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Forecasting Macroeconomic Variables Using Large Datasets: Dynamic Factor Model versus Large-Scale BVARs


  • Rangan Gupta

    () (Department of Economics, University of Pretoria)

  • Alain Kabundi

    () (Department of Economics and Econometrics, University of Johannesburg)


This paper uses two-types of large-scale models, namely the Dynamic Factor Model (DFM) and Bayesian Vector Autoregressive (BVAR) Models based on alternative hyperparameters specifying the prior, which accommodates 267 macroeconomic time series, to forecast key macroeconomic variables of a small open economy. Using South Africa as a case study and per capita growth rate, inflation rate, and the short-term nominal interest rate as our variables of interest, we estimate the two-types of models over the period 1980Q1 to 2006Q4, and forecast one- to four-quarters-ahead over the 24-quarters out-of-sample horizon of 2001Q1 to 2006Q4. The forecast performances of the two large-scale models are compared with each other, and also with an unrestricted three-variable Vector Autoregressive (VAR) and BVAR models, with identical hyperparameter values as the large-scale BVARs. The results, based on the average Root Mean Squared Errors (RMSEs), indicate that the large-scale models are better-suited for forecasting the three macroeconomic variables of our choice, and amongst the two types of large-scale models, the DFM holds the edge.

Suggested Citation

  • Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables Using Large Datasets: Dynamic Factor Model versus Large-Scale BVARs," Working Papers 200816, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:200816

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    References listed on IDEAS

    1. Michael R. Moore & Ariel Dinar, 1995. "Water and Land as Quantity-Rationed Inputs in California Agriculture: Empirical Tests and Water Policy Implications," Land Economics, University of Wisconsin Press, vol. 71(4), pages 445-461.
    2. Grimble, R. J., 1999. "Economic instruments for improving water use efficiency: theory and practice," Agricultural Water Management, Elsevier, vol. 40(1), pages 77-82, March.
    3. Huffman, Wallace E., 1988. "An Econometric Methodology for Multiple-Output Agricultural Technology: An Application of Endogenous Switching Models," Staff General Research Papers Archive 11003, Iowa State University, Department of Economics.
    4. Michael R. Moore, 1999. "Estimating Irrigators' Ability to Pay for Reclamation Water," Land Economics, University of Wisconsin Press, vol. 75(4), pages 562-578.
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    Cited by:

    1. Mirriam Chitalu Chama-Chiliba & Rangan Gupta & Nonophile Nkambule & Naomi Tlotlego, 2011. "Forecasting Key Macroeconomic Variables of the South African Economy Using Bayesian Variable Selection," Working Papers 201132, University of Pretoria, Department of Economics.
    2. Rangan Gupta & Monique Reid, 2013. "Macroeconomic surprises and stock returns in South Africa," Studies in Economics and Finance, Emerald Group Publishing, vol. 30(3), pages 266-282, July.
    3. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    4. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    5. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States," Working papers 2009-13, University of Connecticut, Department of Economics.
    6. Rangan Gupta & Alain Kabundi, 2010. "Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.

    More about this item


    Dynamic Factor Model; BVAR; Forecast Accuracy;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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