ISSN (Print) - 0012-9976 | ISSN (Online) - 2349-8846

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A Horse Race among the Alternative Taylor Rule Specifications

Some Insights from India

The paper estimates a slew of augmented Taylor rule specifications using call and treasury bill rates. After accounting for break points, we calculate the output gap based on a single-index dynamic factor extracted from monthly high-frequency indicators that are representative of broad sectoral activity. In our study, we found that interest rates in India are mostly in line with the augmented Taylor rule specifications after the Reserve Bank of India started using flexible inflation targeting.

The views and opinions expressed in this paper are those of the authors and do not necessarily represent those of the Reserve Bank of India. The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.

Over the past few decades, the Taylor rule has been considered one of the guiding forces of the central banks’ policy rate decisions, though it is well known that central banks do not necessarily follow it as a hard rule and take interest rate decisions by integrating judgment and discretion. Empirical research and discussions surrounding the policy reaction function have taken centre stage following the seminal paper by John B Taylor (1993). Thereafter, several dynamic stochastic general equilibrium (DSGE) models have used the Taylor rule to provide an effective closure for the indeterminacy problem. Galí and Gertler’s paper (1999) shows that the dynamic stability condition converges with the Taylor principle, providing micro-foundations to this macroeconomic rule. Central banks take policy decisions based on several considerations, for example, high-frequency data releases, nowcasts of major macro variables, and judgments. However, the Taylor rule is regularly estimated in many central banks to compare actual policy rates vis-à-vis the rule-prescribed rates with due caution against its mechanical use. Accordingly, researchers have augmented the standard Taylor rule to capture the policy rate dynamics of different countries and time periods. Thus, different variants of the Taylor rule have subsequently evolved: the forward-looking Taylor rule, backward-looking Taylor rule (Clarida et al 1999), Taylor rule with error correction (Judd and Rudebusch 1998), Taylor rule augmented with output gap dynamics (Walsh 2003), etc.

Among the emerging market economies, India has a long history of monetary policymaking where policy and operating frameworks have evolved with the economy. India’s monetary policy framework has transitioned from a credit-planning regime to monetary targeting, a multiple indicator approach, preconditions for inflation targeting, and finally to a flexible inflation targeting (FIT) framework. However, call rates have played the role of a key money market rate for a long time. So, the immediate questions that arise are (i) which class of Taylor rule best fits the monetary policy rate dynamics? (ii) how has the relationship between the policy interest rate, inflation gap, and output changed in different monetary policy regimes? and (iii) whether there has been a change in this over the past few years? This paper involves estimating a slew of augmented Taylor rule specifications for India that look at the relationship between the policy interest rate and output gap and inflation gap over a period of time.

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Updated On : 4th Jun, 2022
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