and silver (49.74 percent) and base minerals (50.26 percent), see Chamber
of Mines of South Africa (2016) and Statistics South Africa. Moreover, South Africa is currently
among the top gold producing countries and has been in the past, the top gold producer in the world
for several years. Thus, in our paper, mineral commodity exports are in the form of gold exports.
Over the business cycle, weak global demand and lower commodity export prices, can result in lower
output for commodity export dependent emerging market economies (EMEs) such as South Africa,
see also the South African Reserve Bank (2016). Thus, incorporating gold exports in our framework,
may provide insights about South Africa’s macroeconomic fluctuations because of its high degree of
3
mineral commodity export dependence.
Our main innovative aspect is that we allow for (mineral) commodity export shocks in a regime
dependent framework and thus we model commodity exports to follow a regime shock process that
impacts the monetary policy rule parameters in our model. Our model has two Markov switching
processes and these are as follows: (i) one in the monetary policy rule parameters and (ii) another
as a switching process in the commodity price volatility with a low and high volatility regime. Our
approach differs to Blake and Zampolli (2006), Liu and Mumtaz (2011), Alstadheim et al. (2013),
Bianchi et al. (2016), Blagov (2016) and Goncalves et al. (2016) because these studies allow for
changes in policy shocks and transition probabilities in their analysis.
Furthermore, and concerning a group of IT economies, our model differs to Baxa et al. (2014)
who don’t use a Markov-switching model, however they use a time-varying parameter model with
endogenous regressors because this allows them to evaluate changes in policy rules over time. In the
context of South African research, the closest study to ours is by Balcilar et al. (2016) who seek to
establish whether there is regime-switching in the monetary policy rule of the SARB and whether the
variances of structural shocks exhibit regime switching. Thus for their estimation purposes, they put
emphasis on a MS-DSGE model with switches in the monetary policy rule, switches in the volatility
of risk premium shocks and a model that accounts for switches in both the monetary policy rule and
volatility of risk premium shocks. Thus, unlike our approach, these studies do not account for the role
of mineral commodity exports within a regime dependent framework to establish the impact of shocks
on macroeconomic fluctuations; in a set-up that allows monetary policy regime changes and changes in
the volatilities of shocks. Furthermore, these studies do not account for a mineral commodity exports
that follow a regime shock process that influences the monetary policy rule parameters. As a result
and to the best of our knowledge, our paper is the first to follow such an approach.
Although we account for mineral commodity exports that follows a regime shock process and this
differs to Drygalla (2019), our study is aligned to his approach. Drygalla (2019) examines monetary
policy conduct following the adoption of IT in the Czech Republic, Hungary and Poland. Thus, he
estimates a MS-DSGE model that allows for regime switches in the policy parameters and volatility of
shocks impacting an economy, so as to establish the effects of monetary policy regime changes and the
associated monetary policy conduct. This approach allows him to establish whether de jure changes
in monetary policy frameworks, are translated as de facto monetary policy conduct. Furthermore, he
examines the extent to which target variables such as inflation, are stabilized by the de jure monetary
policy framework as compared to being stabilized by a favourable and more stable macroeconomic
environment.
For our purposes, we focus on South Africa because of a variety of reasons. First, South Africa has
the most developed financial markets in the African continent with a high degree of capital market
openness and ranks highly in many components of financial development such as its institutional envi-
4
ronment, financial stability, financial markets, banking and non-banking financial services. Moreover,
South Africa surpasses some emerging and industrialized economies such as Brazil, Chile, Poland,
Italy, Norway, Germany and Denmark in some components of financial development, see the World
Economic Forum: The Financial Development Report 2012. Thus with developed financial markets,
a high degree of capital market openness and a high degree of trade openness, the effects of shocks,
changes in the volatility of shocks and monetary policy regime changes, can impact the expectations
and decision making of rational agents and also impact South African macroeconomic dynamics.
Second, South Africa is an EME open to capital flows and it’s vulnerable to external shocks that can
influence its macroeconomic outcomes through a variety of channels. Furthermore, the transmission
and impact of external shocks on South Africa’s economy may be influenced by the effects of policy
regime changes. Lastly, many African countries are highly dependent on South Africa’s economy
because of their strong trade and regional links and because South Africa has been the largest African
economy for many years. As a result, the effects of shocks - within the context of monetary policy
regime changes and changes in the volatilities of shocks - on South Africa’s macroeconomic fluctuations,
may provide information about the business cycle fluctuations of other African countries, see also
Mateane and Proano (2018).
We document monetary policy regimes associated with the SARB over the 1981:Q1 - 2016:Q3
sample period and we document and interpret the first and second moments of important South
African macroeconomic variables over the 1981:Q1 - 2016:Q3 period in table 1. We document the
first and second moments of the annual change (4 quarter change) in real GDP based on seasonally
adjusted values, annual change in inflation based on a seasonally adjusted consumer price index,
quarterly changes in the nominal effective exchange rate and quarterly changes in the monetary policy
rate (repo-rate).1 This approach allows us to align the volatilities of each relevant variable with the
associated monetary policy regime. The sample period 1981:Q1 - 2016:Q3, coincides with multiple
frameworks adopted by the SARB. For example, over the 1960-1998 period, the several frameworks
include exchange rate targeting, discretionary monetary policy, monetary-aggregate targeting and an
eclectic approach. Furthermore, the SARB announced its intention to adopt IT in August 1999 and
officially adopted IT in February 2000.
Baaziz et al. (2013) and Peters (2014) provide more details concerning the monetary policy frame-
works adopted by the SARB from 1980 until the adoption of the explicit inflation targeting regime;
along with the associated events that may have influenced the adoption of specific policy regimes.
Thus, in line with Baaziz et al. (2013) and Peters (2014), we define the period 1981:Q1 - 1994:Q4
as the monetary-aggregate targeting (MAT) and exchange rate targeting (EXT) regime, the period
1995:Q1 - 1999:Q4 as the informal inflation targeting (IIT) regime and the period 2000:Q1 - 2016:Q3
as the explicit inflation targeting (IT) regime.
