This paper estimates with Bayesian method a monetary model of the South African economy, which encompasses both open-economy features and forward-looking behavior of private agents and of the monetary authority. Such models are essential tools of monetary policy under an inflation-targeting regime, in order to meet the ongoing challenge of keeping the inflation expectations anchored in the fact of external shocks.
South Africa announced an inflation-targeting regime in 2000, which was implemented in 2002, with a target range in the low-moderate zone (generally 3-6 percent). Such regime has served South Africa well. Following the sharp depreciation at the end of 2001, inflation peaked at 11.3 percent in October 2002.1 The subsequent appreciation of the rand and monetary tightening led to a steady decline of inflation and inflation remained within the official target range of 3-6 percent for several years. At the same time, continuous improvements in the South African Reserve Bank’s (SARB) inflation-targeting framework strengthened its credibility and inflation expectations became much better anchored. More recently, however, rising global food and energy prices, together with a thriving economy, have contributed to higher inflation in 2007 and 2008.
Indeed, recent international experience with inflation targeting, as discussed, for example, in Roger and Stone (2005) and Mishkin and Schmidt-Hebbel (2007), provides some support for the view that inflation targeting is associated with an improvement in overall economic performance. Inflation targeting tends to help countries achieve lower inflation in the long run, experience smaller inflation response to oil-price and exchange-rate shocks, strengthen monetary policy independence, improve monetary policy efficiency, and obtain inflation outcomes closer to desired levels.
However, several authors, including Calvo (2001) and Mishkin (2004) have pointed to the specific difficulties that emerging market economies may face in conducting inflation targeting. First, credibility issues may weaken the design of optimal macroeconomic policy in these countries, and may reduce the effectiveness of monetary policy. Second, weak institutions may lead to currency substitution or liability dollarization, or even fiscal dominance, largely reducing the capability of the monetary authorities to effectively target inflation. Third, large exchange rate and other external shocks complicate the conduct of monetary policy, by introducing substantial volatility.
South Africa is not particularly affected by the first two types of issues. Macroeconomic polices have been impressive and currency substitution or liability dollarization are virtually absent. In particular, the sharp depreciation of the rand in 2001–02 has proven that there is certainly no “fear of floating.” However, like many other emerging market countries, South Africa implementation of the inflation-targeting strategy is often challenged by large exogenous—often external—shocks, as discussed above. In the typical environment in which many emerging markets operate—small open economies well integrated within a globalized world—an essential tool for policymaking lies in a coherent forward-looking framework for assessing the effect of external and domestic shocks on inflation, and for gauging the appropriate policy response.
To this purpose, this paper estimates with Bayesian methods a small dynamic macroeconomic model for the South African economy. The estimated model can help assess—within an inflation-targeting framework—the impact on inflation dynamics of the main domestic and external factors, such as those arising from exchange rates, domestic prices, and domestic as well as external demand. It is also able to display important empirical features of the monetary transmission mechanism in South Africa, and helps evaluate the policy response to shocks that affect inflation.
The model incorporates the central features of inflation targeting, including forward-looking behavior of private agents and of the monetary authority. As such, it embodies the basic principle that the fundamental role for monetary policy is to provide an anchor for inflation and inflation expectations. At the same time, it offers a consistent framework for understanding and interpreting inflation developments and for evaluating the central inflation forecast.2 Indeed, in an inflation-targeting framework, a sound inflation forecast is key to successful monetary policy.
