## 1.   Inflation in India

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---

### Introduction: purpose and approach
- Objective: estimate the second-round effects of food price inflation and investigate their importance for monetary policy formulation in India.
- Approach:
  - Document why second-round effects may matter for emerging market monetary policy.
  - Empirically investigate the importance of second-round effects in India.
  - Develop and estimate a dynamic small open-economy New Keynesian model (gap form) tailored to India that captures pass-through from headline to core inflation.

### Recent inflation dynamics: salient facts
- High and persistent inflation has coincided with a growth slowdown in India.
- Key drivers:
  - Food inflation feeding quickly into wages and core inflation.
  - Entrenched inflation expectations.
  - Cost-push shocks from binding sector-specific supply constraints (agriculture, energy, transportation).
  - Pass-through from a weaker rupee.
  - Ongoing energy price increases.
- Role of food inflation:
  - CPI-Combined weight on food, beverages and tobacco is 49.7 percent.
  - Share of food expenditure in total household expenditure:
    - India: 45.1 percent (data based on 2011/12 household survey, NSSO's 68th round).
    - Emerging Markets average: 41.6 percent.
    - Advanced Economies average: 10.1 percent.
    - Rural India: 48.3 percent (average share); Urban India: 37.3 percent (average share).
  - Households’ inflation expectations:
    - Fraction of households expecting headline CPI inflation to move with food inflation has averaged about 90 percent in recent years.
    - In December 2012, more than 95 percent of respondents were influenced by expected changes in food prices when forming general price expectations.
- Wage developments:
  - Agricultural and non-agricultural wages on sustained growth trajectories since the mid-2000s.
  - Real wage growth has been significantly positive.
  - MGNREGA likely buttressed inflation pressures by setting a floor on many rural wages and linking wage growth indexation to retail inflation.

### Why monetary policy should attend to food inflation in India
- In many emerging economies, food price shocks are more volatile and persistent than in advanced economies and propagate strongly into nonfood inflation.
- In economies with high food shares in consumption and less firmly anchored expectations, excluding food and fuel from policy considerations may be inappropriate.
- In credit-constrained consumer environments, a narrow focus on nonfood inflation can be suboptimal.
- Conclusion: ignoring food inflation risks policy mistakes because food shocks can have persistent and economy-wide second-round effects.

### Empirical assessment of food and fuel pass-through
- Empirical questions (monthly data 1996–2013 unless noted):
  1. Does headline inflation revert to core inflation?
  2. Does core inflation revert to headline inflation?
- Regression framework:
  - Headline and core year-over-year CPI inflation series used.
  - Sample for reported regressions: 1997-2013.
- Selected regression results (Table 2 highlights; robust standard errors in parentheses):
  - Coefficient estimate: -0.31 (0.27)
  - Coefficient estimate: -0.68 *** (0.21)
  - Constant terms: 0.136 and -0.11 (0.59) (0.49)
  - Sample: 1997-2013
- Interpretation:
  - Null that β = 0 (headline reverts to core) cannot be rejected → headline inflation does not revert to core inflation.
  - Hypotheses that β = 1 and that β = 1 with α = 0 (headline fully reverts to core within a year) are rejected.
  - Estimate σ = -0.68 (highly statistically significant) indicates core inflation reverts to headline inflation.
  - Null hypothesis σ = 0 (core does not revert to headline) is rejected.
  - Hypotheses that σ = 1 and that σ = 1 with α = 0 cannot be rejected: core inflation catches up with headline inflation and reverts to headline quickly.
  - Overall: large second-round effects are present; shocks to headline (food/fuel) feed into core inflation.

### Model: structure and pass-through mechanism
- Model type: small open-economy New Keynesian macroeconomic model with rational expectations; gap formulation; micro-founded forward-looking IS and Phillips dynamics; adapted to India.
- Key equations/features:
  - Aggregate demand (IS): real activity related to expected/past real activity, real interest rate, real exchange rate, foreign output gap.
  - Price-setting (Phillips) for core CPI: relates to past and expected inflation, output gap, exchange rate, and a pass-through term from headline to core inflation.
  - Food (non-core) inflation equation: related to past and expected inflation, output gap, exchange rate (no headline→core pass-through term).
  - Uncovered interest parity condition for the exchange rate with some backward-looking expectations.
  - Policy interest rate rule: policy rate as function of output gap and expected inflation (open-economy Taylor-type).
- Headline inflation aggregation:
  - π_t = (1 − ϑ) π^c_t + ϑ π^{ff}_t, where ϑ is the weight on non‑core inflation.
- Pass-through modeling:
  - Core inflation equation includes a lagged (four-quarter) headline-to-core component (notation in text: (π^c_{t+4} − π^c_{t−4}) type term) capturing pass-through from headline to core.
- Open-economy links:
  - Real exchange rate z captures imported goods’ price effects; an increase in z corresponds to a real depreciation.
  - Foreign sector treated as a relatively large, exogenous trading partner (United States used as proxy).