1The nominal effective exchange rate is the South African rand measured as a trade-weighted average of twenty
major trading partners of South Africa
5
Table 1: South African monetary policy regime summary statistics
MAT and EXT Infornal IT Official IT
Inflation
mean 12.99 7.08 5.62
Standard Deviation 2.40 2.08 2.42
Output
Mean 1.05 2.61 2.96
Standard Deviation 2.78 1.50 1.84
∆NEER
Mean -2.51 -2.07 -1.27
Standard Deviation 5.98 5.41 5.85
∆Repo-rate
Mean 0.12 -0.05 -0.07
Standard Deviation 1.41 1.76 0.72
∆NEER and ∆Repo-rate are the quarterly changes in the nominal effective
exchange rate and policy rate respectively
The first and second moments of inflation and output in table 1, show that over the IT regime,
both variables have stabilized substantially, relative to the MAT, EXT and IIT regimes. In particular,
annual output growth exhibits a higher mean growth and lower volatility over the IT regime, relative
to the MAT, EXT and IIT regimes. Concerning inflation, where price stability is the primary objective
of the SARB over the IT regime, the sample average of annual inflation over the IT period lies within
the SARB’s 3 - 6 % inflation targeting band. This IT period also coincides with the most recent
global financial crisis (GFC) over 2007-2010 and rising oil prices from 2006, that eventually peak over
June-July 2008. These factors negatively impacted global economic activity and EMEs such as South
Africa by increasing global risk aversion and the volatility of a wide array of economic variables.
Over the IT period, South Africa’s economy has undergone several reforms such as a higher degree
of capital market openness, a higher degree of exchange rate flexibility, a higher degree of trade
openness and a higher degree of integration with the rest of the world after the oppressive apartheid
regime. Thus, even when accounting for all these factors, what is evident is that a formal and well
defined monetary policy framework with a clear and accountable objective, has allowed the SARB
to achieve an annual average inflation over the period 2000:Q1-2016:Q3, that lies within its 3 - 6 %
inflation targeting band, however the volatility is not substantially different to the MAT, EXT and
IIT regimes. Moreover, the IT framework is consistent with a higher mean annual output growth and
lower output volatility. For a related and formal perspective, see Kabundi et al. (2019) who estimate
6
a Phillips curve for South Africa in a time-varying parameter framework. They find that inflation
expectations in South Africa after 2008 have reduced because of good policy of an IT framework and
good luck due to the recessionary conditions of the GFC; along with a global reduction in energy
and food prices. Kabundi et al. (2019) also document factors influencing inflation dynamics and the
relationship between inflation and unemployment in South Africa over the period 1994:Q1-2014:Q1.
Lastly, table 1 shows a lower mean depreciation of the nominal effective exchange rate over the IT
regime, however the volatility is not substantially different to the MAT, EXT and IIT regimes. The
repo-rate exhibits a lower mean change and lower volatility over the IT regime, relative to the MAT,
EXT and IIT regimes and with an average policy rate adjustment of about 7 basis point reduction
from one quarter to another. Nonetheless, our research approach may reveal a wider information set
concerning which variables are the main drivers of South Africa’s macroeconomic fluctuations and this
may influence and help monetary policy conduct because this wide information set influences target
variables - such as inflation and output - of monetary policy conduct.
3 Model
We use Nimark’s (2009) commodity based small open economy DSGE model and thus use a small
open economy DSGE model that characterizes the salient features of the South African economy. For
our purposes, we only present the important parts of the log-linearised model that are relevant to our
study and that are consistent with Nimark (2009).2
We express the consumption Euler equation as:
ct =γ
γ − η − γηEtct+1 −
η (1− γ)
γ − η − γηct−1 −
1
γ − η − γη(rt − Etπt+1) + εct , (1)
where ct is consumption, γ is the inverse elasticity of intertemporal substitution, η is the degree
of habit formation, rt −Etπt+1 is the expected real interest rate and εct is a preference shock process.
We express mineral commodity export demand as:
xet = y∗t − δepwt + εxet , (2)
where xet are mineral commodity exports, y∗t is foreign output, δe is the price elasticity of commod-
ity exports, pwt is the relative price of world primary commodity exports and εxet is the commodity
exports shock process. The relative price of world primary commodity exports, are expressed as:
pwt = pwt−1 + πt − π∗t −∆st, (3)
2See Nimark (2009) for detailed derivations.
7
where πt is consumer price (CPI) inflation, st are the the terms of trade and π∗t is foreign consumer
price inflation. Domestic production yt is allocated between consumption and primary commodity
exports. Thus, we express the consumption Euler equation as an open economy IS curve as:
yt =γ
γ − η − γηEtyt+1 −
αγ
γ − η − γηEt∆y
∗t+1 −
αγδeγ − η − γη
Et∆pwt+1 −1
γ − η − γη(rt − Etπt+1)
(4)
− αη (1− γ)
γ − η − γη∆xet −
η (1− γ)
γ − η − γηyt−1 + εct
where α is the share of imports in consumption. Households allocate their savings to domestic
and foreign currency bonds. Thus, we express the uncovered interest parity condition in a similar
manner to Schmitt-Grohe and Uribe (2003) and Justiniano and Preston (2010) because this captures
an imperfect international securities market between domestic and foreign bonds. Thus, we express
the uncovered interest parity condition as:
qt = Etqt+1 − (rt − Etπt+1) +(r∗t − Etπ∗t+1
)+ κbt + εqt , (5)
where qt is the real exchange rate, r∗t is the foreign interest rate, κ is the debt elasticity with
respect to interest rate risk premium, bt is the net foreign debt position and εqt is the risk premium
shock process.
We express the Phillips curve for domestically produced goods (domestic inflation) as follows:
πht = µhfEtπht+1 + µhbπ
ht−1 + λhmcht + επ
h
t , (6)
and the Phillips curve for imported goods (imported inflation) as follows:
πit = µifEtπit+1 + µibπ
it−1 + λimcit + επ
h
t , (7)
where πht and πit are domestic and imported inflation respectively, mcht is the real marginal cost
of the domestic producers and mcit is the real marginal cost of imported goods. The cost push shock
term επh
t is common to both domestic producers and importers. The parameters in the Phillips curve
for domestically produced goods and imported goods are expressed as follows:
µlf ≡βθl
θl +∼ω (1− θl (1− β))
µlb ≡∼ω
θl +∼ω (1− θl (1− β))
8
λl ≡
(1− ∼ω
) (1− θl
) (1− βθl
)θl +
∼ω (1− θl (1− β))
where l ε {h, i} , µlf is the parameter attached to the forward looking variables and µlb is the
parameter attached to the backward looking variables. The household’s subjective discount factor
is β ε (0, 1) . We assume a Calvo (1983) price setting mechanism for both domestic producers and
importing firms, where a proportion θh and θi of domestic producers and importing firms respectively,
do not change prices in a given period. However, we assume that a proportion∼ω of both domestic
producers and importers do change prices using a rule of thumb that links their price to lagged inflation
in their respective sectors. Thus, in line with Nimark (2009), domestic CPI inflation is a weighted
average of inflation of domestically produced goods and inflation of imported goods, expressed as
follows:
πt = (1− α)πht + απit (8)
We modify the Taylor-type rule used by Nimark (2009) and thus assume that monetary policy
conduct is characterized by a rule that incorporates exchange rate changes. The role of exchange rate
fluctuations may be relevant for monetary policy conduct of EMEs and in particular, to the extent to
which exchange rate fluctuations impact inflation, see Taylor (2000) and Obstfeld (2014). Thus our
Taylor-type rule is expressed as:
rt = ρrrt−1 + (1− ρr) [γ1πt + γ2yt + γ3∆et] + εrt , (9)
where rt is the policy rate, yt is the output gap and ∆et is the percentage change in the nominal
effective exchange rate. The parameters that capture interest rate smoothing and policy rate adjust-
ments to consumer price inflation, the output gap and the percentage change in the exchange rate are
ρr, γ1, γ2, γ3 respectively. The shock term that enters the policy rule is εrt .
We assume the remaining foreign variables, y∗t foreign output, r∗t foreign interest rate and π∗t
foreign consumer price inflation follow AR(1) autoregressive processes. In our model, we have nine
shock terms and they evolve as AR(1) autoregressive processes and they are as follows: an export
shock, preference shock, imported-cost inflation shock, technology shock, monetary policy shock, risk
premium shock, foreign inflation shock, foreign output shock and foreign interest rate shock. Thus,
we present the remaining model equations in table 2. Furthermore, for our purposes, regime switches
are introduced into eqns. (1) to (9), the remaining equations in table 2 and all the shock terms are
regime-dependent.
9
Table 2: Remaining model equations
Description Equation
Terms of trade st = st−1 − πht + πft
Exchange rate depreciation ∆et = qt − qt−1 + πt − πfit
Net foreign assets nfat =1
βnfat−1 − α (qt − αst) + yt − ct
Foreign variables
Foreign inflation π∗t = ρπ∗π∗
t−1 + επ∗t
Foreign output y∗t = ρy∗y∗t−1 + εy
∗
t
Foreign interest rate r∗t = ρr∗r∗t−1 + εr
∗t
Shock processes
Export shock εxet = ρxeεxet−1 + εxet , εxet ∼ N
(0, σ2
εxe)
Preference shock εct = ρcεct−1 + εct , ε
ct ∼ N
(0, σ2
εc)
Import cost shock επi
t = ρπiεπit−1 + επ
i
t , επi
t ∼ N(
0, σ2
επi
)Technology shock εzptt = ρzpε
zpt−1 + εzpt , ε
zpt ∼ N
(0, σ2
εzp)
Monetary policy shock εrt = ρrεrt−1 + εrt , ε
rt ∼ N
(0, σ2
εr)
Risk premium shock εqt = ρqεqt−1 + εqt , ε
qt ∼ N
(0, σ2
εq)
Foreign inflation shock επ∗t = ρπ∗επ
∗t−1 + επ
∗t , επ
∗t ∼ N
(0, σ2
επ∗
)Foreign output shock εy
∗
t = ρy∗εy∗
t−1 + εy∗
t , εy∗
t ∼ N(
0, σ2εy
∗
)Foreign interest shock εr
∗t = ρr∗ε
r∗t−1 + εr
∗t , εr
∗t ∼ N
(0, σ2
εr∗
)
4 Regime-Switching Environment and Empirical Implementation
4.1 Solution and Estimation Method
We use a MS-DSGE approach because it allows for different policy rate responses to target variables
in different policy regimes and this set-up characterizes a rational expectations model where changes
in monetary policy rule parameters are allowed to influence expectations formation of agents.3 Thus,
our small open economy model is cast into a MS-DSGE model in a state space representation form
as:
v ≡[bt+1 (yt+1) , ft+1 (yt+1) ,
∼st (yt) , pt (yt) , bt (yt) , ft (yt) , pt−1, bt−1, εt, θyt+1
]′, (11)
where bt is a vector of forward and exogenous variables of dimension mbx1, ft is vector of forward
looking variables of dimension mfx1, pt is a vector of exogenous variables of dimension mpx1,∼st is a
vector of current variables of dimension msx1, εt is a vector of shocks of dimension mεx1 and θyt+1is
3We estimate our model using RISE, a MATLAB package designed to solve and estimate regime-switching DSGE
models. RISE refers to Rationality in Switching Environment software developed by Maih (2015). This package can be
obtained from https://github.com/jmaih/RISEtoolbox.
10
a vector of the matrices with switching parameters in the model and of dimension mθx1.
Our model is solved using Maih’s (2015) efficient perturbation algorithm because it allows us to
determine a single equilibrium condition relevant for economic analysis.4 This improves on the minimal
state variable algorithm proposed by Farmer et al. (2015).5 We apply the efficient perturbation
method algorithm on equations (1) - (9) and this results in (11) grouping the parameters into lagged
and current variables, as well as forward-looking endogenous and exogenous variables. The next step
is to estimate the first-order perturbation solution to yield a regime-dependent solution of the form:
Υyt ≡ Υyt (zt) + Υyt (zt − zt) , (12)
where Υyt is an approximation rule and zt = [pt−1, bt−1, θ, εt] is a vector of state variables of
dimension mzx1, ztis a vector of the steady state values of the state variables and θ is a vector of the
pertubation parameters.
