## htnea2025003

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---

### Key takeaways
- The monetary policy stance captures the gap between the real policy interest rate r_t and the economy’s real neutral interest rate r_t^∗.
- Policy characterization:
  - accommodative if r_t < r_t^∗ (tends to raise output growth above potential);
  - restrictive if r_t > r_t^∗.
- The real neutral rate is a superior benchmark but difficult to estimate because it varies with the state of the economy and unobserved shocks, including financial conditions.
- Policy is often framed relative to the “natural” real interest rate r̄_t^∗ (the steady-state level of the neutral rate).
- Under some conditions real interest rates may need to rise well above the natural rate r̄_t^∗ for policy to be restrictive, particularly when:
  - financial conditions are relatively loose;
  - fiscal policy is expansionary;
  - the output gap is initially positive;
  - monetary policy transmission is weak.
- In open economies assessments should also consider:
  - deviations in real exchange rates from equilibrium levels;
  - sensitivity of the real economy to such deviations;
  - global demand developments.
- Final assessment requires judgment because of model uncertainty and data limitations.

### Conceptual framework and IS representation
- Stance operates by influencing financing costs; in interest-rate–focused frameworks the primary instrument is the policy interest rate.
- Common stance measure: real interest rate gap r_t − r_t^∗ (both in real terms).
- IS curve (closed-economy workhorse):
  - x_t = ρ x_{t−1} − (1 − ρ) σ (r_t − r_t^∗)  (equation (1a))
  - x_t is the output gap; ρ and σ represent aggregate demand momentum and interest elasticity of demand.
- Real interest and neutral rate are interpreted over a “medium-term horizon” (for example, over the next couple of years).
- Parameters ρ and σ implicitly depend on structural factors such as financial development and the share of private investment and durables.
- Framework is forward-looking in practice; interest rate terms capture current and future rate paths.
- For quantity-based operating frameworks there is a short-term interest rate consistent with the targeted level of reserve money.

### Decomposing the neutral rate and policy implications
- Decomposition: r_t^∗ = r̄_t^∗ + r_{ct}^∗ leading to:
  - x_t = ρ x_{t−1} − (1 − ρ) σ [r_t − (r̄_t^∗ + r_{ct}^∗)]  (equation (1b))
- Components:
  - r̄_t^∗: long-term (natural) component driven by slow-moving forces (productivity growth, demographics); roughly constant over business-cycle horizons (example: say 0.5 percent for the United States as of 2023).
  - r_{ct}^∗: cyclical component driven by temporary factors (shocks to government spending, financial conditions, autonomous demand).
- Practical implications (bulleted):
  - A policy of gradual “normalization” (slow convergence of the real rate to r̄_t^∗ from a lower level) can be highly expansionary if r_{ct}^∗ is close to zero or positive.
  - Setting the real rate above r̄_t^∗ can still be expansionary if r_{ct}^∗ is sizable and persistently positive so that r_t − r_t^∗ remains persistently negative.
  - Conversely, setting the real rate at r̄_t^∗ can be restrictive if r_{ct}^∗ is persistently negative (e.g., during/post global financial crisis or European sovereign debt crisis).
  - If the central bank needs to cool a hot economy quickly (x_{t−1} >> 0) and aggregate demand persistence is high (ρ high), setting r_t = r̄_t^∗ + r_{ct}^∗ may not be sufficient; a more forceful tightening that pushes r_t well above r_{ct}^∗ and potentially far above r̄_t^∗ may be needed.
- Estimation guidance:
  - Estimating r_t^∗ that incorporates short-to-medium–term deviations r_{ct}^∗ is more relevant; when unavailable, use r̄_t^∗ plus qualitative assessment of r_{ct}^∗ based on financial conditions.
  - Magnitude of the interest rate gap needs to be sufficiently large given high uncertainty in r_t^∗ estimates; use various methodologies to enhance confidence.

### Open-economy considerations (Box 1)
- Exchange rates and exchange rate regimes critically influence the monetary policy stance and transmission in small open economies.
- Monetary policy affects international relative prices, shifting expenditure patterns, net exports, the real economy, and inflation.
- Modified IS including effective exchange rate (equation (I) in source):
  - x_t = ρ_x x_{t−1} − ρ_r σ mci_t + ρ_f x^f_t
  - mci_t = σ_r (r_t − ( r̄_t^* + r_{ct}^* )) − (1−σ_r)(z_t − z_t^*)  (equation (II))
- Definitions and implications:
  - Real exchange rate z_t = s_t + (p^f_t − p_t) (logs); equilibrium z_t^* consistent with internal and external balances.
  - An appreciating real effective exchange rate relative to equilibrium implies tighter monetary conditions.
  - Foreign output gap x^f_t affects extent and duration of domestic tightening required.
  - Uncovered interest parity linkage:
    - E_t Δs_{t+1} = (m_t − m^f_t − prp_m_t)  (equation (III))
  - Shocks to country-specific risk premia (prp_m_t) can alter desired short-term nominal rates depending on pass-through to aggregate demand.
- Key structural determinants:
  - Sensitivity of real exchange rates to nominal interest differentials influences the required policy adjustments.
  - Degree of openness determines sensitivity of output to real exchange rate changes.
- Cautions:
  - MCIs can be misleading when exchange rates move in response to non-monetary shocks.
  - Challenges exist from methods used to construct MCI weights.

