IDEAS home Printed from https://ideas.repec.org/a/bla/sajeco/v74y2006i3p391-409.html
   My bibliography  Save this article

A Bvar Model For The South African Economy

Author

Listed:
  • Rangan Gupta
  • Moses M. Sichei

Abstract

The paper develops a Bayesian vector autoregressive (BVAR) model of the South African economy for the period of 1970:1‐2000:4 and forecasts GDP, consumption, investment, short‐term and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVAR model is compared with the same generated from the univariate and unrestricted VAR models. The BVAR model is found to produce the most accurate out of sample forecasts. The same is also capable of correctly predicting the direction of change in the chosen macroeconomic indicators.

Suggested Citation

  • Rangan Gupta & Moses M. Sichei, 2006. "A Bvar Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 74(3), pages 391-409, September.
    • Handle: RePEc:bla:sajeco:v:74:y:2006:i:3:p:391-409
    DOI: 10.1111/j.1813-6982.2006.00077.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1813-6982.2006.00077.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1813-6982.2006.00077.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Pami Dua & Anirvan Banerji & Stephen M. Miller, 2006. "Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 415-437.
    2. Hossain Amirizadeh & Richard M. Todd, 1984. "More growth ahead for Ninth District states," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    3. William C. Gruben & William T. Long, 1988. "The New Mexico economy: outlook for 1989," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Nov, pages 21-36.
    4. Kirsten L. Ludi & Marc Ground, 2006. "Investigating the Bank-Lending Channel in South Africa: A VAR Approach," Working Papers 200604, University of Pretoria, Department of Economics.
    5. Shawn Ni & Dongchu Sun, 2005. "Bayesian Estimates for Vector Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 105-117, January.
    6. William C. Gruben & William T. Long, 1988. "Forecasting the Texas economy: applications and evaluation of a systematic multivariate time series model," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Jan, pages 11-28.
    7. James J. Balazsy & James G. Hoehn, 1985. "The Ohio economy: a time series analysis," Economic Review, Federal Reserve Bank of Cleveland, issue Q III, pages 25-36.
    8. William C. Gruben & Donald W. Hayes, 1991. "Forecasting the Louisiana economy," Economic and Financial Policy Review, Federal Reserve Bank of Dallas, issue Mar, pages 1-16.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    2. Caraiani, Petre, 2010. "Forecasting Romanian GDP Using a BVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-87, December.
    3. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    4. Rangan Gupta & Stephen Miller, 2012. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 339-361, April.
    5. 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.
    6. 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 200912, University of Pretoria, Department of Economics.
    7. Lozano, Francisco-Javier, 2013. "Evaluación de modelos de predicción para la venta de viviendas [Evaluation of forecasting models for house sales]," MPRA Paper 118652, University Library of Munich, Germany.
    8. Usman Shakoor & Mudassar Rashid & Ashfaque Ali Baloch & Muhammad Iftikhar ul Husnain & Abdul Saboor, 2021. "How Aging Population Affects Health Care Expenditures in Pakistan? A Bayesian VAR Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 585-607, January.
    9. Annari De Waal & Rene頖an Eyden & Rangan Gupta, 2015. "Do we need a global VAR model to forecast inflation and output in South Africa?," Applied Economics, Taylor & Francis Journals, vol. 47(25), pages 2649-2670, May.
    10. Patricio Jaramillo, 2009. "Estimación de VAR Bayesianos para la Economía Chilena," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 24(1), pages 101-126, Junio.
    11. Balcilar, Mehmet & Gupta, Rangan & Shah, Zahra B., 2011. "An in-sample and out-of-sample empirical investigation of the nonlinearity in house prices of South Africa," Economic Modelling, Elsevier, vol. 28(3), pages 891-899, May.
    12. 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.
    13. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, March.
    14. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
    15. 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.
    16. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rangan Gupta, 2006. "FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 611-628, December.
    2. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, March.
    3. 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.
    4. Prem P. Talwar & Edward J. Chambers, 1993. "Forecasting Provincial Business Indicator Variables and Forecast Evaluation," Urban Studies, Urban Studies Journal Limited, vol. 30(10), pages 1763-1773, December.
    5. Rangan Gupta & Josine Uwilingiye, 2008. "Measuring The Welfare Cost Of Inflation In South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(1), pages 16-25, March.
    6. Starck, Christian, 1991. "Specifying a Bayesian vector autoregression for short-run macroeconomic forecasting with an application to Finland," Research Discussion Papers 4/1991, Bank of Finland.
    7. Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
    8. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    9. Amusa, Kafayat & Gupta, Rangan & Karolia, Shaakira & Simo-Kengne, Beatrice D., 2013. "The long-run impact of inflation in South Africa," Journal of Policy Modeling, Elsevier, vol. 35(5), pages 798-812.
    10. Kociecki, Andrzej, 2012. "Orbital Priors for Time-Series Models," MPRA Paper 42804, University Library of Munich, Germany.
    11. Milan Eliskovski, 2018. "Investigating credit transmission mechanism in the Republic of Macedonia: evidence from Vector Error Correction Model," Working Papers 2018-02, National Bank of the Republic of North Macedonia.
    12. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    13. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    14. Aßmann, Christian & Boysen-Hogrefe, Jens & Pape, Markus, 2014. "Bayesian analysis of dynamic factor models: An ex-post approach towards the rotation problem," Kiel Working Papers 1902, Kiel Institute for the World Economy (IfW Kiel).
    15. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    16. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    17. Yu Hsing & Wen-jen Hsieh, 2014. "Test of the Bank Lending Channel for a BRICS Country," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 4(8), pages 1016-1023, August.
    18. Jeff B. Cromwell & Michael J. Hannan, 1993. "The Utility of Impulse Response Functions in Regional Analysis: Some Critical Issues," International Regional Science Review, , vol. 15(2), pages 199-222, August.
    19. repec:rre:publsh:v:40:y:2010:i:2:p:181-96 is not listed on IDEAS
    20. Matousek, Roman & Solomon, Helen, 2018. "Bank lending channel and monetary policy in Nigeria," Research in International Business and Finance, Elsevier, vol. 45(C), pages 467-474.
    21. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pinter, Gabor, 2016. "VAR models with non-Gaussian shocks," LSE Research Online Documents on Economics 86238, London School of Economics and Political Science, LSE Library.

    More about this item

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:sajeco:v:74:y:2006:i:3:p:391-409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.