Following this, the transition matrix is governed by a benchmark P probability matrix characterized
as:
P =
p11 p12
p21 p22
, (10)
where p12 = prob(∼st+1 = 2|∼st = 1) is transition probability from state 1 to state 2.
We estimate our model with Bayesian methods using a Markov-Chain Monte Carlo (MCMC)
algorithm. In particular, we use the random walk Metropolis-Hastings algorithm because in estimating
DSGE models, some of the conditional distributions are not obtainable in closed form, see Herbst and
Schorfheide (2015). The parameters of the prior distribution are set and a new set of parameters is
drawn from the random walk candidate density. Thereafter, the likelihood and the prior distribution
at the draw value of the parameters, are evaluated with the aim of generating the posterior distribution
and estimating the marginal density from the data.
We adapt the Kim filter algorithm rather than the Kalman filter algorithm because the Kim filter
is suitable in a large set of MS-DSGE models to compute the posteriors and marginal densities. The
Kim filter is a combination of the Kalman and Hamilton filters, where the possible paths are collapsed
through averaging at each step of the likelihood (Kim and Nelson,1999). This keeps the computation
of the likelihood tractable.
4This set-up accounts for lagged endogenous variables and regime switches that depend on current and future
regimes. Furthermore, the set-up is suitable for log-linearised rational expectations models, where parameters are
allowed to switch across regimes.5Davig and Leeper (2007) and Farmer et al. (2015) solution algorithms generate multiple equilibria when one regime
produces more volatility relative to another regime, and this generates indeterminacy.
11
4.2 Data
Our sample period is 1981:Q1 - 2016:Q3 and we choose this sample because it covers the wide array
of monetary policy regimes such as the 1981:Q1 - 1995:Q1 monetary-aggregate targeting (MAT)
and exchange rate targeting (EXT) regime, the 1995:Q2 - 2000:Q2 informal inflation targeting (IIT)
regime and the 2000:Q2 - 2016:Q3 explicit inflation targeting (IT) regime. In our set-up, we have nine
observable variables, where six are domestic (South African) observable variables and these are as
follows: real GDP seasonally adjusted, real household consumption expenditure seasonally adjusted,
gold exports seasonally adjusted as a proxy for mineral commodity exports, a policy rate in the form
of the repo-rate, consumer price (CPI) inflation measured as the quarterly change in the seasonally
adjusted consumer price index and a nominal effective exchange rate.
The remaining three observable variables are foreign variables in the form of the US interest rate
(three month Treasury bill rate), US real GDP seasonally adjusted and US CPI inflation. We use
US variables as foreign variables because the US is the world’s largest economy and has been one of
South Africa’s main trade partners for many years. Data for South African CPI, the repo-rate, US
CPI, US interest rate and US real GDP are derived from the IMF’s International Financial Statistics
(IFS) database. Data for South African real household consumption expenditure, gold exports and
the nominal effective exchange rate are derived from the SARB’s database.
We measure South African and US CPI inflation using quarterly changes on an annual basis in
each country’s consumer price index. The percentage change in the nominal effective exchange rate
is the quarterly percentage change in the South African rand measured as a trade-weighted average
of twenty major trading partners of South Africa. We transform all the data series into their growth
rates by taking the first difference of their natural log and multiply by 100 to standardize the variables.
The policy rate (repo-rate) and foreign interest rate are measured as per cent per annum. Lastly, we
construct the South African and US output gaps using the HP filter.
4.3 Priors and Markov Switches
This section presents the number of Markov switches introdued into our model and the priors of
the structural and policy regime switches. Firstly, we characterize and assign values to the model’s
structural parameters. The discount factor β is fixed at 0.97 and this translates into a long run annual
average real interest rate of 3.09 per cent. The elasticity of labour supply ψ =1
ϕis set at 1.30 to
ensure that workers are willing to increase the number of hours worked in response to wage changes.
The debt elasticity with respect to interest rate risk premium κ is fixed at 1.45 per cent, and this
delivers a default spread of 145 basis points as estimated by Allan Haung country risk premiums.6
The share of imported goods in consumption α and price elasticity of primary commodity exports δe
6See, www.sjsu.edu/faculty/watkins/econ202/risk.htm.
12
are set at 0.24 and 0.14 respectively and these values are based on a five-year average concentration
and diversification indices from UNCTAD.7
We set the elasticty of substitution between home and foreign goods ω to 1.5 so that the markup
for South Africa is comparable to the US and euro area estimates (Burger and Du Plessis (2013)). In
line with Justiniano and Preston (2010), we fix the following parameters at a value of 0.5: the price
indexation for home goods δh, the price indexation for foreign produced goods δf , the price adjustment-
cost for home produced goods φh, the price adjustment-cost for foreign produced goods φf , the degree
of habit formation in consumption λ and the inverse elasticity of intertemporal substitution γ =1
τ.
We assume that the prior distributions of policy parameters switch because of different monetary
policy regimes. Our prior choices for the different monetary policy regimes are in line with Ortiz and
Sturzenegger (2008) and Peters (2014) and the historical monetary policy regime outline of the SARB
by Baaziz et al. (2013).
We define regime 1 as a regime where the prior policy rate responses to inflation and output are
small. We define regime 2 as a regime where the prior policy rate responses to inflation and output
are large. We also assume that the prior policy rate responses to exchange rate changes are large in
regime 1 and small in regime 2 because the SARB had explicitly targeted the exchange rate before
the IIT and IT regime and does not target the exchange rate over the IT regime. We set the prior for
the policy smoothing parameter ρr at 0.60 and the policy rate shock term εrt is set at 0.15. Following
Bianchi (2012) and to capture the effect that regimes are persistent, we set the priors for the transition
matrices at 0.95 in each regime.
We assume that the economy faces switches in primary commodity export shocks. Thus for our
purposes, we define regime 1 as a regime where the economy experiences low volatility in primary
commodity shocks σpwt with a prior of 0.37. Whereas, we define regime 2 - which is consistent with
greater exchange rate flexibility and a higher degree of capital account openness - as a regime where
the economy experiences high volatility in primary commodity shocks σpwt with a prior value of 0.87
and this value is in line with Nimark (2009). In addition, the prior distribution of the structural
shocks processes follow a beta distribution with a prior value of 0.60. The priors of the stuctural
shocks variances, follow a Weibull distribution with a prior value of 0.18.
