Startseite Asymmetries in the monetary policy reaction function: evidence from India
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Asymmetries in the monetary policy reaction function: evidence from India

  • Irfan Ahmad Shah EMAIL logo und Srikanta Kundu
Veröffentlicht/Copyright: 14. Juli 2021
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Studies in Nonlinear Dynamics & Econometrics
Aus der Zeitschrift Studies in Nonlinear Dynamics & Econometrics Band 26 Heft 4

Abstract

This paper analyzes the reaction function of monetary authority in India from 1997Q1 to 2019Q4 using nonlinear Taylor rule. It has been found that monetary policy reaction function (MPRF) in India is asymmetric and is influenced by the state of the economy, determined by the lagged interest rate. To capture such asymmetry, we have used a set of nonlinear models including smooth transition regression (STR) model, threshold regression (TR) model and Markov-switching regression (MSR) model along with the instrumental variable estimation technique. The analysis discloses that the behaviour of the Reserve Bank of India (RBI) is asymmetric, reacts aggressively to output gap in general and particularly during periods of high interest rate. Furthermore, the RBI reacts more to inflation and output gap during low volatile regimes in MSR models compared to high volatile regimes. We also found that there is a high degree of inertia in the policy rates of the RBI. The study concludes that nonlinear models may not only help in understanding the behaviour of the RBI but also prevent from making incorrect and misleading conclusions in Indian context.

Keywords: asymmetric preferences; monetary policy; regime-switching; Taylor rule
Corresponding author: Irfan Ahmad Shah, Centre For Development Studies, Thiruvananthapuram, Kerala, India, E-mail:

Acknowledgements

The authors are grateful to Manmohan L. Agarwal, M. Parameswaran, Gert Peersman, Chetan Ghate and students at CDS and Ghent University for helpful comments and discussions. We have benefited from the feedback at various conferences including IGIDR Mumbai, ISI Delhi and IIM Bangalore.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: No funding was received for this paper.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

Appendix A

The two instrumenting equation for IV-PML estimation for MSR model are as:

(12) y t = c 0 s t + c 1 s t y t − 1 + c 2 s t r t + c 3 s t e t + c 4 s t M 3 t + c 5 s t s t o c k s t + ε 0
(13) π t = d 0 s t + d 1 s t y t + d 2 s t e t + d 3 s t π t − 1 + ε 0

where Eq. (12) is the aggregate demand (IS) equation. Aggregate supply is represented by Eq. (13). Further, y t is output gap, r t is the real interet rate, e t is exchange rate, M3t is broad money supply growth rate, stocks represent stock market returns, π t is the inflation rate. These variables are already defined in the Data section. Moreover, we follow Patra and Kapur (2012) for considering Eqs. (12) and (13) as instrumenting equations.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/snde-2019-0121).

Received: 2019-10-02
Revised: 2021-06-29
Accepted: 2021-07-02
Published Online: 2021-07-14

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. What does Google say about credit developments in Brazil?
  4. Forecasting transaction counts with integer-valued GARCH models
  5. Asymmetries in the monetary policy reaction function: evidence from India
  6. A mixture autoregressive model based on Gaussian and Student’s t-distributions
  7. Time-specific average estimation of dynamic panel regressions
  8. Rescaled variance tests for seasonal stationarity
https://doi.org/10.1515/snde-2019-0121
Schlagwörter für diesen Artikel
asymmetric preferences; monetary policy; regime-switching; Taylor rule

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. What does Google say about credit developments in Brazil?
  4. Forecasting transaction counts with integer-valued GARCH models
  5. Asymmetries in the monetary policy reaction function: evidence from India
  6. A mixture autoregressive model based on Gaussian and Student’s t-distributions
  7. Time-specific average estimation of dynamic panel regressions
  8. Rescaled variance tests for seasonal stationarity
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