1. bookVolume 9 (2020): Issue 3 (September 2020)
Journal Details
License
Format
Journal
eISSN
2336-9205
First Published
11 Mar 2014
Publication timeframe
3 times per year
Languages
English
access type Open Access

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

Published Online: 18 Sep 2020
Volume & Issue: Volume 9 (2020) - Issue 3 (September 2020)
Page range: 163 - 182
Received: 19 Mar 2019
Accepted: 27 Jun 2019
Journal Details
License
Format
Journal
eISSN
2336-9205
First Published
11 Mar 2014
Publication timeframe
3 times per year
Languages
English
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.

Keywords

JEL Classification

1. Aguir, A. (2018). Central Bank Credibility, Independence, and Monetary Policy. Journal of Central Banking Theory and Practice, 7(3), 91-110.10.2478/jcbtp-2018-0025Search in Google Scholar

2. Awdeh, A. (2019). Monetary Policy and Economic Growth in Lebanon. Journal of Central Banking Theory and Practice, 8(2), 147-171.10.2478/jcbtp-2019-0018Search in Google Scholar

3. Belviso, F., & Milani, F. (2006). Structural factor-augmented VARs (SFAVARs) and the effects of monetary policy. Topics in Macroeconomics, 6(3).10.2202/1534-5998.1443Search in Google Scholar

4. Bernanke, B. S., & Boivin, J. (2003). Monetary policy in a data-rich environment. Journal of Monetary Economics, 50(3), 525-546.10.1016/S0304-3932(03)00024-2Search in Google Scholar

5. Bernanke, B. S., & Gertler, M. (2001). Should central banks respond to movements in asset prices? American Economic Review, 91(2), 253-257.10.1257/aer.91.2.253Search in Google Scholar

6. Bernanke, B. S., Boivin, J., & Eliasz, P. (2005). Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120(1), 387-422.Search in Google Scholar

7. Bhattacharyya, I., & Sensarma, R. (2005). Signalling instruments of monetary policy: The Indian experience. Journal of Quantitative Economics, 3(2), 180-196.10.1007/BF03404632Search in Google Scholar

8. Bhattacharyya, I., & Sensarma, R. (2008). How effective are monetary policy signals in India? Journal of Policy Modeling, 30(1), 169-183.10.1016/j.jpolmod.2007.07.003Search in Google Scholar

9. Bicchal, M. (2010) Monetary Policy and Inflation in India: A Structural VAR Analysis, 53(3), Available at: http://dx.doi.org/10.2139/ssrn.181388610.2139/ssrn.1813886Search in Google Scholar

10. Boivin, J., & Giannoni, M. P. (2006). Has monetary policy become more effective? Review of Economics and Statistics, 88(3), 445-462.10.1162/rest.88.3.445Search in Google Scholar

11. Figueiredo, F. M. R. (2010). Forecasting Brazilian inflation using a large data set. Central Bank of Brazil Working Paper, 228. Available at: https://www.bcb.gov.br/pec/wps/ingl/wps228.pdfSearch in Google Scholar

12. Gambacorta, L., Hofmann, B., & Peersman, G. (2014). The effectiveness of unconventional monetary policy at the zero lower bound: A cross-country analysis. Journal of Money, Credit and Banking, 46(4), 615-642.10.1111/jmcb.12119Search in Google Scholar

13. Júnior, J. L. R. (2009). Identification of monetary policy shocks and its effects: FAVAR methodology for the Brazilian economy. Brazilian Review of Econometrics, 29(2), 285-313.10.12660/bre.v29n22009.3444Search in Google Scholar

14. Kannan, R., Sanyal, S., & Bhoi, B. B. (2007). Monetary conditions index for India. RBI Occasional Papers, 27, 57-86.Search in Google Scholar

15. Lagana, G., & Mountford, A. (2005). Measuring Monetary Policy in the UK: A Factor-Augmented Vector Autoregression Model Approach. Manchester School Supplement, 73, 77-98.10.1111/j.1467-9957.2005.00462.xSearch in Google Scholar

16. Lahura, E. (2010). The effects of monetary policy shocks in Peru: Semi-structural identification using a factor-augmented vector autoregressive model. Banco Central de Reserva del Perú, Documento de Trabajo, 8, 1-49.Search in Google Scholar

17. Mordi, C. N., Adebiyi, M. A., Adenuga, A. O., Adebayo, O. M., Abeng, M. O., Akpan, I. N., & Evbuomwan, O. O. (2013). Modeling the real sector of the Nigerian economy (pp. 1-59). CBN, Research Department.Search in Google Scholar