Lastly, following Liu et al. (2011) and Bjørnland et al. (2016), we depart from the normal practice
of the direct usage of prior means and standard deviations. Thus, we use quantiles distribution of the
statistical estimates of the prior means to recover the hyperparameters with 90 percent probability
interval of the distributions.8
7unctad.org/en/pages/statistics.aspx8See, Gelman et al. (2014) for a detailed discussion and treatment of this approach. Furthermore, Gelman et al.
(2014: 11) provide details about the credible intervals of the posterior densities, model checking and improvements.
13
5 Empirical Results
To establish the appropriate DSGE model for the South African economy that has undergone mon-
etary policy regime changes and has been subject to different economic disturbances with changing
volatilities, we examine five alternative DSGE models. These alternative DSGE models are in the
following form: (i) a model that does not allow for regime switches in the policy paramaters and does
not allow for regime switches in the volatility of shocks (Constant DSGE), (ii) a model that allows
for simultaneous regime switches in the policy paramaters and in the volatility of shocks (VolPolSame
DSGE), (iii) a model that allows for independent regime switches in the policy paramaters and in
the volatility of shocks (VolPolInd DSGE), (iv) a model that only allows for regime switches in the
volatility of shocks (VolOnly DSGE) and (v) a model that only allows for regime switches in the policy
paramaters (PolOnly DSGE). Thus, to establish the model that best fits the data, we use the Akaike
information criterion (AICc) and Bayesian information criterion (BIC). We also use the log-marginal
densities (log −MDD) to characterize the estimated DSGE model that best fits the data and thus
the model with the largest marginal likelihood is considered as the best fit model. The AICc, BIC
and the log −MDD statistics are reported in Table 3.
Table 3: Statistics for model comparison
Constant VolPolSame VolPolInd. VolOnly PolOnly
BIC 4025.18 39545.52 507480.22 3694.32 3921.72
AICc 3948.09 39459.89 507480.22 3584.26 3837.74
Log-posterior -1930.75 -19656.20 -253618.59 -1738.04 -1866.62
Log-lik -1801.89 -19618.26 -253617.24 -1679.07 -1792.15
Log-prior -128.86 -37.94 -1.3533 -58.97 -74.47
Log-MDD(Laplace) -2176.10 -19930 -253920.87 -1926.40 -2083.49
Note: Constant=structural shocks and policy parameters are time-invariant;
VolPolSame=structural shocks and policy parameters switch simultanteous;
VolPolInd=structural shocks and policy parameters switch independent;
VolOnly=only volatility in the structural shocks are regime switching;
PolOnly=policy parameters only are regime switching.
Based on the log posterior densities, the data is adjusted to obtain the AICc and BIC. We find
that the VolOnly DSGE model, has the lowest AICc and BIC scores indicating that this model is
parsimonious and is a better fit as compared to the other models. Furthermore, using the log−MDD
statistic, we also find that the VolOnly DSGE model, outperforms all the other model specifications.
Our findings with respect to the appropriate MS-DSGE model, are similar to Lui et al. (2011),
however they analyse the US economy. To validate our results, we run a number of robustness tests
to determine the appropriateness of the best fit model and we report these results in table 4.
14
Table 4: Robustness check: Statistics for model comparison
BIC AICc Log-MDD Log-posterior Log-like Log-prior
MEX 6416.20 6326.37 -3398.25 -3098.98 -3023.97 -75.01
REM 3826.86 3740.65 -2010.87 -1814.23 -1721.64 -92.59
VolOnly 3694.32 3584.26 -1926.40 -1738.04 -1679.07 -58.97
Note: MEX=includes merchandise exports and assume the structural shocks are
regime switching. REM=restricted model, that is, the original model of (?) and
assume the structural shocks are regime switching, VolOnly=volatility only in the
structural shocks are regime switching.
Thus, table 4 shows that the model with VolOnly DSGE model, continues to outperform all other
robustness check specifications. We interpret these findings as suggesting that policy authorities - in
particular the SARB - may potentially take into account the volatility of shocks and the potential
switches in the volatility of shocks.
5.1 Parameter Estimates
Following our specifications tests on the appropriate MS-DSGE model and robustness tests, we report
the parameter estimates of our VolOnly and PolOnly DSGE models because these are the two best
performing models. For comparison purposes, we also report the estimated parameters of our Constant
DSGE model. Table 5 and 6 report the posterior mode of the structural parameters and shock process
parameters and in particular, column 5, 6 and 7 of table 5 and 6, report the parameter estimates of our
Constant, VolOnly and PolOnly DSGE models respectively. We first report the estimated posterior
modes of the monetary policy rule parameters and the structural shocks based on the relevant DSGE
models in table 5.
