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

Balcılar, Mehmet; Gupta, Rangan; Kotzé, Kevin

doi:10.1007/s00181-016-1157-6

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…The utility function consists of dividends flow and two rigidities (stickiness of bond position and price stickiness in the Rotemberg form -Lombardo and Ves-tin, 2008). Firms are working in a market with monopolistic competition; therefore, they have a demand restriction (7). The budget restriction (8) and production function (9) is common.…”

Section: Intermediate Goods-producing Firmsmentioning

confidence: 99%

Forecasting using a Nonlinear DSGE Model

Ivashchenko

Gupta

2018

Journal of Central Banking Theory and Practice

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A medium-scale nonlinear dynamic stochastic general equilibrium (DSGE) model was estimated (54 variables, 29 state variables, 7 observed variables). The model includes an observed variable for stock market returns. The root-mean square error (RMSE) of the in-sample and out-of-sample forecasts was calculated. The nonlinear DSGE model with measurement errors outperforms AR (1), VAR (1) and the linearised DSGE in terms of the quality of the out-of-sample forecasts. The nonlinear DSGE model without measurement errors is of a quality equal to that of the linearised DSGE model.

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Section: Intermediate Goods-producing Firmsmentioning

confidence: 99%

Forecasting using a Nonlinear DSGE Model

Ivashchenko

Gupta

2018

Journal of Central Banking Theory and Practice

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“…Furthermore they found that the difference in forecasting performance is usually statistically significant, when compared to a random-walk and linear NKDSGE model (particularly over longer horizons). However, when considering the use of regime-switching nonlinearities, Balcilar et al (2017) note that the out-of-sample forecasts for South African inflation that are generated by different forms of Markov-switching NKDSGE models are largely inferior to the single regime counterpart.…”

Section: Review Of Inflation Forecasting In South Africamentioning

confidence: 99%

Big data forecasting of South African inflation

Botha¹,

Burger

Kotzé

et al. 2022

Empir Econ

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We investigate whether the use of statistical learning techniques and big data can enhance the accuracy of inflation forecasts. We make use of a large dataset for the disaggregated prices of consumption goods and services, which we partially reconstruct, and a large suite of different statistical learning and traditional time-series models. The results suggest that the statistical learning models are able to compete with most benchmarks over medium to longer horizons, despite the fact that we only have a relatively small sample of available data. This may imply that the ability of statistical learning models to explain nonlinear relationships, or as an alternative, restrict the set of predictors to relevant information, is of importance. These characteristics of the statistical learning models may be particularly useful during periods of crisis, when deviations from the steady state are more persistent. We find that the accuracy of the central bank's near-term inflation forecasts compares favourably with those of The authors would like to thank Patrick Kelly and Marietjie Bennett from Statistics South Africa for their assistance with the data. The insightful comments of the anonymous referees have also lead to a number of improvements, for which the authors are grateful. The remaining errors are those of the authors.

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“…4. Balcilar et al (2017) apply this methodology to South Africa, which is a relatively developed emerging market economy that is affected by shocks that are of a much smaller magnitude, and of a lower frequency. 5.…”

Section: Notesmentioning

confidence: 99%

Monetary policy, financial frictions and structural changes in Uganda: a Markov-switching DSGE approach

Anguyo

Gupta

Kotzé

2020

Economic Research-Ekonomska Istraživanja

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This paper considers the use of regime-switching dynamic stochastic general equilibrium models for monetary policy analysis and forecasting purposes. The objective is to determine whether or not the inclusion of these regime-switching features provide a more accurate description of the economy in a particular low income country. All of the models incorporate financial frictions that are introduced through the activities of heterogeneous agents in the household and several other features that are incorporated in most small open-economy models. Two variants of regime-switching models are considered: one includes switching in the monetary policy rule (only) and the other employs switching in both the monetary policy rule and the volatility of the shocks. The models are applied to the quarterly macroeconomic data for Uganda and most of the parameters are estimated with the aid of Bayesian techniques. The results of the extensive inand out-of-sample evaluation suggest that the model parameters do not remain constant over the two regimes. In addition, the transition probabilities suggest that there are three distinct periods where the central bank response has been more aggressive. These periods relate to a change in policy framework and significant shocks that have affected the Ugandan economy. It is also noted that the forecasting performance of the regime-switching models are possibly superior to the model that excludes these features over certain horizons.

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

Cited by 7 publications

References 40 publications

Forecasting using a Nonlinear DSGE Model

Forecasting using a Nonlinear DSGE Model

Big data forecasting of South African inflation

Monetary policy, financial frictions and structural changes in Uganda: a Markov-switching DSGE approach

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