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Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation

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  • Aron, Janine
  • Muellbauer, John

Abstract

Forecasting inflation is particularly challenging in emerging markets, where trade and monetary policy regimes have shifted and the exchange rate and food prices are highly volatile. This study shows that the information in long-run co-integrated relationships for relative prices in South Africa is helpful in outperforming univariate benchmark models for forecasting inflation. It also investigates gains to the inflation forecast accuracy as a result of aggregating weighted forecasts of the sub-component price indices, versus forecasting the aggregate consumer price index itself. Rich multivariate equilibrium correction models employ general and sectoral information for ten sub-components, including structural breaks and institutional changes. Model selection over the period 1979–2003 generates pseudo out-of-sample forecasts, four quarters ahead, until 2007. The largest gain in forecast accuracy against naïve benchmark models comes from incorporating equilibrium correction into the long-run. For more sophisticated models, aggregating the weighted forecasts of the sub-components outperforms the aggregate forecasts. The analysis also contributes to an improved understanding of sectoral inflationary pressures.

Suggested Citation

  • Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
  • Handle: RePEc:eee:intfor:v:28:y:2012:i:2:p:456-476
    DOI: 10.1016/j.ijforecast.2011.05.004
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    Cited by:

    1. Gabriel Pino & J. D. Tena & Antoni Espasa, 2016. "Geographical disaggregation of sectoral inflation. Econometric modelling of the Euro area and Spanish economies," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 799-815, February.
    2. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    3. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    4. Espasa, Antoni & Tena Horrillo, Juan de Dios & Pino, Gabriel, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Rangan Gupta & Patrick T. Kanda & Mampho P. Modise & Alessia Paccagnini, 2015. "DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa," Applied Economics, Taylor & Francis Journals, vol. 47(3), pages 207-221, January.
    6. repec:gam:jecnmx:v:5:y:2017:i:4:p:44-:d:114224 is not listed on IDEAS
    7. Duncan, Roberto & Martinez-Garcia, Enrique, 2018. "New Perspectives on Forecasting Inflation in Emerging Market Economies: An Empirical Assessment," Globalization and Monetary Policy Institute Working Paper 338, Federal Reserve Bank of Dallas.

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