Forecasting Performance of an Estimated Dsge Model for the South African Economy

Alpanda, Sami; Kotzé, Kevin; Woglom, Geoffrey

doi:10.1111/j.1813-6982.2011.01260.x

Cited by 22 publications

(17 citation statements)

References 72 publications

(116 reference statements)

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“…Gupta and Kabundi (2010), when using a small-open economy DSGE model to forecast the nominal effective exchange rate depreciation, shows the ability of such a model to outperform not only classical and Bayesian VARs based on a small set of predictors, but also large-scale factor models and Bayesian VARs relying on 266 macroeconomic time-series. Similar results were obtained by Alpanda et al, (2011) based on a relatively more sophisticated small-open economy DSGE model. The authors found that the DSGE model consistently outperformed the random-walk model in forecasting, the depreciation of the nominal rand-dollar exchange rate, and also performed better, in general, than the smallscale VAR and Bayesian VARs, based on a set ten fundamental variables.…”

Section: Introductionsupporting

confidence: 83%

Can We Beat the Random-Walk Model for the South African Rand–U.S. Dollar and South African Rand–UK Pound Exchange Rates? Evidence from Dynamic Model Averaging

Bruyn

Gupta

Eyden

2015

Emerging Markets Finance and Trade

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Traditionally, the literature on forecasting exchange rates with many potential predictors have primarily only accounted for parameter uncertainty using Bayesian Model Averaging (BMA). Though BMA-based models of exchange rates tend to outperform the random walk model, we show that when accounting for model uncertainty over and above parameter uncertainty through the use of Dynamic model Averaging (DMA), the gains relative to the random walk model are even bigger. That is, DMA models outperform not only the random walk model, but also the BMA model of exchange rates. We obtain these results based on fifteen potential predictors used to forecast two South African Rand-based exchange rates. In the process, we also unveil variables, which tends to vary over time, that are good predictors of the Rand-Dollar and Rand-Pound exchange rates at different forecasting horizons. JEL Classification codes: C11, C53, F37, F47

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Section: Introductionsupporting

confidence: 83%

Can We Beat the Random-Walk Model for the South African Rand–U.S. Dollar and South African Rand–UK Pound Exchange Rates? Evidence from Dynamic Model Averaging

Bruyn

Gupta

Eyden

2015

Emerging Markets Finance and Trade

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“…When considering these results we note that the smoothing coefficient, ρ, in the two models differ slightly. In the model that does not include any switching we have a coefficient of 0.82, which is similar to the value that was obtained in Alpanda et al (2011). In the Makov-switching model the posterior estimate for the interest rate smoothing coefficients are ρ(κ = 1) = 0.87 and ρ(κ = 2) = 0.90, which allows for greater smoothing in the interest rate.…”

Section: In-sample Statisticssupporting

confidence: 59%

“…Thereafter, we are able to initialize the Markov Chain Monte Carlo (MCMC) procedure that is used to construct the full posterior distribution and marginal data density. Details of the prior parameter values that are used in the calculation of the posterior estimates are similar to those that were used in Alpanda et al (2011) and are provided along with all the posterior estimates in Table 2.…”

Section: Solution and Estimationmentioning

confidence: 94%

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Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model

2016

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The aim of this paper is to investigate structural changes in the South African economy using an estimated small open-economy dynamic stochastic general equilibrium (DSGE) model. The structure of the model follows recent work in this area and incorporates the expectations of agents and a number of shocks that are assumed to affect the economy at various points in time. In addition, the dynamic linkages between the respective variables in the model may be explained in terms of the microfoundations that characterise the behaviour of firms, households and the central bank. After estimating the model, we allow for the parameters in a number of different structural equations to change periodically over time. Different versions of the model are assessed using various statistical criteria to identify the model that is able to explain the changing dynamics in the South African economy. The results suggest that the central bank has responded in a consistent manner over the sample period; however, there are periods of time where it does not focus too greatly on output pressure. This impacts on some of the impulse response functions where we note that a monetary policy shock has a slightly larger effect on inflation, while the risk-premium shock has a larger effect on output, inflation and interest rates.JEL Classifications: E32, E52, F41.

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“…Recent studies, namely, Liu and Gupta (2007), Liu et al (2009Liu et al ( , 2010, Gupta and Kabundi (2010, forthcoming) and Alpanda et al (2011), have initiated a growing interest in forecasting macroeconomic variables in South Africa using Dynamic Stochastic General Equilibrium (DSGE) models.…”

Section: Table Of Contents 1 Introductionmentioning

confidence: 99%

A DSGE-VAR model for forecasting key South African macroeconomic variables

Gupta

Steinbach

2013

Economic Modelling

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The paper develops a Small Open Economy New Keynesian DSGE-VAR (SOENKDSGE-VAR) model of the South African economy, characterised by incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. The model is estimated using Bayesian techniques on data from the period 1980Q1 to 2003Q2, and then used to forecast output, inflation and nominal short-term interest rate for one-to eight-quarters-ahead over an out-of sample horizon of 2003Q3 to 2010Q4. When the forecast performance of the SOENKDSGE-VAR model is compared with an independently estimated DSGE model, the classical VAR and six alternative BVAR models, we find that, barring the BVAR model based on the SSVS prior on both VAR coefficients and the error covariance, the SOENKDSGE-VAR model is found to perform competitively, if not, better than all the other VAR models.JEL Classification: C11, C53, E37

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Forecasting Performance of an Estimated Dsge Model for the South African Economy

Cited by 22 publications

References 72 publications

Can We Beat the Random-Walk Model for the South African Rand–U.S. Dollar and South African Rand–UK Pound Exchange Rates? Evidence from Dynamic Model Averaging

Can We Beat the Random-Walk Model for the South African Rand–U.S. Dollar and South African Rand–UK Pound Exchange Rates? Evidence from Dynamic Model Averaging

Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model

A DSGE-VAR model for forecasting key South African macroeconomic variables

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