### Measuring real short-term interest rates (Box 1)
- Real policy interest rate:
  - r_t = m_t − E_t π_{t+1}  (equation (2a))
  - E_t π_{t+1} is the annualized expected percent change in the price level between t and t+1.
- Designated policy nominal rate varies across countries (deposit facility rate, marginal lending rate, (reverse) repo rate, overnight interbank lending rate).
- Where disconnects exist between policy and market rates, other short-term market rates may better gauge actual stance.
- Expectations about future short rates matter; risk-free rates at a one-to-three–year horizon (and longer for some advanced economies) help capture agents’ expectations about the policy path.
- Guidance on inflation-expectations measures:
  - Use a range of measures corresponding to the horizon of nominal rates; report real rates deflated with different expectation measures when possible.
  - Survey-based measures merit more weight if frequent and reliable; market-based measures can be volatile and contain risk premiums.
  - Where inflation expectations are unavailable, realized core inflation can be a useful alternative deflator.
- Empirical example:
  - In Costa Rica, divergence between survey and market-based inflation expectations implied a six months difference regarding when the stance became restrictive; toward early 2024 a clearer consensus emerged.

### Measuring the natural and neutral real interest rates (Box 1)
- Two interpretations:
  1. Slow-moving medium-to-long–term variable r̄_t^* driven by demographics, inequality, productivity, financial development.
  2. Short-term variable r_t^* with a substantial cyclical component (Wicksellian definition).
- For policy assessment, a short-to-medium–term neutral rate r_t^* is preferable because it incorporates cyclical and higher-frequency considerations.
- Using a long-term r̄_t^* is less data-intensive and easier to communicate, but accounting for r_{ct}^* is important.
- At abrupt financing shocks (e.g., European sovereign debt crisis), cyclical r_t^* accounting for credit crunch and spreads would be much lower than r̄_t^*, implying that returning to equilibrium requires a more accommodative policy than suggested by r̄_t^*.

### Methods to estimate r_t^* and r̄_t^*
- Approaches described:
  - Reduced-form models (Laubach and Williams variations), linking interest rate gaps to inflation and output gaps; can incorporate external financial variables for open economies.
  - Semistructural forecasting and policy models used by many central banks.
  - Practical alternatives:
    - Use long-term r̄_t^* from central bank long-horizon forecasts or market-implied measures (example: the three-month government bond yield five years ahead, adjusted for inflation expectations).
    - Term structure models (Nelson-Siegel, ACM, joint-component models) to fit sovereign yield curves and decompose nominal yields into expected nominal short-rate path and term premia.
    - Factor models (Del Negro and others 2017, 2019) using short-term rates, long-term rates, and expected inflation to extract r̄_t^* as a deep trend.
    - Approximate r̄_t^* with long-term potential growth estimates based on neoclassical growth-model intuition.
    - Secular-factor frameworks (Rachel and Smith 2017; Platzer and Peruffo 2022) for long-term contributors and cross-country comparisons.
- Practical notes:
  - Term-structure and factor-model approaches require sufficient market liquidity and long time series; Bayesian/Kalman filter techniques may be needed.
  - Judgment is necessary—selection depends on data availability, market development, and the degree to which cyclical, financial, and external factors should be incorporated.

### Box 2 — Using yield curve slope as an indicator of the policy stance (Chile example)
- Methodology:
  - Decompose sovereign yield curve with a term structure model to derive the path of the expected real short rate and obtain a market-based estimate of r̄_t^∗.
  - Use short-term real yield as a proxy for r_t so that the slope of the risk-adjusted yield curve estimates r_t − r̄_t^∗.
  - Implementation (Chile): difference between (2-year risk-neutral nominal rate minus 1y1y inflation expectations) and (10-year risk-neutral nominal rate minus 5y5y inflation expectations); risk-neutral rate from ACM model.
- Chile finding:
  - Over the four years shown, the yield curve evolution indicated a sharp tightening of the monetary policy stance between the end of 2020 and mid-2022, with a gradual loosening thereafter.
- Interpretation and caveats:
  - Need to consider cyclical component r_{ct}^∗ and that short-term sovereign yields may include risk premiums overlapping with r_{ct}^∗.
  - Given uncertainty in r^∗ estimates, the slope of the risk-adjusted real yield curve complements other methodologies.
- Practical guidance for data-constrained cases:
  - Derive domestic natural rates from long-term averages of spreads to a closely linked advanced economy and add to that economy’s r̄_t^∗.
  - Use regression methods relating spreads to country characteristics to project r̄_t^∗.
  - Report ranges under high uncertainty (example: Costa Rica’s r_t^* estimates between 0 and 3 percent; in 2023 deemed close to 1 percent).
- Robust policy posture in high uncertainty:
  - Adopt a “better safe than sorry” philosophy; place greater reliance on r_t^* estimates that imply a more proactive policy response to inflation (Brandao-Marques, Meeks, and Nguyen 2024).
  - Use economic slack and speed of transmission considerations to judge necessary policy response.

