(This abstract was borrowed from another version of this item.)"> (This abstract was borrowed from another version of this item.)">
[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/kap/ecopln/v43y2010i3p187-204.html
   My bibliography  Save this article

Has the SARB become more effective post inflation targeting?

Author

Listed:
  • Rangan Gupta
  • Alain Kabundi
  • Mampho Modise
Abstract
This paper assesses the impact of a monetary policy shock on 15 key macroeconomic variables of South Africa, in the pre- and post-inflation targeting periods. For this purpose, we use a Factor-Augmented Vector Autoregressive (FAVAR) model comprising of 107 monthly time series over two equal sub-samples of 1989:01-1997:12 and 2000:01-2008:12. The results, based on impulse response functions, are in line with economic theory and indicate no puzzling effects often observed with small-scale monetary Vector Autoregressive (VAR) models. More importantly, we find that the ability of monetary policy in affecting key macroeconomic variables, including inflation, has increased in the post-targeting period. But, majority of the effects are insignificant, which could, however, also be due to the shorter-lengths of the sub-samples relative to the number of variables used in this study, rather than depicting the inability of monetary policy to significantly affect the South African economy.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Rangan Gupta & Alain Kabundi & Mampho Modise, 2010. "Has the SARB become more effective post inflation targeting?," Economic Change and Restructuring, Springer, vol. 43(3), pages 187-204, August.
    • Handle: RePEc:kap:ecopln:v:43:y:2010:i:3:p:187-204
    DOI: 10.1007/s10644-009-9083-7
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10644-009-9083-7
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10644-009-9083-7?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
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Ben S. Bernanke & Ilian Mihov, 1998. "Measuring Monetary Policy," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(3), pages 869-902.
    3. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    4. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    5. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    6. Rangan Gupta & Alain Kabundi, 2009. "The Effect Of Monetary Policy On House Price Inflation: A Factor Augmented Vector Autoregression (Favar) Approach," Working Papers 200903, University of Pretoria, Department of Economics.
    7. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    8. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    9. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    10. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    11. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    12. Rangan Gupta & Alain Kabundi, 2010. "The effect of monetary policy on house price inflation," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 37(6), pages 616-626, November.
    13. Gupta, Rangan & Jurgilas, Marius & Kabundi, Alain, 2010. "The effect of monetary policy on real house price growth in South Africa: A factor-augmented vector autoregression (FAVAR) approach," Economic Modelling, Elsevier, vol. 27(1), pages 315-323, January.
    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. Kabundi, Alain & Schaling, Eric & Some, Modeste, 2015. "Monetary policy and heterogeneous inflation expectations in South Africa," Economic Modelling, Elsevier, vol. 45(C), pages 109-117.
    2. Giorgio Canarella & Stephen M Miller, 2017. "Inflation Persistence Before and After Inflation Targeting: A Fractional Integration Approach," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(1), pages 78-103, January.
    3. Petrevski, Goran, 2023. "Macroeconomic Effects of Inflation Targeting: A Survey of the Empirical Literature," EconStor Preprints 271122, ZBW - Leibniz Information Centre for Economics.
    4. Eliphas Ndou & Nombulelo Gumata & Mthuli Ncube & Eric Olson, 2013. "Working Paper 189 - An Empirical Investigation of the Taylor Curve in South Africa," Working Paper Series 992, African Development Bank.
    5. Giorgio Canarella & Stephen M. Miller, 2016. "Inflation Persistence and Structural Breaks: The Experience of Inflation Targeting Countries and the US," Working papers 2016-21, University of Connecticut, Department of Economics.
    6. Davide Debortoli & Ricardo Nunes, 2014. "Monetary Regime Switches and Central Bank Preferences," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(8), pages 1591-1626, December.
    7. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2020. "The Effectiveness Of Monetary Policy In South Africa Under Inflation Targeting: Evidence from a Time-Varying Factor-Augmented Vector Autoregressive Model," Journal of Developing Areas, Tennessee State University, College of Business, vol. 54(4), pages 55-73, October-D.
    8. Ruthira Naraidoo & Leroi Raputsoane, 2010. "Zone‐Targeting Monetary Policy Preferences And Financial Market Conditions: A Flexible Non‐Linear Policy Reaction Function Of The Sarb Monetary Policy," South African Journal of Economics, Economic Society of South Africa, vol. 78(4), pages 400-417, December.
    9. Samuel Addo, 2018. "Policy regime changes and central bank prefernces," Working Papers 752, Economic Research Southern Africa.
    10. Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
    11. Alain Kabundi & Elsabé Loots, 2010. "Patterns Of Co‐Movement Between South Africa And Germany: Evidence From The Period 1985 To 2006," South African Journal of Economics, Economic Society of South Africa, vol. 78(4), pages 383-399, December.

    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. Chevallier, Julien, 2011. "Macroeconomics, finance, commodities: Interactions with carbon markets in a data-rich model," Economic Modelling, Elsevier, vol. 28(1), pages 557-567.
    2. Gupta, Rangan & Jurgilas, Marius & Kabundi, Alain, 2010. "The effect of monetary policy on real house price growth in South Africa: A factor-augmented vector autoregression (FAVAR) approach," Economic Modelling, Elsevier, vol. 27(1), pages 315-323, January.
    3. Ansgar Belke & Thomas Osowski, 2019. "International Effects Of Euro Area Versus U.S. Policy Uncertainty: A Favar Approach," Economic Inquiry, Western Economic Association International, vol. 57(1), pages 453-481, January.
    4. Forni, Mario & Gambetti, Luca, 2010. "The dynamic effects of monetary policy: A structural factor model approach," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 203-216, March.
    5. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    6. Rangan Gupta & Marius Jurgilas & Alain Kabundi & Stephen M. Miller, 2009. "Monetary Policy and Housing Sector Dynamics in a Large-Scale Bayesian Vector Autoregressive Model," Working Papers 200913, University of Pretoria, Department of Economics.
    7. Rangan Gupta & Stephen M. Miller & Dylan van Wyk, 2010. "Financial Market Liberalization, Monetary Policy, and Housing Price Dynamics," Working papers 2010-06, University of Connecticut, Department of Economics.
    8. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    9. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
    10. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    11. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.
    12. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
    13. Bork, Lasse, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," Finance Research Group Working Papers F-2009-03, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    14. Matteo Luciani, 2015. "Monetary Policy and the Housing Market: A Structural Factor Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 199-218, March.
    15. Bekiros, Stelios D. & Paccagnini, Alessia, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 298-323.
    16. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    17. Auer, Simone, 2019. "Monetary policy shocks and foreign investment income: Evidence from a large Bayesian VAR," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 142-166.
    18. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    19. Lütkepohl, Helmut, 2014. "Structural vector autoregressive analysis in a data rich environment: A survey," SFB 649 Discussion Papers 2014-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. Gianluca Laganà & Andrew Mountford, 2005. "Measuring Monetary Policy In The Uk: A Factor‐Augmented Vector Autoregression Model Approach," Manchester School, University of Manchester, vol. 73(s1), pages 77-98, September.

    More about this item

    Keywords

    Monetary policy shock; Inflation targeting; Impulse response functions; FAVAR; C32; E52; E58;
    All these keywords.

    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

    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:kap:ecopln:v:43:y:2010:i:3:p:187-204. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.