### Estimation: parameters, calibration, and priors
- Parameterization:
  - Values chosen based on country-modeling experience and India-specific characteristics; priors for the United States taken from Berg, Karam, and Laxton (2006b).
- Steady-state choices and technical assumptions:
  - Rest of the world equilibrium real interest rate set at 1.5 percent.
  - Long-term headline CPI inflation rate for India set at 6.5 percent.
  - Implied equilibrium nominal short-term interest rate: about 8 percent.
  - De-trended real exchange rate series removes average real appreciation of about 1 percent per annum.
  - Technical assumption: zero equilibrium risk premium.
  - All gaps measuring deviations from long-run equilibrium are by definition zero.
- Bayesian estimation methodology:
  - Estimation implemented with the Bayesian estimation module in DYNARE (Juillard, 2001).
  - Posterior constructed from prior density p(θ) and likelihood f(X|θ) (Kalman filter; Laplace approximation to find posterior mode; Metropolis-Hastings).
  - Metropolis-Hastings implementation: generate 100000 draws in 4 chains, discard first 50000 draws.
- Data used:
  - Sample: India quarterly data from 1996Q1 to 2013 Q4.
  - India's nominal interest rate proxy: Three-month Treasury bill rate.
  - Real exchange rate proxy: real exchange rate (CPI-based).
  - India inflation measure: backcasted CPI-Combined based on CPI-IW.
  - Rest-of-world variables: United States GDP, inflation, and interest rate.
  - Seasonal adjustment: X12 filter.
- Priors:
  - Priors guided by theory and empirical evidence; relatively diffuse where evidence is lacking.
  - Structural parameters: gamma distributions or beta distributions when restricted to [0,1].
  - Standard errors of shock processes: inverted gamma distribution.

### Estimation results — Output gap equation
- Parameter prior means and posterior means:
  - ldβ (lead of output gap): prior mean 0.40; posterior mean 0.19.
  - lagβ (lag of output gap): prior mean 0.60; posterior mean 0.59.
  - RRgapβ (real interest rate gap): prior mean 0.05; posterior mean 0.04.
  - Zgapβ (exchange rate gap): prior mean 0.05; posterior mean 0.04.
  - RWygapβ (foreign output gap): prior mean 0.15; posterior mean 0.10.
- Interpretations:
  - Estimated lagβ of 0.6 comparable to other emerging markets.
  - ldβ = 0.19 indicates expectations about future output gap matter.
  - RRgapβ = 0.04 implies a one percentage point increase in real interest rates leads to a 0.04 percent fall in the output gap the following period.
  - RWygapβ = 0.1 implies a 1 percentage point increase in the foreign output gap leads to a contemporaneous 0.1 percentage point increase in the Indian output gap.

### Estimation results — Phillips curves (core and headline)
- Core inflation equation parameters (prior mean → posterior mean):
  - ldcπ,α (lead of core inflation): 0.20 → 0.26.
  - ygapc,α (output gap on core inflation): 0.25 → 0.25.
  - zc,α (exchange rate pass-through to core): 0.30 → 0.15.
  - πα,c (pass-through from headline to core): 0.30 → 0.32.
- Headline (food & fuel) equation parameters (prior mean → posterior mean):
  - ldffπ,α: 0.20 → 0.22.
  - ygapff,α: 0.25 → 0.28.
  - zff,α: 0.30 → 0.33.
- Interpretations:
  - Core inflation is backward looking; backward-looking component in core inflation is 0.8 (implying inertia and persistence).
  - ygapc,α = 0.25 consistent with literature estimates of 0.2–0.3.
  - Exchange rate pass-through to core inflation estimated at 0.15.
  - πα,c = 0.32 implies that if headline inflation exceeds core inflation by 1 percentage point, it will lead to a 0.3 percentage points increase in core inflation in the next quarter (strong second-round effects).