### Considerations related to transmission, other instruments, and estimation uncertainty
- Transmission chain:
  - Policy rate → short-term market rates → long-term rates → financial conditions → investment and consumption → aggregate demand and output gap → inflation with lags.
- Factors affecting transmission:
  1. Fast-moving changes in financial conditions mostly captured by r_{ct}^∗.
  2. Structural country-specific characteristics summarized by σ in equation (1a).
- Other monetary instruments:
  - QE eases financial conditions and depresses long-term yields via signaling and portfolio-rebalancing channels, compressing term premium and lowering expected policy path.
  - Express long-term real yield r_t^L as expected real policy rate path plus term premium φ; when QE is active, actual financing costs are lower than implied by policy rate.
  - FX interventions and capital flow management can affect financing conditions; effects may be temporary and limited.
  - Reserve requirements and quantitative instruments influence stance but are harder to quantify; capture via banks’ lending rates or FCIs where feasible.
  - Shadow rates provide proxies during effective lower bound periods.
- Estimation uncertainty and complementary metrics:
  - Neutral rate estimates can be volatile and subject to ex post revisions (notably during the pandemic).
  - Some advocate a data-dependent approach placing more weight on observed inflation than on uncertain neutral rate estimates.
  - Complementary checks:
    - Compare stance relative to historical patterns and predicted rates from estimated short-term policy rules.
    - Use first-difference variants of Taylor rules to avoid estimating unobservables (caveat: could imply significant tightening—example: up to 12 percent during 2024–26 in the United States under certain implementations).
    - Derive inflation projections under various interest rate assumptions to evaluate adequacy of current stance.

*IMF | How to Note NOTE/2025/002 — How to Measure the Monetary Policy Stance; Olamide Harrison and Vina Nguyen; January 2025*

### References .............................................................................................................

### htnea2025003 - References .............................................................................................................

### Key Takeaways
- The monetary policy stance captures the gap between the real policy interest rate 푟푟_t and the economy’s real neutral interest rate 푟푟_t^∗.
- Policy is:
  - accommodative if 푟푟_t < 푟푟_t^∗ (tends to raise output growth above potential);
  - restrictive if 푟푟_t > 푟푟_t^∗.
- The real neutral rate is a superior benchmark but difficult to estimate because it varies with the state of the economy and unobserved shocks, including financial conditions.
- The policy stance is often framed relative to the “natural” real interest rate 푟̅_t^∗ (the steady-state level of the neutral rate).
- Under some conditions real interest rates may need to rise well above the natural rate 푟̅_t^∗ for policy to be restrictive, particularly when:
  - financial conditions are relatively loose;
  - fiscal policy is expansionary;
  - the output gap is initially positive;
  - monetary policy transmission is weak.
- In open economies assessments should also consider:
  - deviations in real exchange rates from equilibrium levels;
  - sensitivity of the real economy to such deviations;
  - global demand developments.
- Final assessment requires judgment because of model uncertainty and data limitations.

### Conceptual framework and IS representation
- The monetary policy stance operates by influencing financing costs; in interest-rate–focused frameworks the primary instrument is the policy interest rate.
- A common approach measures the stance by the real interest rate gap 푟푟_t − 푟푟_t^∗ (both in real terms).
- The IS curve (closed-economy workhorse) used to organize effects is:
  - 푥푥_t = ρ푥푥_{t−1} − (1 − ρ) σ (푟푟_t − 푟푟_t^∗)  (equation (1a))
  - where 푥푥_t is the output gap; ρ and σ represent aggregate demand momentum and interest elasticity of demand.
- The real interest rate and neutral rate are interpreted over a “medium-term horizon” relevant for consumption/savings decisions (for example, over the next couple of years).
- Parameters ρ and σ implicitly depend on structural factors such as financial development and the share of private investment and durables.
- The IS framework is forward-looking in practice (microfounded versions include expected future demand), and interest rate terms capture current and future rate paths.
- For quantity-based operating frameworks there is a short-term interest rate consistent with the targeted level of reserve money.

### Decomposing the neutral rate and policy implications
- Decompose the neutral rate into a long-term (natural) component 푟̅_t^∗ and a cyclical deviation 푟_ct^∗ so that 푟_t^∗ = 푟̅_t^∗ + 푟_ct^∗, leading to:
  - 푥_t = ρ푥_{t−1} − (1 − ρ) σ [푟_t − (푟̅_t^∗ + 푟_ct^∗)]  (equation (1b))
- Long-term component 푟̅_t^∗ is driven by slow-moving forces (productivity growth, demographics) and can be treated as roughly constant over business-cycle horizons (example: say 0.5 percent for the United States as of 2023).
- Cyclical component 푟_ct^∗ is driven by temporary factors (shocks to government spending, financial conditions, autonomous demand).
- Practical implications:
  - A policy of gradual “normalization” (slow convergence of the real rate to 푟̅_t^∗ from a lower level) can be highly expansionary if 푟_ct^∗ is close to zero or positive.
  - Setting the real rate above 푟̅_t^∗ can still be expansionary if 푟_ct^∗ is sizable and persistently positive so that 푟_t − 푟_t^∗ remains persistently negative.
  - Conversely, setting the real rate at 푟̅_t^∗ can be restrictive if 푟_ct^∗ is persistently negative (e.g., during/post global financial crisis or European sovereign debt crisis).
  - If the central bank needs to cool a hot economy quickly (푥_{t−1} >> 0) and aggregate demand persistence is high (ρ high), setting 푟_t = 푟̅_t^∗ + 푟_ct^∗ may not be sufficient; a more forceful tightening that pushes 푟_t well above 푟_ct^∗ and potentially far above 푟̅_t^∗ may be needed.
- Estimating 푟_t^∗ that incorporates short-to-medium–term deviations 푟_ct^∗ is more relevant; when unavailable, a long-term estimate 푟̅_t^∗ plus qualitative assessment of 푟_ct^∗ based on financial conditions can inform stance evaluations.
- The magnitude of the interest rate gap needs to be sufficiently large given high uncertainty in 푟_t^∗ estimates; using various methodologies to estimate 푟_t^∗ can enhance confidence.

