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We are grateful to Paul Cashin, Nigel Chalk, Roberto Garcia-Saltos, Chetan Ghate, Laura Papi, Rafael Portillo, Thomas Richardson, and our colleagues in the Asia and Pacific Department for helpful comments and discussions. We benefited from the feedback received from seminar participants at the Reserve Bank of India (November, 2013), at the NIPFP-DEA Annual Research Program (February, 2014), and at the Indian Statistical Institute, New Delhi (April, 2014).
The Urjit Patel Committee Report (RBI, 2014) also reflects on the role of these factors arguing for the need to pay attention to food and fuel inflation: “High inflation in food and energy items is generally reflected in elevated inflation expectations. With a lag, this gets manifested in the inflation of other items, particularly services. Shocks to food inflation and fuel inflation also have a much larger and more persistent impact on inflation expectations than shocks to non-food non-fuel inflation.”
Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA).
The estimates reported correspond to CPI-IW inflation. Conclusions remain the same if WPI or CPI-combined (spliced using CPI-IW) inflation is used instead.
We use the U.S. output gap as the proxy for the output gap of the rest of the world.
Core CPI are compiled by staff by stripping out food and energy items from the CPI basket.
An increase in the real exchange rate (z) corresponds here to a real depreciation.
For a detailed description of the Bayesian estimation technique see Schorfheide (2000) and Geweke (1999). Details on the software for estimation can be found in Juillard (2004).
This is also consistent with the cross-country evidence that finds the co-efficient on expected inflation to be below 0.5 (Berg et al., 2006a).
See RBI (2014, page 15) “The CPI-Combined based headline inflation measure appears to be the most feasible and appropriate measure of inflation—as the closest proxy of a true cost of living index—for the conduct of monetary policy.”