### Estimated uncovered interest parity (real exchange rate) and Taylor-rule
- Real exchange rate expectations parameter zδ:
  - Prior mean 0.60; posterior mean 0.49.
  - Interpretation: δ = 0.49 (reported as 0.5 in discussion) makes monetary policy potentially more effective, though incomplete exchange rate pass-through in India reduces efficacy.
- Open-economy Taylor-rule parameters (prior mean → posterior mean):
  - RSlagγ (interest rate smoothing): 0.80 → 0.82.
  - πγ (inflation responsiveness): 1.90 → 1.88.
  - ygapγ (output gap responsiveness): 0.60 → 0.64.
- Interpretations:
  - High degree of interest rate smoothing in India (coefficient ≈ 0.8).
  - πγ ≈ 1.9 indicates strong responsiveness to inflation.
  - ygapγ = 0.64 suggests RBI places weight on stabilizing real activity alongside inflation.

### Shock scenarios and policy implications
- Monetary policy shock (temporary 100 basis points increase in nominal interest rate):
  - Peak widening of output gap by almost 1 percent in about 4 quarters.
  - Core CPI inflation slows by about ¾ of a percentage point.
  - Headline CPI inflation declines by almost 1 percentage point.
  - Nearly 2 percent peak real appreciation.
- Demand shock:
  - Positive demand shock raises output and the output gap, gradually putting upward pressure on prices.
  - Containing inflation requires tighter monetary policy, which lowers demand and leads to real appreciation.
  - Inflation peaks after 4–6 quarters depending on shock persistence.
- Food and fuel price shock (1.5 percentage points jump in food and fuel price inflation — about 6 percent annualized inflation rate):
  - Output gap widens at peak by about 0.5 percentage points.
  - Headline and core inflation rise by about 0.5 percent.
  - Interest rate increases by about 80 basis points on impact.
  - Headline inflation shock passes through to core inflation, raising it by about 0.2 percentage points.
- Overall: inflation shocks lead to widening output gap, higher inflation and interest rates, and real appreciation.

### Model fit and historical monetary stance
- Since mid 2008 actual interest rates are consistently below model-predicted rates, suggesting negative monetary policy shocks.
- The gap between actual and predicted rates was large in 2008–2009.
- The gap reopened in late 2010 and averaged about 100 basis points during 2011–12.
- IMF staff argued for a tighter monetary stance to counter inflationary pressures during that period.

### Key takeaways and policy recommendations
- Empirical evidence indicates:
  - Headline inflation shocks (food and fuel) do not quickly revert to core; either shocks are persistent or second-round effects are large.
  - Core inflation tends to revert to headline inflation, indicating strong second-round dynamics.
- Policy implications emphasized:
  - Ignoring food inflation when formulating monetary policy in India risks policy mistakes; persistent supply shocks to food and fuel require monetary policy action to mitigate second-round effects and anchor inflation expectations.
  - Monetary policy needs to respond decisively to tackle high and persistent inflation.
  - Headline CPI inflation should be the nominal anchor for monetary policy; its persistent and entrenched nature should guide monetary policy stance.
  - Given food inflation persistently high for five years, monetary policy needs to remain tight to control generalized inflation.
  - The RBI may need to raise rates to tackle inflation durably if faced with a persistent and sizable supply-side food price shock that pressures broad-based inflation.
  - Because inflation is mostly backward looking, monetary policy must maintain a tight stance for a prolonged period.
  - Recent revisions to the RBI’s liquidity management framework should improve monetary transmission, reducing required policy interest rate adjustments to contain inflationary pressures.
  - Given a relatively flat Phillips curve, structural reforms to raise potential growth are critical to reduce the burden on monetary policy.

*Source: IMF staff analysis as presented in the chapter "1.   Inflation in India" (excerpts from the provided document, _wp14178).*

### 1.   Inflation   in   India   ..........................................................................................

### 1.   Inflation in India

### Introduction: purpose and approach
- Objective: estimate the second-round effects of food price inflation and investigate their importance for monetary policy formulation in India.
- Approach:
  - Document why second-round effects may matter for emerging market monetary policy.
  - Empirically investigate the importance of second-round effects in India.
  - Develop and estimate a dynamic small open-economy New Keynesian model (gap form) tailored to India that captures pass-through from headline to core inflation.

### Recent inflation dynamics: salient facts
- High and persistent inflation has coincided with a growth slowdown in India.
- Key drivers identified:
  - Food inflation feeding quickly into wages and core inflation.
  - Entrenched inflation expectations.
  - Cost-push shocks from binding sector-specific supply constraints (agriculture, energy, transportation).
  - Pass-through from a weaker rupee.
  - Ongoing energy price increases.
- Role of food inflation:
  - CPI-Combined weight on food, beverages and tobacco is 49.7 percent.
  - The share of food expenditure in total household expenditure:
    - India: 45.1 percent (data based on 2011/12 household survey, NSSO's 68th round).
    - Emerging Markets average: 41.6 percent.
    - Advanced Economies average: 10.1 percent.
    - Note: rural India average share of spending on food estimated at 48.3 percent; urban India 37.3 percent.
  - Households’ inflation expectations:
    - The fraction of households expecting headline CPI inflation to move with food inflation has averaged about 90 percent in recent years.
    - In December 2012, more than 95 percent of respondents were influenced by expected changes in food prices when forming general price expectations.
- Wage developments:
  - Both agricultural and non-agricultural wages have been on sustained growth trajectories since the mid-2000s.
  - Real wage growth has been significantly positive.
  - MGNREGA has likely buttressed inflation pressures by setting a floor on many rural wages and linking wage growth indexation to retail inflation.

### Why monetary policy should attend to food inflation in India
- In many emerging economies, food price shocks are more volatile and persistent than in advanced economies and propagate strongly into nonfood inflation.
- In economies with high food shares in consumption and less firmly anchored expectations, excluding food and fuel from policy considerations may be inappropriate.
- In credit-constrained consumer environments, a narrow focus on nonfood inflation can be suboptimal.
- Conclusion: ignoring food inflation risks policy mistakes because food shocks can have persistent and economy-wide second-round effects.

### Empirical assessment of food and fuel pass-through
- Empirical questions (monthly data 1996–2013 unless noted):
  1. Does headline inflation revert to core inflation?
  2. Does core inflation revert to headline inflation?
- Regression framework:
  - Headline and core year-over-year CPI inflation series used.
  - Sample noted for specific regressions: Sample: 1997-2013 (reported table).
- Selected regression results (Table 2: Regression analysis of non-core inflation pass-through; robust standard errors in parentheses; ***,**, * indicate 1, 5, 10 percent significance):
  - Dependent variable(s) and coefficients reported:
    - Coefficient estimate: -0.31 (0.27)
    - Coefficient estimate: -0.68 *** (0.21)
    - Constant terms: 0.136 and -0.11 (0.59) (0.49)
    - Sample: 1997-2013
- Interpretation of results:
  - The null hypothesis that coefficient β = 0 (headline reverts to core) cannot be rejected → headline inflation does not revert to core inflation.
  - Hypotheses that β = 1 and that β = 1 with α = 0 (headline fully reverts to core within a year) are rejected.
  - The estimate of σ (reported as -0.68, highly statistically significant) indicates core inflation reverts to headline inflation.
  - The null hypothesis σ = 0 (core does not revert to headline) is rejected.
  - Hypotheses that σ = 1 and that σ = 1 with α = 0 cannot be rejected: core inflation catches up with headline inflation and reverts to headline quickly.
  - Overall conclusion: large second-round effects are present; shocks to headline (food/fuel) feed into core inflation.

### Model: structure and modifications to capture pass-through
- Model type: small open-economy New Keynesian macroeconomic model with rational expectations, gap formulation, and micro-founded forward-looking IS and Phillips dynamics, adapted to India.
- Key features:
  - Baseline behavioral equations:
    1. Aggregate demand (IS) curve: real activity related to expected/past real activity, real interest rate, real exchange rate, foreign output gap.
    2. Price-setting (Phillips) equation: core CPI inflation related to past and expected inflation, output gap, exchange rate, and a pass-through term from headline to core inflation.
    3. Food (non-core) inflation equation: food inflation related to past and expected inflation, output gap, exchange rate.
    4. Uncovered interest parity condition for the exchange rate with some backward-looking expectations.
    5. Policy interest rate rule: policy rate as function of output gap and expected inflation.
  - Headline inflation aggregation:
    - π_t = (1 − ϑ) π^c_t + ϑ π^{ff}_t, where ϑ is the weight on non‑core inflation; π^c is core inflation; π^{ff} is food and fuel inflation.
  - Pass-through modeling:
    - Core inflation equation includes a term capturing pass-through from headline to core: a lagged (four-quarter) headline-to-core component appears in the core equation (notation in text: (π^c_{t+4} − π^c_{t−4}) type term).
  - Open-economy links:
    - Real exchange rate z captures imported goods’ price effects; an increase in z corresponds to a real depreciation.
    - Foreign sector modeled as a relatively large, exogenous trading partner (United States used as proxy).
- Specific equation highlights (as presented in source text):
  - Output gap dynamics: ygap_t equation includes RRgap (real interest rate gap), zgap (real exchange rate gap), RWygap (rest-of-world output gap), lag terms, and shock ε^{ygap}_t.
  - Headline inflation aggregation: (ff/ c notation) π^{ff}_t and π^c_t combined with weight ϑ.
  - Core inflation equation includes a pass-through term )44(1,1_{t−}^c ππ (representing the extent of pass-through from non-core to core).
  - Food and fuel inflation equation has analogous structure but without the pass-through-from-headline term.
  - Real exchange rate dynamics: z_t equation includes domestic and foreign real interest rates, equilibrium risk premium ρ*, and parameters δ.
  - Monetary policy (Taylor-type) rule: nominal rate R^S_t responds to expected inflation, output gap, real rate gap, equilibrium real rate RR*, and inflation objective π*.

### Estimation: parameters and calibration choices
- Parameterization:
  - Parameter values chosen based on country-modeling experience and India-specific characteristics; priors for the United States taken from Berg, Karam, and Laxton (2006b).
- Steady-state choices:
  - Rest of the world equilibrium real interest rate set at 1.5 percent.
  - Rest of the world inflation rate set at (text truncated in source — value not provided in supplied excerpt).

### Key takeaways and policy implications
- Empirical evidence indicates:
  - Headline inflation shocks (food and fuel) do not quickly revert to core; either shocks are persistent or second-round effects are large.
  - Core inflation tends to revert to headline inflation, indicating strong second-round dynamics.
- Policy implication emphasized in the text:
  - Ignoring food inflation when formulating monetary policy in India risks policy mistakes; persistent supply shocks to food and fuel require monetary policy action to mitigate second-round effects and anchor inflation expectations.
- Additional institutional/contextual factors:
  - High weight of food in consumption and CPI, strong linkage of food prices to wage setting, and the role of programs such as MGNREGA in supporting rural wage floors reinforce the transmission from food inflation to broader inflation dynamics.

*Source: IMF staff analysis as presented in the chapter "1.   Inflation in India" (excerpts from the provided document).*

### 2.4 percent. We set India’s long-term headline CPI inflation rate at 6.5 percent and the

### _wp14178 - 2.4 percent. We set India’s long-term headline CPI inflation rate at 6.5 percent and the

### Assumptions and calibration
- Long-term headline CPI inflation rate set at 6.5 percent.  
- Equilibrium real interest rate set at 1.5 percent.  
- Implied equilibrium nominal short-term interest rate: about 8 percent.  
- De-trended real exchange rate series removes average real appreciation of about 1 percent per annum.  
- Technical assumption: zero equilibrium risk premium.  
- All gaps measuring deviations of actual variables from their long-run equilibrium are by definition zero.

### Bayesian estimation methodology
- Model estimated using Bayesian techniques with priors informed by previous empirical studies.  
- Estimation implemented with the Bayesian estimation module in DYNARE (Juillard, 2001).  
- Advantages cited:
  - Formalizes use of prior empirical/theoretical knowledge.
  - Stabilizes highly nonlinear optimization, important with short sample periods (India quarterly National Accounts data start in 1996Q1).
  - Allows for measurement errors in the data; excess volatility can be allocated to measurement error and excluded from stochastic simulations.
  - Provides framework for parameterizing and evaluating possibly mis-specified macro models and for model comparison via marginal likelihood or posterior model probability.
- Posterior construction:
  - Posterior density proportional to product of prior density p(θ) and likelihood f(X|θ).
  - Likelihood calculated from state-space representation using the Kalman filter.
  - Laplace approximation used to find posterior mode as a starting value for Metropolis-Hastings.
  - Metropolis-Hastings implementation: generate 100000 draws in 4 chains, discard first 50000 draws.