### Open-economy considerations
- For many small open economies exchange rates and exchange rate regimes critically influence the monetary policy stance and transmission of rate changes.
- Monetary policy affects international relative prices, shifting expenditure patterns, net exports, the real economy, and inflation.
- The IS curve can be modified to include the effective exchange rate (illustrated in Box 1).

### Illustrative simulations (Figure 1 key points)
- Figure 1 panels illustrate six scenarios (real rates and output gap dynamics) showing:
  - A policy of gradual normalization can be expansionary when cyclical component 푟_ct^∗ is zero or positive.
  - Setting real rates above 푟̅_t^∗ is not necessarily restrictive if 푟_ct^∗ is large and positive.
  - Even setting the real rate to 푟̅_t^∗ + 푟_ct^∗ may not be sufficient to cool a large or persistent output gap.
- Numerical annotations and axes in Figure 1 show percent scales and time periods (e.g., 0 4 8 12 16 20 on the horizontal axis) and percent levels on vertical axes as in the source figures.

### Empirical and historical notes
- Example long-term illustrative values and historical context:
  - Long-term natural rate example: 0.5 percent for the United States as of 2023.
  - Del Negro and others (2017) showed a decline in the natural interest rate in the United States from 2–2.5 percent in late 1990s to around 1 percent in 2016, attributed to an increasing global demand for safe assets.

*IMF | How to Note NOTE/2025/002 — How to Measure the Monetary Policy Stance; Olamide Harrison and Vina Nguyen; January 2025*

### Box 1. Open-Economy Considerations for Assessing the Monetary Stance

### Box 1. Open-Economy Considerations for Assessing the Monetary Stance

### Open-economy transmission and monetary conditions
- Monetary policy transmits to the real economy and inflation through changes in international relative prices that trigger expenditure-switching effects and changes in net exports.
- To capture both the real interest and exchange rate channels, the real interest rate gap in equation (I) can be substituted with the deviation of a monetary conditions index (MCI), termed mci_t, from its long-term trend:
  - x_t = ρ_x x_{t−1} − ρ_r σmci_t + ρ_f x^f_t  (equation (I) as presented in source)
  - mci_t = σ_r (r_t − ( r̄_t^* + r_{ct}^* )) − (1−σ_r)(z_t − z_t^*)  (equation (II) as presented in source)
- The real exchange rate z_t is defined as the sum of the log nominal exchange rate s_t and the (log) terms of trade [p^f_t − p_t]. The equilibrium level z_t^* is the level consistent with internal and external balances in the long term.
- An appreciating real effective exchange rate relative to equilibrium implies tighter monetary conditions, which pass through to aggregate demand.
- The foreign output gap x^f_t directly affects the extent and duration of tightening required to close the domestic output gap—larger foreign output gaps require tighter policy for longer to achieve price stability.
- Exchange rates and interest rates are linked via an uncovered interest parity condition:
  - E_t Δs_{t+1} = (m_t − m^f_t − prp_m_t)  (equation (III) as presented in source)
- Shocks to country-specific risk premia (prp_m_t) that induce real exchange rate changes can alter desired short-term nominal rates depending on pass-through to aggregate demand.
- Key structural determinants:
  - Sensitivity of real exchange rates to nominal interest differentials influences how much policy must change to achieve a desired stance: greater exchange rate responsiveness implies smaller required interest-rate adjustments; weaker responsiveness implies monetary policy may need to remain tighter for longer.
  - Degree of openness determines sensitivity of output to real exchange rate changes—greater openness implies stronger effects of terms-of-trade changes on aggregate demand.
- For small open economies, the natural interest rate depends positively on expected world output growth (Clarida, Gali, and Gertler 2001). Higher expected world output growth requires higher domestic real short-term rates to maintain a neutral stance, all else equal.
- Caution in MCI interpretation:
  - MCIs can be misleading when exchange rates move in response to non-monetary shocks (e.g., depreciations that tighten financing conditions); a mechanical reading can imply incorrect policy prescriptions.
  - Challenges also arise from methods used to construct MCI weights.

### Measuring real short-term interest rates
- Real policy interest rate r_t is measured by deflating the nominal short-term policy rate m_t by inflation expectations for the relevant horizon:
  - r_t = m_t − E_t π_{t+1}  (equation (2a) as presented in source)
  - E_t π_{t+1} is the annualized expected percent change in the price level between t and t+1.
- The designated policy nominal rate varies across countries (deposit facility rate, marginal lending rate, (reverse) repo rate, overnight interbank lending rate). In many countries, disconnects between the policy rate and other short-term rates (interbank, sovereign yields) exist due to liquidity, market development, counterparties, or multiple instruments.
  - In such cases, other short-term market rates may better gauge the actual stance than central bank policy rates.
- Expectations about future short rates matter; risk-free rates at a one-to -three–year horizon (and longer horizons for some advanced economies) help capture agents’ expectations about the policy path and where policy rates may peak or bottom out.
- Guidance on inflation-expectations measures:
  - Use a range of measures corresponding to the horizon of nominal rates; report real rates deflated with different expectation measures when possible.
  - Survey-based measures generally merit more weight if frequent and reliable; market-based measures can be volatile and contain risk premiums.
  - Where inflation expectations are unavailable, realized core inflation can be a useful alternative deflator—core inflation preferred to headline to “look through” temporary volatility.
- Empirical examples:
  - In Costa Rica, divergence between survey and market-based inflation expectations implied a six months difference regarding when the stance became restrictive; toward early 2024 a clearer consensus emerged.

