IDEAS home Printed from https://ideas.repec.org/a/cbk/journl/v9y2020i3p163-182.html
   My bibliography  Save this article

Testing for the Effectiveness of Inflation Targeting in India: A Factor Augmented Vector Autoregression (FAVAR) Approach

Author

Listed:
  • Jithin P

    (Department of Humanities and Social Sciences, Indian Institute of Technology Madras, Chennai, India)

  • Suresh Babu M

    (Department of Humanities and Social Sciences, Indian Institute of Technology Madras, Chennai, India)

Abstract

Employing Factor Augmented Vector Autoregression (FAVAR) model where factors are obtained using the principal component analysis (PCA) and the parameters of the model are estimated using Vector Autoregression framework, we analyse how changes in monetary policy variables impact inflation, output, money supply, and the financial sector in India. Our results for the period 2001:04 to 2016:03 show that the benchmark FAVAR model showed more reliable results than baseline VAR model. Benchmark FAVAR model shows the existence of weak ‘liquidity puzzle’ in India. The impulse responses from the FAVAR approach reveal that monetary policy is more efficient in explaining the variations in inflation rather than stimulating output indicating its effectiveness in attaining the objective of price stability.

Suggested Citation

  • Jithin P & Suresh Babu M, 2020. "Testing for the Effectiveness of Inflation Targeting in India: A Factor Augmented Vector Autoregression (FAVAR) Approach," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(3), pages 163-182.
  • Handle: RePEc:cbk:journl:v:9:y:2020:i:3:p:163-182
    as

    Download full text from publisher

    File URL: http://www.cbcg.me/repec/cbk/journl/vol9no3-10.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ben S. Bernanke & Mark Gertler, 2001. "Should Central Banks Respond to Movements in Asset Prices?," American Economic Review, American Economic Association, vol. 91(2), pages 253-257, May.
    2. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    3. Abdelkader Aguir, 2018. "Central Bank Credibility, Independence, and Monetary Policy," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 7(3), pages 91-110.
    4. 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.
    5. Belviso Francesco & Milani Fabio, 2006. "Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-46, December.
    6. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    7. Ali Awdeh, 2019. "Monetary Policy and Economic Growth in Lebanon," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 8(2), pages 147-171.
    8. Bhattacharyya, Indranil & Sensarma, Rudra, 2008. "How effective are monetary policy signals in India," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 169-183.
    Full references (including those not matched with items on IDEAS)

    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. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    2. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    3. Geert Bekaert & Seonghoon Cho & Antonio Moreno, 2010. "New Keynesian Macroeconomics and the Term Structure," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 33-62, February.
    4. Belviso Francesco & Milani Fabio, 2006. "Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-46, December.
    5. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
    6. Baumeister, Christiane & Liu, Philip & Mumtaz, Haroon, 2013. "Changes in the effects of monetary policy on disaggregate price dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 543-560.
    7. Eickmeier, Sandra & Hofmann, Boris, 2013. "Monetary Policy, Housing Booms, And Financial (Im)Balances," Macroeconomic Dynamics, Cambridge University Press, vol. 17(4), pages 830-860, June.
    8. Valls Pereira, Pedro L. & da Silva Fonseca, Marcelo Gonçalves, 2012. "Credit Shocks and Monetary Policy in Brazil: A Structural Favar Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 32(2), April.
    9. Mr. Christopher W. Crowe & Mr. S. Mahdi Barakchian, 2010. "Monetary Policy Matters: New Evidence Basedon a New Shock Measure," IMF Working Papers 2010/230, International Monetary Fund.
    10. Moench, Emanuel, 2008. "Forecasting the yield curve in a data-rich environment: A no-arbitrage factor-augmented VAR approach," Journal of Econometrics, Elsevier, vol. 146(1), pages 26-43, September.
    11. Forni, Mario & Giannone, Domenico & Lippi, Marco & Reichlin, Lucrezia, 2009. "Opening The Black Box: Structural Factor Models With Large Cross Sections," Econometric Theory, Cambridge University Press, vol. 25(5), pages 1319-1347, October.
    12. McCallum, Andrew & Smets, Frank, 2007. "Real wages and monetary policy transmission in the euro area," Kiel Working Papers 1360, Kiel Institute for the World Economy (IfW Kiel).
    13. Smets, Frank & Beyer, Robert C. M., 2015. "Labour market adjustments in Europe and the US: How different?," Working Paper Series 1767, European Central Bank.
    14. Liosi, Konstantina, 2023. "The sources of economic uncertainty: Evidence from eurozone markets," Journal of Multinational Financial Management, Elsevier, vol. 69(C).
    15. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554, Edward Elgar Publishing.
    16. Jalali-Naini , Ahmad. R. & Hemati , Maryam, 2012. "The Effect of Monetary Shocks on Disaggregated Prices in a Data Rich Environment: a Bayesian FAVAR Approach," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 6(4), pages 27-60, July.
    17. Juan José Echavarría & Andrés González, 2012. "Choques internacionales reales y financieros y su impacto sobre la economía colombiana," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 30(69), pages 14-66, December.
    18. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    19. Aikman, David & Bush, Oliver & Davis, Alan, 2016. "Monetary versus macroprudential policies causal impacts of interest rates and credit controls in the era of the UK Radcliffe Report," Bank of England working papers 610, Bank of England.
    20. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.

    More about this item

    Keywords

    Factor Augmented VAR; Monetary policy; Economic growth; Inflation;
    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
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

    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:cbk:journl:v:9:y:2020:i:3:p:163-182. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/cbmgvme.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.