### Data used
- Sample: India quarterly data from 1996Q1 to 2013 Q4.  
- India's nominal interest rate proxy: Three-month Treasury bill rate.  
- Real exchange rate proxy: real exchange rate (CPI-based).  
- India inflation measure: backcasted CPI-Combined based on CPI-IW.  
- Rest-of-world variables: United States GDP, inflation, and interest rate.  
- Seasonal adjustment: X12 filter.

### Prior distributions
- Priors guided by theory and empirical evidence; relatively diffuse priors chosen where empirical evidence is lacking.  
- Structural parameters: gamma distributions or beta distributions when parameters are restricted to [0,1].  
- Standard errors of shock processes: inverted gamma distribution.

### Estimation results — Output gap equation
- Estimated equation structure includes leads and lags of output gap, real interest rate gap, foreign output gap, and exchange rate gap.  
- Parameter prior means and posterior means:
  - ldβ (lead of output gap): prior mean 0.40; posterior mean 0.19.  
  - lagβ (lag of output gap): prior mean 0.60; posterior mean 0.59.  
  - RRgapβ (real interest rate gap): prior mean 0.05; posterior mean 0.04.  
  - Zgapβ (exchange rate gap): prior mean 0.05; posterior mean 0.04.  
  - RWygapβ (foreign output gap): prior mean 0.15; posterior mean 0.10.  
- Interpretations:
  - Estimated lagβ of 0.6 comparable to other emerging markets.
  - Lead coefficient ldβ = 0.2 indicates expectations about future output gap matter.
  - RRgapβ = 0.04 implies a one percentage point increase in real interest rates leads to a 0.04 percent fall in the output gap the following period.
  - RWygapβ = 0.1 implies a 1 percentage point increase in the foreign output gap leads to a contemporaneous 0.1 percentage point increase in the Indian output gap.

### Estimation results — Phillips curve (core and headline CPI)
- Core inflation equation parameters (prior mean → posterior mean):
  - ldcπ,α (lead of core inflation): 0.20 → 0.26.  
  - ygapc,α (output gap on core inflation): 0.25 → 0.25.  
  - zc,α (exchange rate pass-through to core): 0.30 → 0.15.  
  - πα,c (pass-through from headline to core): 0.30 → 0.32.  
- Headline/core second equation (food & fuel / headline components) parameters (prior mean → posterior mean):
  - ldffπ,α: 0.20 → 0.22.  
  - ygapff,α: 0.25 → 0.28.  
  - zff,α: 0.30 → 0.33.  
- Interpretations:
  - Core inflation is backward looking; backward-looking component in core inflation is 0.8 (implying inertia and persistence in inflation).  
  - ygapc,α = 0.25 consistent with literature estimates of 0.2–0.3.  
  - Exchange rate pass-through to core inflation estimated at 0.15.  
  - πα,c = 0.32 implies that if headline inflation exceeds core inflation by 1 percentage point, it will lead to a 0.3 percentage points increase in core inflation in the next quarter (i.e., strong second-round effects).

### Estimated uncovered interest parity (real exchange rate) equation
- Equation structure includes real interest rate differentials and foreign variables.
- Parameter zδ (forward vs backward-looking real exchange rate expectations):
  - Prior mean 0.60; posterior mean 0.49.  
- Interpretation:
  - Estimated δ = 0.49 (reported as 0.5 in discussion) makes monetary policy potentially more effective, though incomplete exchange rate pass-through in India reduces efficacy.

### Estimated open-economy Taylor-rule (monetary policy rule)
- Parameter prior means and posterior means:
  - RSlagγ (interest rate smoothing): 0.80 → 0.82.  
  - πγ (inflation responsiveness): 1.90 → 1.88.  
  - ygapγ (output gap responsiveness): 0.60 → 0.64.  
- Interpretations:
  - High degree of interest rate smoothing in India (coefficient ≈ 0.8).
  - πγ ≈ 1.9 indicates strong responsiveness to inflation.
  - ygapγ = 0.64 suggests RBI places weight on stabilizing real activity alongside inflation.

### Shock scenarios and policy implications — Monetary policy shock
- Typical transmission: temporary increase in nominal interest rate → reduces domestic demand and appreciates the rupee → output contracts (due to lower demand and reduced competitiveness) → inflation falls.  
- Quantified impulse response to a 100 basis points temporary increase in interest rate:
  - Peak widening of output gap by almost 1 percent in about 4 quarters.  
  - Core CPI inflation slows by about ¾ of a percentage point.  
  - Headline CPI inflation declines by almost 1 percentage point.  
  - Nearly 2 percent peak real appreciation.

### Shock scenarios — Demand shock
- Positive demand shock raises output and the output gap, gradually puts upward pressure on prices.  
- Containing inflation requires tighter monetary policy, which lowers demand and leads to real appreciation.  
- Inflation peaks after 4–6 quarters depending on shock persistence.

### Shock scenarios — Food and fuel price shock
- Interpreted as international oil/food price shocks or domestic supply shocks (e.g., rainfall).  
- Response to a 1.5 percentage points jump in food and fuel price inflation (about 6 percent annualized inflation rate):
  - Output gap widens at peak by about 0.5 percentage points.  
  - Headline and core inflation rise by about 0.5 percent.  
  - Interest rate increases by about 80 basis points on impact.  
  - Headline inflation shock passes through to core inflation, raising it by about 0.2 percentage points.  
- Overall: inflation shocks lead to widening output gap, higher inflation and interest rates, and real appreciation.

### Model fit and historical monetary stance
- Model-predicted vs actual nominal interest rates:
  - Since mid 2008 actual interest rates are consistently below model-predicted rates, suggesting negative monetary policy shocks.  
  - Gap between actual and predicted rates was large in 2008–2009.  
  - Gap reopened in late 2010 and averaged about 100 basis points during 2011–12.
- IMF staff reports argued for a tighter monetary stance to counter inflationary pressures during that period.

### Conclusions and policy recommendations
- India experienced a prolonged period of high inflation largely driven by persistently high food inflation, complicating the RBI’s task.  
- Central banks should look through transitory supply shocks but should react to second-round effects.  
- Emerging market characteristics (high food share in household expenditure, less firmly anchored expectations, persistent supply shocks) imply second-round effects are important for India.  
- Recommendations and implications:
  - Monetary policy needs to respond decisively to tackle high and persistent inflation.
  - Headline CPI inflation should be the nominal anchor for monetary policy; its persistent and entrenched nature should guide monetary policy stance.
  - Given food inflation persistently high for five years, monetary policy needs to remain tight to control generalized inflation.
  - The RBI may need to raise rates to tackle inflation durably if faced with a persistent and sizable supply-side food price shock that pressures broad-based inflation.
  - Because inflation is mostly backward looking, monetary policy must maintain a tight stance for a prolonged period.
  - Recent revisions to the RBI’s liquidity management framework should improve monetary transmission, reducing required policy interest rate adjustments to contain inflationary pressures.
  - Given a relatively flat Phillips curve, structural reforms to raise potential growth are critical to reduce the burden on monetary policy.

*Source: IMF staff analysis in the provided content unit.*

### REFERENCES

### REFERENCES

### Core methodological and modeling sources
- Bauwens, Luc, Lubrano, Michel, and Richard, Jean-Francois, 2000, “Bayesian Inference in Dynamic Econometric Models,” OUP Catalogue, Oxford University Press.
- Geweke, John, 1998, “Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Communication,” Staff Report 249, Federal Reserve Bank of Minneapolis.
- Juillard, Michel, 2001, “DYNARE: A Program for the Simulation of Rational Expectation Models,” Computing in Economics and Finance 2001 213, Society for Computational Economics.
- Rubio-Ramirez, Juan F, and Jesus Fernández-Villaverde, 2005, “Estimating Dynamic Equilibrium Economies: Linear Versus Nonlinear Likelihood,” Journal of Applied Econometrics, Vol. 20(7), pp 891–910.
- Schorfheide, Frank, 2000, “Loss Function-Based Evaluation of DSGE Models,” Journal of Applied Econometrics, Vol. 15(6), pp 645–670.
- Calvo, Guillermo A., 1983. “Staggered Prices in a Utility-Maximizing Framework,” Journal of Monetary Economics, Elsevier, vol. 12(3), pp 383-398.

### Bayesian, simulation, and estimation references
- Berg, Andrew, Philippe D. Karam, and Douglas Laxton, 2006a, “A Practical Model-Based Approach to Monetary Policy Analysis, Overview,” IMF Working Paper 06/080 (Washington: International Monetary Fund).
- Berg, Andrew, Philippe D. Karam, and Douglas Laxton, 2006b, “Practical Model-Based Monetary Policy Analysis -A How-to Guide,” IMF Working Paper 06/081 (Washington: International Monetary Fund).
- Geweke, John, 1998, “Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Communication,” Staff Report 249, Federal Reserve Bank of Minneapolis.
- Bauwens, Luc, Lubrano, Michel, and Richard, Jean-Francois, 2000, “Bayesian Inference in Dynamic Econometric Models,” OUP Catalogue, Oxford University Press.