### Measuring the natural and neutral real interest rates
- Two interpretations of natural/neutral rates:
  1. Slow-moving medium-to -long–term variable r̄_t^* driven by demographics, inequality, productivity, financial development (recent literature often calls this the natural real interest rate).
  2. Short-term variable r_t^* with a substantial cyclical component (Wicksellian definition): the real short-term rate that would prevail if the output gap were closed and inflation stable.
- For policy assessment, a short-to -medium–term neutral rate r_t^* is preferable because it incorporates cyclical and higher-frequency considerations and aligns the interest rate gap more proportionally with output gap measures.
- Using a long-term r̄_t^* can be less data-intensive and easier to communicate, but accounting for the deviation r_{ct}^* between short-term r_t^* and long-term r̄_t^* is important to measure stance accurately—financial conditions can inform the sign of current deviations.
- At abrupt financing shocks (e.g., European sovereign debt crisis), a cyclical r_t^* accounting for credit crunch and spreads would be much lower than r̄_t^*, implying that returning to equilibrium requires a more accommodative policy than suggested by long-term r̄_t^*.

### Methods to estimate r_t^* and r̄_t^*
- Reduced-form models (Laubach and Williams variations) produce medium-term equilibrium estimates by linking interest rate gaps to inflation and output gaps; modifications can incorporate external financial variables (credit spreads, exchange rates) for small open economies.
- Semistructural forecasting and policy models used by many central banks can estimate r_t^* and support policy analysis.
- Practical alternatives when short-term r_t^* is difficult to estimate:
  - Use long-term r̄_t^* derived from central bank long-horizon forecasts or surveys of market participants; example: the three-month government bond yield five years ahead, adjusted for inflation expectations, as a market-implied r̄_t^*.
  - Term structure models to fit sovereign yield curves and decompose nominal yields into expected nominal short-rate path and term premia (real term premium and inflation risk premium); models cited include Nelson-Siegel, ACM, and joint-component models (Hördahl and Tristani 2014; Abrahams and others 2015).
  - Factor models (Del Negro and others 2017, 2019) that use short-term rates, long-term rates, and expected inflation to extract r̄_t^* as a deep trend of real short-term rates; 2019 version allows a common factor across countries.
  - Approximate r̄_t^* with long-term potential growth estimates based on neoclassical growth model intuition (natural rate ≈ sum of growth rate of per capita consumption and population growth under certain parameterizations).
  - Use secular-factor frameworks (Rachel and Smith 2017; Platzer and Peruffo 2022) to quantify long-term contributors to global r̄_t^* and for cross-country comparisons.
- Practical notes:
  - Term-structure and factor-model approaches require sufficient market liquidity and long time series; Bayesian/Kalman filter techniques may be needed.
  - Judgment is necessary—selection among approaches depends on data availability, market development, and the degree to which cyclical, financial, and external factors should be incorporated.

*Source: IMF How to Note (Box 1 text on open-economy considerations and interest-rate measurement).*

### Box 2. Using Yield Curve Slope as an Indicator of the Policy Stance—Example of Chile

### Box 2. Using Yield Curve Slope as an Indicator of the Policy Stance—Example of Chile

### Methodology: Yield curve slope as market-based estimate
- Decompose the sovereign yield curve using a term structure model to derive the path of the expected real short rate, providing a market-based estimate of 푟푟̅푡푡∗.
- Use the short-term yield in real terms as a proxy for 푟푟푡푡 so that the slope of the (risk-adjusted) yield curve provides a market-based estimate of 푟푟푡푡 − 푟푟̅푡푡∗.
- Specific implementation (as shown for Chile):
  - Chart the difference between (2-year risk-neutral nominal rate minus 1y1y inflation expectations) and (10-year risk-neutral nominal rate minus 5y5y inflation expectations).
  - The risk-neutral rate is obtained using ACM (Adrian, Crump, and Moench 2013) term structure model.

### Chile example and key finding
- Box Figure 2.1 (Chile: Slope of the Risk-Adjusted Real Yield Curve) shows:
  - Over the four years shown, the evolution of the yield curve indicated a sharp tightening of the monetary policy stance between the end of 2020 and mid-2022, with a gradual loosening thereafter.

### Interpretation and caveats
- Results require careful interpretation because:
  - Central banks need to consider not only the long-term natural interest rate 푟푟̅푡푡∗ but also the more cyclical component 푟푟푐푐푡푡∗, which can capture temporary shocks including shocks to financial conditions.
  - Short-term sovereign yields (three-month, one-year, or two-year) may encompass some risk premiums, creating overlap between the measured slope and 푟푟푐푐푡푡∗.
- Given considerable uncertainty around any estimates of 푟푟∗ (including with term structure models), the slope of the risk-adjusted real yield curve can usefully complement other methodologies for a holistic assessment of the stance.

### Practical guidance and extensions for data-constrained cases
- When data are constrained:
  - Derive domestic natural rates from a long-term average of the spread between the country’s real interest rate and that of a closely linked advanced economy; add this spread to available estimates of 푟푟̅푡푡∗ for the advanced economy.
  - Use regression methods to relate spreads to country characteristics and generate projections for 푟푟̅푡푡∗ with estimated coefficients.
- Reporting under high uncertainty:
  - Report a range rather than just a point estimate. Example cited: Costa Rica’s 푟푟푡푡∗ estimates are between 0 and 3 percent, and in 2023 it was deemed to be close to 1 percent (see Wales 2023 [IMF 2023]).
- Robust policy posture in high uncertainty:
  - Adopt a “better safe than sorry” philosophy, placing greater reliance on 푟푟푡푡∗ estimates that imply a more proactive monetary policy response to inflation (Brandao-Marques, Meeks, and Nguyen 2024).
  - Use considerations such as economic slack and speed of transmission to judge the necessary policy response.