18. Munir, K., & Qayyum, A. (2014). Measuring the effects of monetary policy in Pakistan: a factor-augmented vector autoregressive approach. Empirical Economics, 46(3), 843-864.10.1007/s00181-013-0702-9Search in Google Scholar

19. Nachane, D. M., Ray, P., & Ghosh, S. (2002). Does Monetary Policy Have Differential State-Level Effects? An Empirical Evaluation. Economic and Political Weekly, 37(47), 4723-4728.Search in Google Scholar

20. Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. Review of Economic Studies, 72(3), 821-852.10.1111/j.1467-937X.2005.00353.xSearch in Google Scholar

21. Qayyum, Abdul (2006). Money, inflation, and growth in Pakistan. Pakistan Development Review, 45(2), 203-212.10.30541/v45i2pp.203-212Search in Google Scholar

22. Rajan, R. S., & Yanamandra, V. (2015). Effectiveness of Monetary Policy in India: The Interest Rate Pass-Through Channel. In Managing the Macroeconomy (pp. 40-73). Palgrave Macmillan, London.10.1057/9781137534149_2Search in Google Scholar

23. Ramachandran, M., & Kamaiah, B. (1994). Separability of Monetary Assets: Some Evidence from Approximation Analysis for India. Journal of Quantitative Economics, 10, 337-50.Search in Google Scholar

24. Ramachandran, M., & Kumar, R. (2017). Shocks and inflation. RBI DRG series, Available at: https://rbidocs.rbi.org.in/rdocs/Publications/PDFs/DRG30032017B9FD5B290FB94079A6B81A987D02B04A.PDFSearch in Google Scholar

25. Rangarajan, A., Talora, C., Okuyama, R., Nicolas, M., Mammucari, C., Oh, H., ... & Miele, L. (2001). Notch signaling is a direct determinant of keratinocyte growth arrest and entry into differentiation. The EMBO journal, 20(13), 3427-3436.10.1093/emboj/20.13.3427Search in Google Scholar

26. Rangarajan, C. (1997). Role of monetary policy. Economic and Political Weekly, 3325-3328.Search in Google Scholar

27. Reddy, Y. V. (2004). Monetary and financial sector reforms in India: a practitioner’s perspective. In K Basu & C Marks (Eds), India’s emerging economy: Performance and prospects in the 1990s and beyond, 61-82.Search in Google Scholar

28. Ribon, S. (2011). The Effect of Monetary Policy on Inflation: A Factor Augmented VAR Approach using disaggregated data (No. 2011.12). Bank of Israel.Search in Google Scholar

29. Roşoiu, A. (2015). Monetary policy and factor-augmented VAR model. Procedia Economics and Finance, 32, 400-407.10.1016/S2212-5671(15)01410-0Search in Google Scholar

30. Samantaraya, A. (2009). An Index to Assess the Stance of Monetary Policy in India in the Post-Reform Period. Economic and Political Weekly, 44(20), 46-50.Search in Google Scholar

31. Saxegaard, M. (2006). Excess liquidity and the effectiveness of monetary policy: evidence from Sub-Saharan Africa (No. 6-115). International Monetary Fund.10.5089/9781451863758.001Search in Google Scholar

32. Sims, C. A. (1992). Interpreting the macroeconomic time series facts: The effects of monetary policy. European Economic Review, 36(5), 975-1000.10.1016/0014-2921(92)90041-TSearch in Google Scholar

33. Smitha, T. H., & Sankaranarayanan, K. C. (2010). Impact of monetary policy on indian economy in the post-reform period (Doctoral dissertation, Cochin University of Science & Technology).Search in Google Scholar

34. Stock, J. H., & Watson, M. W. (2002). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97(460), 1167-1179.10.1198/016214502388618960Search in Google Scholar

35. Twinoburyo, E. N., & Odhiambo, N. M. (2018). Monetary policy and economic growth: a review of international literature. Journal of Central Banking Theory and Practice, 7(2), 123-137.10.2478/jcbtp-2018-0015Search in Google Scholar

36. Upadhyaya, K. P. (1991). The efficacy of monetary and fiscal policies in developing countries: An application of the St. Louis Equation. Indian Economic Journal, 39(1), 35.Search in Google Scholar

37. Vasudevan, A. (2002). Evolving monetary policy in India: some perspectives. Economic and Political Weekly, 37(11), 1055-1061.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo