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The Role of Asset Prices in Forecasting Inflation and Output in South Africa

Author

Listed:
  • Rangan Gupta

    (Rangan Gupta, Professor, University of Pretoria, Department of Economics, Pretoria 0002, South Africa. E-mail: Rangan.Gupta@up.ac.za)

  • Faaiqa Hartley

    (Faaiqa Hartley, Graduate Student, Department of Economics, University of Pretoria, Pretoria 0002, South Africa. E-mail: faaiqasalie@gmail.com)

Abstract

This article assesses the predictive ability of asset prices relative to other variables in forecasting inflation and real GDP growth in South Africa. A total of 42 asset and non-asset predictor variables are considered. Forecasts of inflation and real GDP growth are computed using both individual predictor autoregressive distributed lag (ARDL) models, forecast combination approaches, as well as large scale models. The large scale data models considered include Bayesian vector autoregressive models and classical and Bayesian univariate and multivariate factor augmented vector autoregressive models. The models are estimated for an in-sample of 1980:Q2 to 1999:Q4, and then one- to eight-step-ahead forecasts for inflation and real GDP growth are evaluated over the 2000:Q1 to 2010:Q2 out-of-sample period. Principle Component forecast combination models are found to produce the most accurate out-of-sample forecasts of inflation and real GDP growth relative to the other combination and more sophisticated models considered. Asset prices are found to contain particularly useful information for forecasting inflation and real GDP growth at certain horizons. Asset prices are however found to be stronger predictors of inflation, particularly in the long run. JEL Classification: C11, C32, R53

Suggested Citation

  • Rangan Gupta & Faaiqa Hartley, 2013. "The Role of Asset Prices in Forecasting Inflation and Output in South Africa," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 12(3), pages 239-291, December.
  • Handle: RePEc:sae:emffin:v:12:y:2013:i:3:p:239-291
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    Cited by:

    1. Omokolade Akinsomi & Mehmet Balcilar & Rıza Demirer & Rangan Gupta, 2017. "The effect of gold market speculation on REIT returns in South Africa: a behavioral perspective," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 774-793, October.
    2. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    3. 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.
    4. repec:ipg:wpaper:2014-585 is not listed on IDEAS
    5. repec:ipg:wpaper:2014-473 is not listed on IDEAS
    6. 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.
    7. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    8. Gupta, Rangan & Modise, Mampho P., 2013. "Macroeconomic Variables and South African Stock Return Predictability," Economic Modelling, Elsevier, vol. 30(C), pages 612-622.
    9. Tsangyao Chang & Tsung-Pao Wu & Rangan Gupta, 2015. "Are house prices in South Africa really nonstationary? Evidence from SPSM-based panel KSS test with a Fourier function," Applied Economics, Taylor & Francis Journals, vol. 47(1), pages 32-53, January.
    10. Aye, Goodness C. & Balcilar, Mehmet & Bosch, Adél & Gupta, Rangan, 2014. "Housing and the business cycle in South Africa," Journal of Policy Modeling, Elsevier, vol. 36(3), pages 471-491.
    11. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Charl Jooste & Stephen M. Miller & Zeynel Abidin Ozdemir, 2012. "Fiscal Policy Shocks and the Dynamics of Asset Prices: The South African Experience," Working Papers 1211, University of Nevada, Las Vegas , Department of Economics.
    12. Gupta, Rangan & Kanda, Patrick T., 2015. "Does the Price of Oil Help Predict Inflation in South Africa? Historical Evidence Using a Frequency Domain Approach. - Il prezzo del petrolio predice l’inflazione in Sud Africa? Evidenza storica attra," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 68(4), pages 451-467.
    13. Furkan Emirmahmutoglu & Mehmet Balcilar & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2014. "Causal relationship between asset prices and output in the US: Evidence from state-level panel Granger causality test," Working Papers 201411, University of Pretoria, Department of Economics.
    14. Rangan Gupta, 2012. "Forecasting House Prices for the Four Census Regions and the Aggregate US Economy: The Role of a Data-Rich Environment," Working Papers 201214, University of Pretoria, Department of Economics.
    15. Rangan Gupta & Mampho P. Modise & Josine Uwilingiye, 2016. "Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(8), pages 1935-1955, August.
    16. Rangan Gupta, 2013. "Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment," Applied Economics, Taylor & Francis Journals, vol. 45(33), pages 4677-4697, November.
    17. Rangan Gupta & Hylton Hollander & Mark E. Wohar, 2016. "The Impact of Oil Shocks in a Small Open Economy New-Keynesian Dynamic Stochastic General Equilibrium Model for South Africa," Working Papers 201652, University of Pretoria, Department of Economics.

    More about this item

    Keywords

    Asset prices; combination forecasts; BVAR; FAVAR;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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