Monetary Policy Regime Changes and South Africa’s ... · Monetary Policy Regime Changes and South...

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

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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.

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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

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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-

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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

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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


∆NEER

Mean -2.51 -2.07 -1.27


∆Repo-rate

Mean 0.12 -0.05 -0.07


∆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

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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.

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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))

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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.

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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.

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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.

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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.

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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

0 6 12 18 24 30 36-0.4

-0.2

0

0.2Real consumption PCE

volatility Only

policy Only

0 6 12 18 24 30 36-4

-2

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0.005

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volatility Only

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0 6 12 18 24 30 36-0.01

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1.5

2Net gold exports

0 6 12 18 24 30 360

0.02

0.04

0.06Output gap

0 6 12 18 24 30 36-1

-0.5

0

0.5


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

21

0 6 12 18 24 30 36-1

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volatility Onlypolicy Only

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volatility Onlypolicy Only

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2

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0 6 12 18 24 30 36-0.4

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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

22

0 6 12 18 24 30 36-1

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volatility Only

policy Only

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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





Technolgy shock

Risk premia 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





Technolgy shock

Risk premia 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





Technolgy shock

Risk premia 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





Technolgy shock

Risk premia 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





Technolgy shock

Risk premia 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





Technolgy shock

Risk premia 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





Technolgy shock

Risk premia 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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