We find a high degree of interest rate smoothing by the SARB, with values of ρr = 0.89, ρr = 0.98
and ρr = 0.99, based on the Constant, VolOnly and PolOnly DSGE models respectively. These values
show that a low percentage change in the SARB’s target variables - for example inflation - are reflected
in the repo-rate within the quarter. This high degree of interest rate smoothing is not unique to the
SARB and is not unique to EME central banks, see Clarida et al. (1998) for evidence with respect to
the Bundesbank, the Bank of Japan, the Fed, the Bank of England, the Bank of France and the Bank of
Italy, Castro (2011) for evidence with respect to the European Central Bank (ECB) and Ruhl (2015)
for evidence with respect to the Bundesbank and the ECB and Drygalla (2019) for evidence with
respect the Czech National Bank and the Narodowy Bank Polski. Concerning policy rate responses
to inflation, the output gap and the exchange rate, table 5 shows that based on the alternative DSGE
model specifications, the SARB allocates the largest weight towards inflation stabilization, a lower
15
Table 5: Posterior mode of monetary policy parameters and structural inno-
vations
Prior Posterior
Par. Distr. 5% 95% Constant Volatility Polonly 5% 95%
ρr B 0.60 0.90 0.89 0.98 0.99 0.48 3.97
γ1 G 2.19 5.00 1.26 1.45 2.16 0.92 2.44
γ2 G 0.30 3.00 0.63 0.71 2.05 0.69 1.01
γ3 G 0.30 3.00 0.34 0.31 0.16 0.69 1.01
voltp,12 B 0.95 0.99 - 0.42 0.00 0.43 0.96
voltp,21 B 0.95 0.99 - 0.94 0.26 0.43 0.96
σr(vol, 1) W 0.18 1.00 0.36 0.04 0.00 0.13 1.54
σr(vol, 2) W 0.23 1.00 - 0.05 - 0.13 1.54
σd(vol, 1) W 0.18 1.00 0.76 0.95 - 0.13 1.54
σd(vol, 2) W 0.27 1.00 - 1.03 0.00 0.13 1.54
σs(vol, 1) W 0.37 1.00 2.77 1.29 0.00 0.13 1.54
σs(vol, 2) W 0.87 1.00 - 1.96 0.03 0.13 1.54
σz(vol, 1) W 0.18 1.00 0.35 1.81 0.70 0.13 1.54
σz(vol, 2) W 0.23 1.00 - 0.87 - 0.13 1.54
σq(vol, 1) W 0.37 1.00 0.33 0.68 2.71 0.13 1.54
σq(vol, 2) W 0.87 1.00 - 0.67 - 0.13 1.54
σe(vol, 1) W 0.37 1.00 0.54 1.36 1.52 0.13 1.54
σe(vol, 2) W 0.87 1.00 - 1.62 - 0.13 1.54
σfi(vol, 1) W 0.18 1.00 0.78 1.21 1.78 0.13 1.54
σfi(vol, 2) W 0.23 1.00 - 1.43 - 0.13 1.54
σfy(vol, 1) W 0.18 1.00 0.57 0.68 0.72 0.13 1.54
σfy(vol, 2) W 0.23 1.00 - 0.18 - 0.13 1.54
σfr(vol, 1) W 0.18 1.00 0.17 0.20 0.18 0.13 1.54
σfr(vol, 2) W 0.23 1.00 - 0.18 - 0.13 1.54
Note: B=beta distribution, G=Gamma distribution and W=Weibull distributin. See
Gelman et al (2014:11) for exposition on why some of the posterior densities may be
outside the Bayesian credible intervals.
weight towards output gap stabilization and the lowest weight towards the exchange rate. Our findings
are consistent with other estimated DSGE models using quarterly data, where the SARB allocates
the largest weight to inflation, a lower weight to the output gap and the lowest weight to the exchange
rate, see Ortiz and Sturzenegger (2008), Alpanda et al. (2010) and Peters (2014).
Concerning the remaining structural parameters and with respect to the VolOnly and PolOnly
DSGE models, we find that the posterior mode values for the inverse elasticity of labour supply are
ψ = 1.18 and ψ = 1.65 respectively, whereas ψ = 0.49 for the PolOnly DSGE model. These value
are relatively close to the estimated values of Justiniano and Primiceri (2008) for the US with a value
of 1.59 and also the estimated value of Alpanda et al. (2010) for South Africa with a value of 1.45.
16
Based on the VolOnly, Constant and PolOnly DSGE models, we find that the values for the posterior
mode for habit formation in consumption are λ = 0.12, λ = 0.014 and λ = 0.04 respectively. These
values are substantially lower than the values reported by Justiniano and Primiceri (2008) for the US
with a value of 0.81 and Alpanda et al. (2010) for South Africa with a value of 0.83. In the context
of habit formation in consumption, our parameter estimates differ to Alpanda et al. (2010) who also
examine South Africa. This may be due to several reasons. For example, we use a different structural
model that accounts for primary commodity exports, switches in the monetary policy parameters and
switches in the volatility of structural shocks and we use a longer sample period.
Based on the VolOnly and PolOnly DSGE model, we find that the values for the posterior mode
for the share of imported goods in consumption are the same with parameter value α = 0.09 and for
the Constant DSGE model α = 0.12. These value suggests a low degree of openness. Furthermore,
based on the VolOnly, PolOnly and Constant DSGE model, the values for the posterior mode for
price adjustment cost for domestically produced goods are φh = 0.10, φh = 0.013 and φh = 0.008
respectively and for imported goods the values are φf = 1.22, φf = 0.83 and φf = 1.41 respectively.
Thus suggesting that the pricing associated with domestically produced goods, adjusts more rapidly
as compared to the pricing associated with imported goods. Using the VolOnly and Constant DSGE
model, the values for the posterior mode of the price indexation for home produced goods are δh =
0.21 and δh = 1.92 respectively, and thus the Constant DSGE model indicates a higher degree of price
stickiness. The estimated price indexation for imported goods based on the VolOnly and Constant
DSGE model, are δf = 0.01 and δf = 0.05 respectively and thus indicate a much lower degree of
stickiness as compared to home produced goods.
Table 6 shows that the variances of shocks corresponding to import-cost inflation, preferences and
foreign inflation, are larger than the variances of shocks corresponding to technology and exports.
However, we observe different findings for different regimes, namely regime 1 and 2. In our context,
regime 1 corresponds to a regime where (i) the prior policy rate responses to inflation and output
are small and (ii) the prior policy rate responses to exchange rate changes are large. Furthermore,
in regime 1, we assume the economy experiences low volatility in mineral commodity export shocks.
Regime 2 corresponds to a regime where (i) the prior policy rate responses to inflation and output are
large and (ii) the prior policy rate responses to exchange rate changes are small. In addition, in regime
2, we assume the economy experiences high volatility in mineral commodity export shocks. Concerning
the VolOnly DSGE model, table 6 shows that the export shock variance in regime 2 is σe (vol, 2) = 1.62
and this value is larger than the export shock variance in regime 1 σe (vol, 1) = 1.36. Nonetheless,
the export shock variances are large in both regimes and this may have substantial implications on
South African macroeconomic fluctuations and monetary policy conduct because of South Africa’s
high degree of mineral commodity export dependence. Furthermore, the estimated posterior mode
variances for the transition probability of regime 2 is voltp,12 = 0.95 and this value is larger than the
estimated posterior mode variance for the transition probability of regime 1 voltp,21 = 0.42.