### Considerations related to transmission
- Transmission channels and implications:
  - Policy rate → short-term market rates → long-term rates → financial conditions → investment and consumption → aggregate demand and output gap → inflation with lags.
  - How the short-term policy rate affects market rates and long-term interest rates guides selection of the appropriate measure of r in equation (1a).
- Two groups of factors affecting transmission from interest rates to the output gap:
  1. Fast-moving changes in financial conditions mostly captured by 푟푟푐푐푡푡∗.
  2. Structural country-specific characteristics summarized by parameter 휎휎 in equation (1a).
- Practical notes:
  - Policy rates and interbank lending rates may diverge even at overnight maturity due to liquidity and other tools.
  - Longer-term benchmark rates can move out of sync with policy rates because of term premium dynamics; align inflation expectation horizons with the chosen nominal rate horizon when deflating nominal rates.
  - In economies with developed financial systems, two- to five-year government bonds may be most relevant for real activity; choose horizons to match country-specific debt maturity moments.
- Financial conditions and asymmetries:
  - Measures emphasizing volatility and risk premiums (e.g., Monetary and Capital Markets Department Financial Condition Index [FCI]) are closely tied to financial frictions; when frictions are present, the appropriate policy rate can be lower than 푟푟̅푡푡∗ (De Fiore and Tristani 2013).
  - Risk premiums can change faster than policy transmission; swings in market sentiment can dampen or magnify interest rate impacts.
  - Persistent divergence between FCIs and the direction of interest rate changes may require adjusting the stance; FCIs can be incorporated into short-term 푟푟푡푡∗ measures through methods that capture FCIs’ impact on output and inflation gaps.
- Exchange rate and monetary conditions indexes (MCIs):
  - Exchange rate movements are key in small open economies and many low-income countries where the exchange rate is the primary monetary transmission channel.
  - MCIs combine short-term interest rate and exchange rate effects; several central banks have used MCIs for monitoring and calibration.

### Other monetary instruments and how to account for them
- Large-scale asset purchases / quantitative easing (QE):
  - QE eases financial conditions and depresses long-term yields via signaling and portfolio rebalancing channels, compressing term premium and lowering expected policy path.
  - Express long-term real yield r_t^L as the sum of expected real policy rate path and term premium φ; when QE is active, actual financing costs are lower than implied by the policy rate, making the stance more accommodative.
  - To account for QE, compare current real yields at longer horizons (e.g., 10 years) with steady-state average values that include average term premium; assume expected real short rate returns to equilibrium by the horizon and equilibrium 푟푟∗ remains unchanged.
- Other tools and complexities:
  - FX interventions (FXI) and capital flow management can affect financing conditions, potentially improving policy trade-offs but with effects that may be temporary and limited; their net impact depends on relative strength and persistence of channels.
  - Reserve requirements and quantitative instruments can influence the stance but are harder to quantify; consider banks’ lending rates and capture channels via FCIs where feasible.
  - Shadow rates literature provides proxies for policy rates during effective lower bound periods (Wu and Xia 2016; Krippner 2012, 2013, 2020).

### Considerations related to estimation uncertainty and complementary metrics
- Challenges in real-time estimation:
  - Neutral rate estimates can be volatile and subject to ex post revisions (notably during the pandemic).
  - Some advocate a data-dependent approach that places more weight on observed inflation than on uncertain neutral rate estimates (Benigno and others 2024).
  - Structural forecasting models typically require steady-state natural or neutral rates as equilibrium prices.
- Complementary assessments for robustness:
  - Compare stance relative to historical patterns: assess deviations of actual policy rates from predicted rates based on an estimated short-term policy rate rule.
  - Use first-difference variants of Taylor rules to avoid estimating unobservables; note potential drawbacks (example: could imply significant tightening of policy rates up to 12 percent during 2024–26 in the United States under certain implementations).
  - Derive inflation projections under various interest rate assumptions to evaluate adequacy of current stance, recognizing this still requires understanding of steady state and transmission and confidence in models used.

*IMF | How to Note — Box 2. Using Yield Curve Slope as an Indicator of the Policy Stance—Example of Chile*

### References

### htnea2025003 - References

### Models and Term Structure Literature
- Abrahams, M., T. Adrian, R. K. Crump, and E. Moench. 2015. “Decomposing Real and Nominal Yield Curves.” Federal Reserve Bank of New York Staff Reports,  New York.
- Adrian, T., R. K. Crump, and E. Moench. 2013. “Pricing the Term Structure with Linear Regressions.” Journal of Financial Economics 110 (1): 110–38.
- Christensen, J. H. E., F. X. Diebold, and G. Rudebusch. 2011. “The Affine Arbitrage-Free Class of Nelson-Siegel Term Structure Models.” Journal of Econometrics 164 (1): 4–20.
- Krippner, L. 2012. “Measuring the Stance of Monetary Policy in Zero Lower Bound Environments.” Economics Letters 118:135–38.
- Krippner, L. 2013. “A Tractable Framework for Zero Lower Bound Gaussian Term Structure Models.” Discussion Paper, Reserve Bank of New Zealand, Wellington, February.
- Krippner, L. 2020. “A Note of Caution on Shadow Rate Estimates.” Journal of Money, Credit, and Banking 52 (4): 951–62.
- Wu, J., and F. Xia. 2016. “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound.” Journal of Money, Credit, and Banking 48 (2–3): 253–91.

### Natural Rate, r*, and Interest Rate Trends
- Benigno, G., B. Hofmann, G. Nuno, and D. Sandri. 2024. “Quo vadis, r*? The Natural Rate of Interest after the Pandemic.” BIS Quarterly Review.
- Del Negro, M., D. Giannone, M. P. Giannoni, and A. Tambalotti. 2017. “Safety, Liquidity, and the Natural Rate of Interest.” Brookings Papers on Economic Activity 1:235–316.
- Del Negro, M., D. Giannone, M. P. Giannoni, and A. Tambalotti, 2019. "Global Trends in Interest Rates," Journal of International Economics, Elsevier, vol. 118(C):248–262.
- Grigoli, F., J. Platzer, and R. Tietz. 2023. “Low for (Very) Long? A Long-Run Perspective on r* across Advanced Economies.” IMF Working Paper 23/085, International Monetary Fund, Washington, DC.
- Holston, K., T. Laubach, and J. C. Williams. 2017. “Measuring the Natural Rate of Interest: International Trends and Determinants.” Journal of International Economics 108 (Supplement 1): S39–S75.
- Holston, K., T. Laubach, and J. C. Williams. 2023. “Measuring the Natural Rate of Interest after COVID‑19.” Federal Reserve Bank of New York Staff Reports 1063, New York, June.
- Obstfeld, M. 2023. “Natural and Neutral Real Interest Rates: Past and Future.” NBER Working Paper 31949, National Bureau of Economic Research, Cambridge, MA.
- Pescatori, A., and J. Turunen. 2015. “Lower for Longer: Neutral Rates in the United States.” IMF Working Paper 15/135, International Monetary Fund, Washington, DC.
- Platzer, J., and M. Peruffo. 2022. “Secular Drivers of the Natural Rate of Interest in the United States: A Quantitative Evaluation.” IMF Working Paper 2022/030, International Monetary Fund, Washington, DC.
- Rachel, L., and T. D. Smith. 2017. “Are Low Real Interest Rates Here to Stay?” International Journal of Central Banking 3 (13): 1  –42.
- Arena, M., G. Di Bella, A. Cuevas, B. Gracia, V. Nguyen, and A. Pienkowski. 2020. “It Is Only Natural: Europe’s Low Interest Rates.” IMF Working Paper 20/116, International Monetary Fund, Washington, DC.
- Borraccia, G., R. Espinoza, V. Guzzo, F. Jiang, R. Lafarguette, V. Nguyen, M. Segoviano, and others. 2023. “Financial Conditions in Europe: Drivers, Dynamics, and Macroeconomic Implications.” IMF Working Paper 23/209, International Monetary Fund, Washington, DC.
- Del Negro, M., D. Giannone, M. P. Giannoni, and A. Tambalotti. 2017. “Safety, Liquidity, and the Natural Rate of Interest.” Brookings Papers on Economic Activity 1:235–316.

### Inflation, Expectations, and Inflation Risk Premia
- Chan, J. C. C., T. E. Clark, and G. Koop. 2018. “A New Model of Inflation, Trend Inflation, and Long-Run Inflation Expectations.” Journal of Money, Credit, and Banking 50 (1): 5  –53.
- Coibion, O., Y. Gorodnichenko, and R. Kamdar. 2018. “The Formation of Expectations, Inflation, and the Phillips Curve.” Journal of Economic Literature 56 (4): 1447–91.
- Hördahl, P., and O. Tristani. 2014. “Inflation Risk Premia in the Euro Area and the United States.” International Journal of Central Banking 10 (September): 1–47.
- Kozicki, S., and P. Tinsley. 2012. “Effective Use of Survey Information in Estimating the Evolution of Expected Inflation.” Journal of Money, Credit, and Banking 44 (1): 145–69.
- Evans, M. D. 1998. “Real Rates, Expected Inflation, and Inflation Risk Premia.” Journal of Finance 53 (1): 187–218.
- Reis, R. 2023. “Four Mistakes in the Use of Measures of Expected Inflation.” AEA Papers and Proceedings 113:47–51.
- Mankiw, N. G., Reis, R., and Wolfers, J. 2003. “Disagreement about inflation expectations.” NBER Macroeconomics Annual 18:209–48.
- Krippner, L. 2020. “A Note of Caution on Shadow Rate Estimates.” Journal of Money, Credit, and Banking 52 (4): 951–62.