### Inflation, commodity prices, and pass-through
- Catão, Luis, and Chang, Roberto, 2010, “World Food Prices and Monetary Policy,’ IMF Working Paper 10/161 (Washington: International Monetary Fund).
- Cecchetti, Stephen C. and Richhild Moessner, 2008, “Commodity Prices and Inflation Dynamics,” BIS Working Paper.
- Choudhri, Ehsan and Dalia Hakura, 2001, “Exchange Rate Pass-through to Domestic Prices: Does the Inflationary Environment Matter?” IMF Working Paper 01/194  (Washington: International Monetary Fund).
- Clark, Todd E., 2001, “Comparing Measures of Core Inflation”, Federal Reserve Bank of Kansas City Economic Review, Vol. 86, No 2, pp 5–31.
- Walsh, James P, 2011, “Reconsidering the Role of Food Prices in Inflation,” IMF Working Paper 11/71 (Washington: International Monetary Fund).
- Mohanty, Deepak, 2014, “Why is Recent Food Inflation in India so Persistent?” Annual Lalit Doshi Memorial Lecture delivered at the St. Xavier’s College, Mumbai, on 13th January 2014.
- Mohanty, Deepak, 2012, “Price Stability and Financial Stability An Emerging Market Perspective”, Address at the 2012 Central Bank of Nigeria Board,, Cape Town, South Africa, June 27, 2012.

### India-specific modeling, analysis, and policy work
- Anand, Rahul and Eswar Prasad, 2010, “Optimal Price Indices For Targeting Inflation Under Incomplete Markets,” IMF Working Paper 10/200 (Washington: International Monetary Fund).
- Anand, Rahul, Magnus Saxegaard, and Shanaka J. Peiris, 2010, “An Estimated Model with Macrofinancial Linkages for India,” IMF Working Paper 10/21 (Washington: International Monetary Fund).
- Anand, Rahul, and Volodymyr Tulin, 2014, “Disentangling India’s Investment Slowdown,” IMF Working Paper 14/47 (Washington: International Monetary Fund).
- Patra, Michael, and Muneesh Kapur, 2010, “A Monetary Policy Model without Money for India,” IMF Working Paper 10/183 (Washington: International Monetary Fund).
- Mohanty, Madhusudan, and Marc Klau, 2004, “Monetary Policy Rules in Emerging Market Economies: Issues and Evidence,” BIS Working Papers 149, Bank for International Settlements.
- Reserve Bank of India, 2014, “Report of the Expert Committee to Revise and Strengthen the Monetary Policy Framework,” January. Available at http://rbidocs.rbi.org.in/rdocs/PublicationReport/Pdfs/ECOMRF210114_F.pdf
- International Monetary Fund, 2011b, India: 2010 Article IV Consultation, IMF Country Report 11/50 (Washington: International Monetary Fund).
- International Monetary Fund, 2012, India: 2012 Article IV Consultation, IMF Country Report 12/96 (Washington: International Monetary Fund).
- International Monetary Fund, 2013, India: 2013 Article IV Consultation, IMF Country Report 13/37 (Washington: International Monetary Fund).
- International Monetary Fund, 2014a, India: 2014 Article IV Consultation, IMF Country Report 14/57 (Washington: International Monetary Fund).
- International Monetary Fund, 2014b, India: Selected Issues, IMF Country Report 14/58 (Washington: International Monetary Fund).

### IMF and global economic context
- International Monetary Fund, 2011a, “Slowing Growth, Rising Risks,” Chapter 3 of World Economic Outlook (Washington: International Monetary Fund).
- Catão, Luis, and Chang, Roberto, 2010, “World Food Prices and Monetary Policy,’ IMF Working Paper 10/161 (Washington: International Monetary Fund).
- Walsh, James P, 2011, “Reconsidering the Role of Food Prices in Inflation,” IMF Working Paper 11/71 (Washington: International Monetary Fund).

*Source: _wp14178 - REFERENCES (IMF PDF).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2014/_wp14178.pdf_