17
Table 6: Posterior mode of structural and shock process parameters
Prior Posterior
Par. Distr. 5% 95% Constant Volatility Polonly 5% 95%
λ G 0.54 1.50 0.014 0.12 0.04 0.06 4.59
τ G 0.54 1.50 1.93 1.17 1.05 0.06 4.59
α G 0.54 1.50 0.12 0.09 0.09 0.06 4.59
ω G 0.54 1.5 1.54 1.23 1.89 0.06 4.59
β B 0.10 2.00 0.06 0.22 0.14 0.18 3.94
φh G 0.58 1.00 0.008 0.10 0.013 0.25 1.58
φf G 0.58 1.00 1.41 1.22 0.83 0.25 1.58
δh G 0.54 1.50 1.92 0.21 0.11 0.06 4.59
δf G 0.54 1.50 0.05 0.01 0.003 0.06 4.59
δe G 0.54 1.50 0.003 0.012 0.004 0.06 4.59
ψ G 0.54 1.50 1.65 1.18 0.49 0.06 4.59
κ G 0.05 1.50 0.001 0.002 0.002 0.001 1.58
ρd B 0.05 0.90 0.81 0.86 0.89 0.28 8.97
ρs B 0.05 0.90 0.89 0.83 0.85 0.28 8.97
ρz B 0.05 0.90 0.96 0.85 0.87 0.28 8.97
ρq B 0.05 0.90 0.96 0.93 0.91 0.28 8.97
ρe B 0.05 0.90 0.98 0.99 0.99 0.28 8.97
ρfi B 0.05 0.90 0.19 0.21 0.25 0.28 8.97
ρfy B 0.05 0.90 0.86 0.80 0.89 0.28 8.97
ρfr B 0.05 0.90 0.45 0.61 0.21 0.28 8.97
Note: B=Beta distribution, G=Gamma distribution. See Gelman et al (2014:11)
for exposition on why some of the posterior densities may be outside the Bayesian
credible intervals.
5.2 Evolution of South African Macroeconomic Outcomes
In this section, we report and interpret the generalized dynamic responses, variance and historical
decompositions of the observable variables based on the VolOnly and PolOnly DSGE models because
these are the two best performing models.
Generalized Dynamic Responses
In this section, we report and interpret the generalized dynamic responses following a positive
monetary policy shock and positive (mineral commodity) export shock in figure 1. Furthermore, we
also report and interpret the generalised dynamic responses following a positive risk premium shock
and positive import cost inflation shock in figure 2. Lastly, we report and interpret the generalised
dynamic responses following a positive preference shock and positive technology shock in figure 3. In
all cases, the solid line dynamic responses correspond to our VolOnly DSGE model and the dashed
line dynamic responses correspond to our PolOnly DSGE model.
18
The first block of figure 1 reports the generalised dynamic responses of the observable variables,
following a positive monetary policy shock. A one standard deviation monetary policy shock, generates
a decline in real consumption growth of about 0.2 percent when using both the VolOnly and PolOnly
DSGE model. Furthermore, when using the VolOnly DSGE Model, there is an associated reduction
in the output gap of about 0.1 percent. The downward trajectory of the output gap, is consistent
with a reduction in consumer price inflation when using both the VolOnly and PolOnly DSGE model.
Furthermore, the positive monetary policy shock results in an exchange rate appreciation of about 2
percent with an associated 0.4 percent reduction in import-cost inflation when using both models.
The second block of figure 1 reports the generalised dynamic responses of the observable variables,
following a positive export demand shock. A one standard deviation export demand shock generates a
reduction in the policy rate of about 0.007 percent when using the VolOnly DSGE model and virtually
no response when using the PolOnly model. However, based on both models, there is an associated
upward trajectory in the output gap of about 0.045 per cent. The reduction in the policy rate may
serve as an incentive towards higher gold extraction, reinforcing the initial increase in export demand,
increase gold export revenues and thus anchor output.
The first and second blocks of figure 2 report the generalised dynamic responses of the observable
variables, following a positive risk premium shock and positive import-cost inflation shock respectively.
A one standard deviation risk premium shock, generates on average an exchange rate depreciation
that exceeds 5 per cent and there is an associated 1 per cent increase in import-cost inflation, however
this effect gradually decays within 12 quarters, when using both models. Furthermore, the increase in
the risk premium, improves the terms of trade by about 2.5 percent, when using both models. On the
other hand, a one standard deviation import-cost inflation shock, generates an increase in consumer
price inflation of about 4 percent, a decline in real consumption growth of about 2 percent and also
generates a decline in output of about 0.2 per cent when using the VolOnly DSGE model.
The first and second blocks of figure 3 report the generalised dynamic responses of the observ-
able variables, following a positive preference shock and a positive technology shock respectively. A
one standard deviation preference shock, generates an increase in real consumption growth of about
2 percent when using both the VolOnly and PolOnly DSGE model, however this effect eventually
stabilizes within 12 quarters. Furthermore, the preference shocks generates an increase in output of
about 0.85 percent when using the VolOnly DSGE model and a marginal effect with the PolOnly
DSGE model. There is also an associated average increase in consumer price inflation of about 2
per cent when using both models. Following the preference shock, the exchange rate appreciates by
about 2 percent and this generates a gradual decline in net gold exports by about 0.15 percent over
12-15 quarters, possibly due to the loss in competitiveness. This result holds when using both the
VolOnly and Constant DSGE model. On the other hand, a one standard deviation technology shock
has a positive impact because it gradually increases net gold exports, a jump in consumption and
19
output with an associated gradual adjustment to the original values, along with reducing consumer
price inflation and moreso when using the VolOnly DSGE model.
In general and in the context of figures 1 - 3 that report the dynamic responses to shocks, we
observe that external shocks in the form of import-cost inflation, risk premium and export shocks,
have a larger impact on South African macroeconomic dynamics as compared to a monetary policy
shock and this result holds consistently when using both the VolOnly and PolOnly DSGE model,
however, moreso for the VolOnly DSGE model. Thus, our results are in line with Nimark (2009)
and Baxa et. al. (2014), who also find that external factors are important for the macroeconomic
outcomes of some small open inflation targeting economies that are commodity export dependent.