### Monetary Policy, Transmission, and Operational Frameworks
- Clarida, R., J.    Gali, and M. Gertler. 2001. “Optimal Monetary Policy in Open Versus Closed Economies: An Integrated Approach.” American Economic Review 91 (2): 248–52.
- De Fiore, F., and O. Tristani. 2013. “Optimal Monetary Policy in a Model of the Credit Channel.” The Economic Journal 123:571.
- Brandão-Marques, L., G. Gelos, T. Harjes, R. Sahay, and Y. Xue. 2020. “Monetary Policy Transmission in Emerging Markets and Developing Economies.” IMF Working Paper 20/35, International Monetary Fund, Washington, DC.
- Brandão-Marques, L., R. Meeks, and V. Nguyen, “Monetary Policy with Uncertain Inflation Persistence.” IMF Working Paper 24/047, International Monetary Fund, Washington, DC.
- Laubach, T., and J. C. Williams. 2003. “Measuring the Natural Rate of Interest.” Review of Economics and Statistics 85 (4): 1063–70.
- Orphanides, A., and J. C. Williams. 2002. “Robust Monetary Policy Rules with Unknown Natural Rates.” Brookings Papers on Economic Activity 2002 (2): 63–118.
- Svensson, L. E. 1997. “Inflation Forecast Targeting: Implementing and Monitoring Inflation Targets.” European Economic Review 41 (6): 1111–46.
- Svensson, L. E. 2001. “Independent Review of the Operation of Monetary Policy in New Zealand: Report to the Minister of Finance.”https://www.treasury.govt.nz/sites/default/files/2007-11/indrevopmonpol.pdf.
- Woodford, M. 2007. “The Case for Forecast Targeting as a Monetary Policy Strategy.” Journal of Economic Perspectives 21 (4): 3–24.
- Maehle, N., and D. King. 2022. “Transitioning Operational Targets—From Reserve Money to Interest Rates.” In Monetary and Capital Markets Department: Technical Assistance Handbook. Washington, DC: International Monetary Fund, July.
- Della Valle, G., D. King, and R. Veyrune. 2022. “Reserve Requirements.” In Monetary and Capital Markets Department: Technical Assistance Handbook. Washington, DC:  International Monetary Fund.
- Pranovich, M., T. Hlédik, N. Mæhle, and C. Selander. 2021. Taking Stock of IMF Capacity Development on Monetary Policy Forecasting and Policy Analysis Systems. Washington, DC: International Monetary Fund.
- Cusbert, T. 2017. “Estimating the NAIRU and the Unemployment Gap.” Bulletin on Australian Economy, Reserve Bank of Australia, June.
- Eika, K. H., N. R. Ericsson, and R.    Nymoen. 1996. “Hazards in Implementing a Monetary Conditions Index.” Oxford Bulletin of Economics and Statistics 58 (4): 765–90.
- Krippner, L. 2012. “Measuring the Stance of Monetary Policy in Zero Lower Bound Environments.” Economics Letters 118:135–38.

### Macro-Financial Dynamics, Risk, and Liquidity
- Adrian, T., F. Duarte, and T. Iyer. 2023. “The Market Price of Risk and Macro-Financial Dynamics.” IMF Working Paper 2023/199, International Monetary Fund, Washington, DC.
- Del Negro, M., D. Giannone, M. P. Giannoni, and A. Tambalotti. 2017. “Safety, Liquidity, and the Natural Rate of Interest.” Brookings Papers on Economic Activity 1:235–316.
- Borraccia, G., R. Espinoza, V. Guzzo, F. Jiang, R. Lafarguette, V. Nguyen, M. Segoviano, and others. 2023. “Financial Conditions in Europe: Drivers, Dynamics, and Macroeconomic Implications.” IMF Working Paper 23/209, International Monetary Fund, Washington, DC.
- Hördahl, P., and O. Tristani. 2014. “Inflation Risk Premia in the Euro Area and the United States.” International Journal of Central Banking 10 (September): 1–47.

### Foundational and Historical Works in Interest Theory and Growth
- Fisher, I. 1930. The Theory of Interest. New York:  Macmillan.
- Ramsey, F. P. 1928. “A Mathematical Theory of Saving.” Economic Journal 38 (152): 543–59.
- Solow, R. M. 1956. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70 (1): 65–94.
- Wicksell, K. 1936. Interest and Prices.  Auburn, AL:    Ludwig von Mises Institute.
- Laidler, D. 2020. “Interactions among Economic Ideas, Policies and Experience-The Establishment of Inflation Targeting in Canada, 1991–2001.” Review of Economic Analysis 12 (2): 133–65.
- Linde, J. 2001. “Testing for the Lucas Critique: A Quantitative Investigation.” American Economic Review 91 (4): 986–1005.
- Lucas, R.    E., "Econometric Policy Evaluation: A Critique." 1976. in K. Brunner and A. Me.ltzer, eds, The Phillips Curve and Labor Markets, Carnegie Rochester Conference Series, New York, North Holland, 19—46
- Ramsey, F. P. 1928. “A Mathematical Theory of Saving.” Economic Journal 38 (152): 543–59.
- Laubach, T., and J. C. Williams. 2003. “Measuring the Natural Rate of Interest.” Review of Economics and Statistics 85 (4): 1063–70.

### Country and IMF-specific Analyses
- International Monetary Fund (IMF). 2023. “Costa Rica Selected Issue Paper.” https://www.imf.org/en/Publications/CR/Issues/2023/12/22/Costa-Rica-Selected-Issues-542918.
- Pescatori, A., and J. Turunen. 2015. “Lower for Longer: Neutral Rates in the United States.” IMF Working Paper 15/135, International Monetary Fund, Washington, DC.
- Platzer, J., and M. Peruffo. 2022. “Secular Drivers of the Natural Rate of Interest in the United States: A Quantitative Evaluation.” IMF Working Paper 2022/030, International Monetary Fund, Washington, DC.
- Pranovich, M., T. Hlédik, N. Mæhle, and C. Selander. 2021. Taking Stock of IMF Capacity Development on Monetary Policy Forecasting and Policy Analysis Systems. Washington, DC: International Monetary Fund.

*htnea2025003 - References*

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_Source: https://www.imf.org/-/media/files/publications/howtonotes/2025/english/htnea2025003.pdf_