Fitting the parameters to the South African economy with conventional, classical estimation methods is a big challenge, given economic and political developments in South Africa over the past decades, involving several structural breaks. The preapartheid sample would not be particularly informative about today’s monetary transmission mechanism; the economy was characterized by prolonged periods of negative real interest rates as well as significant trade and capital restrictions. Currently, South Africa enjoys a much broader integration with world trade and capital markets, a flexible exchange rate regime, and a monetary transmission mechanism where the repurchase rate of the central bank has a key role.3
Therefore, the model is estimated employing Bayesian methods over the postapartheid period. Bayesian method present an advantage if the sample is short, to the extent the researcher brings to the exercise priors that are informative (for example, obtained from the experience of other countries, whose analysis maybe benefited from longer time series). Moreover, Bayesian methods do not need to rely on assumptions about distributions of estimators and test statistics over hypothetical repeated samples. They are less sensitive to econometric issues, such as unit root and cointegration, which heavily alter the frequentist approach.4
This paper is in line with a growing literature estimating with Bayesian method’s small macroeconomic models for specific countries, see, for example, Honjo (2007) and Iakova (2007) for the United Kingdom and Honjo and Hunt (2006) for Iceland. For recent applications of Bayesian estimation methods to much more comprehensive dynamic stochastic general equilibrium models, see, for example, Smets and Wouters (2003) for the euro area; Smets and Wouters (2005) and Iakova and others (2006) for the United States; Elekdag, Justiniano, and Tchakarov (2005) for Korea; Lubik and Schorfheide (2007) for Australia, Canada, New Zealand, and the United Kingdom; and Justiniano and Preston (2008) for Canada. For an application of Bayesian methods to estimate a macroeconomic model for South Africa, see Ortiz and Sturzenegger (2007).
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Thomas Harjes is a senior economist in the IMF European Department and Luca Antonio Ricci is a deputy division chief in the IMF Research Department. The authors started this project when they were desk economists for South Africa. The authors are highly indebted to Andy Berg, Philippe Karam, and Douglas Laxton for sharing their programs and also thank Peter Gakunu, Manuela Goretti, Nikolay Gueorguiev, Alejandro Justiniano, Ondrej Kamenik, Daniel Leigh, Papa N’Diaye, Sean Nolan, Frank Schorfheide, Theo Van Rensburg, Werner Schule, and participants in the presentation at the South Africa Reserve Bank and in the IMF Small Modeling Group seminars for very helpful discussions and comments. The Bayesian estimation is programmed in Dynare, a software kindly provided by Michel Juillard and his team.
It is unclear, however, if and to what extent the sharp depreciation of the rand at the end of 2001 was a fully exogenous event or may have been in part due to the monetary policy stance in 2001.
Woodford (2003) presents comprehensive theoretical foundations for models encompassing these features.
It would be an interesting exercise to explore change in regime, but this is beyond the scope of the paper and is left for future work.
The consumer price index employed in the paper is the measure targeted by the SARB, which excludes interest payments on mortgage loans (CPIX).
As the model is tailored to represent short-run dynamics and the monetary transmission mechanism, there is no explicit formalization of the supply side of the economy. Hence, the dynamics of the output gap mainly reflect movements in the demand side of the economy.
Some normalization is required: the interest rate term needs to be divided by 400, because the interest rates and the risk premium are measured in percent at annual rates, whereas changes in the logarithms of the exchange rate are quarterly.
This strictly holds if there is no interest rate smoothing.
A broad discussion of the methodology and related issues is offered by Geweke (1999), Schorfheide (2000), and An and Schorfeide (2007). For general references on Bayesian estimation, see Koop (2003) and Lancaster (2004). The estimation is implemented using Dynare (see http://www.cepremap.cnrs.fr/dynare/).
In models with fully specified preferences and technology, such as the model of Smets and Wouters (2003), a model-based output and real interest rate gap can be derived. Smets and Wouters argue that for monetary policy purposes, the appropriate estimate of potential output and the natural interest rate should only take into account the parts of the natural level of output and the interest rate that are driven by shocks arising from preferences and technologies. They derive a model-based output and real interest rate gap but show that there is considerable uncertainty around it.
In addition to the 12 parameters of the model, we also estimate the steady state real interest rate and the autoregressive terms for the five observable variables that are exogenous to the theoretical model (see the Appendix).
Smal and de Jager (2001) find a transmission lag of a monetary policy shock to inflation in South Africa of about 6–8 quarters.
In an empirical study for Australia, Canada, New Zealand, and the United Kingdom, Kearns and Manners (2005) find that an unanticipated tightening of 25 basis points immediately appreciates the nominal exchange rate by 0.2–0.4 percent.
With Dornbusch-style overshooting (δ = 1), the effects of exchange rate shocks on inflation and output would be even less persistent.
Woodford (2003) shows also how it would decrease with the degree of strategic complementarity of pricing decisions among producers, as more firms would tend to mimic price stickiness behavior.
The sacrifice ratio is about one in this model.