20
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Figure 1: Dynamic responses to monetary policy and export shocks
Note: First block is a policy shock and last block is an export shock
23
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Figure 2: Dynamic responses to risk premia and import-cost inflation shocks
Note: First block is a risk premia shock and last block is an import cost inflation shock
24
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0
5
10Real Exchange rate
0 6 12 18 24 30 36-60
-40
-20
0relative price of world exports
0 6 12 18 24 30 36-5
0
5
10Terms of Trade
0 6 12 18 24 30 360
0.2
0.4
0.6Net gold exports
0 6 12 18 24 30 36-1
0
1
2Output gap
0 6 12 18 24 30 36-1
-0.5
0
0.5
1Preference shock process
Figure 3: Dynamic responses to preference and technology shock
Note: First block is a preference shock and last block is a technology shock
25
23
Variance and Historical Decompositions
To establish the extent of interaction between variables over a particular forecast horizon, we us
the variance decompositions of the variables in our analysis. We report the variance decompositions
in figure 4 and 5 and these correspond to our VolOnly and PolOnly DSGE models. The left panel
of figure 4 shows that technology and import-cost inflation shocks are the main contributors to the
volatility of the monetary policy rate and over a longer forecast horizon, the contribution of import-
cost inflation is larger. The right panel of figure 4 shows similar results because technology and
import-cost inflation shocks are the main contributors to consumer price inflation volatility. With the
SARB’s primary objective being price stability, it seems that import-cost inflation’s contribution to
consumer price inflation, also contributes to monetary policy rate volatility. The right panel of figure
5 shows that risk premium shocks are the main contributor to exchange rate variability, as compared
to monetary policy rate shocks. The results of the variance decompositions suggest that the major
drivers of macroeconomic volatility in the South African economy are external factors. These findings
fit the theme of an EME with a volatile currency and that is susceptible to external events such as
volatile portfolio flows.
Our historical decompositions also correspond to our VolOnly and PolOnly DSGE models. The
left panel of figure 6 shows the historical decomposition of the monetary policy rate and we observe
that over the period 1982-1990, import-cost inflation and export shocks substantially contribute to
the monetary policy rate; along with risk premium and technology shocks also contributing to the
monetary policy rate over the period 1980-1994. Furthermore, over the period 1990-2016, export and
preference shocks contribute to the monetary policy rate, however and over the period 1999-2016 risk
premium and technology shocks contribute to the monetary policy rate. The right panel of figure 6
shows the historical decomposition of consumer price inflation. We observe that import-cost inflation
shocks are the main drivers of consumer price inflation and over the period 1986-2008, import-cost
inflation shocks have resulted in persistent upswings and downswings in consumer price inflation. After
the most recent global financial crisis, there is a reduction in the swings in consumer price inflation
and this may be because of a greater degree of trade integration of the South African economy with
the rest of the world. The right panel of figure 7 reports the historical decomposition of the nominal
effective exchange rate depreciation. We observe that over the period 2008-2016, import-cost inflation,
export and risk premium shocks have contributed to an exchange rate depreciation.
The historical decompositions show that external factors influence important macroeconomic vari-
ables such as output, the monetary policy rate, consumer price inflation and the exchange rate. These
findings suggest that external factors, have a larger role to play in South African macroeconomic
dynamics and may influence its monetary policy conduct because these factors influence the target
variables associated with monetary policy adjustments.
24
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100Policy rate(volatility Only)
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100Policy rate(policy Only)
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100CPI Inflation(volatility Only)
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100CPI Inflation(policy Only)
Figure 4: Variance deompositions of policy rate and CPI inflation
Note: Left panel is monetary policy rate and right panel is consumer price inflation
26
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100Net gold exports(volatility Only)
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100Net gold exports(policy Only)
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100Exchange rate depreciation(volatility Only)
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
1 4 7 10 13 16 19 220
10
20
30
40
50
60
70
80
90
100Exchange rate depreciation(policy Only)
Figure 5: Variance decompositions of net gold exports and exchange rate depreciation
Note: Left panel is net gold exports and right panel is exchange rate depreciation
27
25
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-1
-0.5
0
0.5
1
volatility Only
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
policy Only
y0
ss
sig
trend
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-25
-20
-15
-10
-5
0
5
10
15
20
25
volatility Only
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-20
-15
-10
-5
0
5
10
15
20
policy Only
y0
ss
sig
trend
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
Figure 6: Historical decompositions of policy rate and consumer price inflation
Note: Left panel is monetary policy rate and right panel is consumer price inflation
28
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-50
-40
-30
-20
-10
0
10
20
30
40
50
volatility Only
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-15
-10
-5
0
5
10
15
policy Only
y0
ss
sig
trend
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-150
-100
-50
0
50
100
150
200volatility Only
1981
Q1
1985
Q3
1990
Q1
1994
Q3
1999
Q1
2003
Q3
2008
Q1
2012
Q3
-150
-100
-50
0
50
100
150
policy Only
y0
ss
sig
trend
Preference shock
Export shock process
Foreign inflation shock
Foreign interest rate shock
Foreign output shock
Technolgy shock
Risk premia shock
Monetary policy shock
Import cost shock
Figure 7: Historical decompositions of net gold exports and exchange rate depreciation
Note: Left panel is net gold exports and right panel is exchange rate depreciation
29
26
6 Conclusion
We examine the impact of shocks on South Africa’s macroeconomic fluctuations, within the context of
changes in monetary policy regimes and changes in the volatility of shocks. By incorporating a mineral
commodity export sector in a regime dependent framework, we find that external shocks in the form
of exports, import-cost inflation, risk premium, preferences and technology shocks, account for a large
proportion of macroeconomic fluctuations in our model. Thus, our findings suggest that external
shocks, along with regime switches in the volatities of these shocks, have a larger role to play in
South African macroeconomic fluctuations and may influence South Africa’s monetary policy conduct
because these factors influence target variables - such as inflation and the output gap - associated with
monetary policy conduct. Concerning monetary policy conduct and using different Markov switching
DSGE models, we find that the South African Reserve Bank consistently allocates the largest weight
towards inflation stabilization, a lower weight towards output gap stabilization and the lowest weight
towards the exchange rate.
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