Inflation-Expectations | Macro Paper Warehouse

Aggregate Implications of Heterogeneous Inflation Expectations: The Role of Individual Experience

Mon, 01 Jan 0001 00:00:00 +0000

Consumers’ inflation expectations are heterogeneous across birth cohorts and history-dependent: using panel data from the Survey of Consumer Expectations (SCE), the paper documents that each cohort’s inflation forecast is anchored to its cumulative inflation history, with the degree of anchoring estimated structurally. The authors model this via an experience-based Kalman filter in which each agent’s forecast combines a common Kalman-filtered signal (derived from food prices) with a cohort-specific reference term built from the cohort’s entire prior sequence of expected inflation. The estimated history-weight parameter θ is negative, confirming that agents positively weight their inflation history rather than overreacting to current news — a pattern that holds not only in US SCE and Michigan Survey of Consumers data but also across six European countries in the ECB Consumer Expectations Survey. Embedded in a Blanchard–Yaari perpetual-youth OLG New Keynesian model — where households hold experience-based expectations but firms set prices under rational Calvo frictions — the mechanism produces qualitatively different aggregate dynamics from full-information rational expectations (FIRE): after inflationary shocks, expectations initially underreact (agents anchor to the low-inflation steady state) and then persist well beyond the shock horizon as high inflation is gradually incorporated into cohort memory, generating hump-shaped expectation dynamics. For monetary policy, the optimal Taylor rule must be more aggressive after cost shocks than under FIRE: an energetic early response prevents the high-inflation episode from entering cohort memories, avoiding a self-reinforcing upward drift in inflation expectations. Applied to the 2021 high-inflation episode, the model predicts that the youngest cohorts — experiencing high inflation for the first time — will exhibit persistently elevated inflation expectations long after the supply shocks that caused the episode have dissipated.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

What are the four empirical patterns in the survey data, and do they hold outside the US?

Using the New York Fed’s Survey of Consumer Expectations (a monthly panel from 2013), the paper documents four patterns: (i) inflation expectations differ substantially across birth cohorts; (ii) cohort-specific inflation experience is age-clustered; (iii) individual inflation history is positively correlated with individual inflation expectations; (iv) cohorts do not differ in how they update to current information once their own inflation history is controlled for. These patterns hold in the Michigan Survey of Consumers and, with cohort fixed effects, across the ECB Consumer Expectations Survey covering six European countries — suggesting the mechanism is not US-specific.

How does the experience-based Kalman filter work, and what does estimation yield?

Each consumer’s forecast has two components: a standard Kalman filter signal common to all agents (extracted from food price data) and a cohort-specific reference term that is a weighted average of all past expectations formed by that cohort, governed by the parameter θ. Structurally estimated from SCE data using time fixed effects, θ is negative — meaning consumers positively anchor to their inflation history rather than over-extrapolating from current news. In a goodness-of-fit regression, the experience-based Kalman filter predicts observed cohort-level heterogeneity with a slope coefficient of 1.069, dominating lifetime average inflation and lagged inflation as predictors.

What is the general equilibrium model, and how do heterogeneous expectations enter the IS curve?

The model is a Blanchard–Yaari perpetual-youth OLG New Keynesian economy. Each surviving cohort solves a standard Euler equation using the experience-based expectations operator rather than rational expectations, yielding a history-dependent IS curve in which the effective real rate depends on the weighted average of each cohort’s reference inflation. Intermediate goods producers set prices under Calvo frictions with rational expectations, yielding a standard New Keynesian Phillips curve. The central bank follows a Taylor rule. The IS curve’s history-dependence means that past inflationary episodes — absorbed into cohort memory — affect present aggregate demand.

What do the impulse responses show under experience-based versus FIRE expectations?

Under a taste (demand) shock, experience-based expectations generate lower inflation on impact — agents anchor to the low-inflation steady state — but inflation remains elevated for longer as the shock is incorporated into cohort memory. Under a cost (supply) shock, two forces compete: anchoring to the steady state damps initial price pressure, but rational firms can raise prices by more because the IS curve becomes more inelastic; the net effect requires a stronger interest rate response than under FIRE. In both cases, household expectation dynamics are hump-shaped — initial underreaction followed by gradual build-up — consistent with evidence in Angeletos et al. (2021) and Pfajfar and Roberts (2018).

How does the optimal Taylor rule change under experience-based expectations?

After a cost shock the central bank should be more aggressive than under FIRE. The social cost of tolerating a transitory inflationary episode is much higher under experience-based expectations because it permanently shifts cohort memory upward, creating self-reinforcing dynamics in future periods. An aggressive early response prevents the episode from entering cohort references. After a taste shock the optimal response is similarly strong under both FIRE and experience-based expectations, so the memory channel adds little incremental urgency on the demand side.

What does the model predict about the 2021 high-inflation episode?

Feeding the model with actual monthly data through December 2021, average inflation expectations post-2021 are predicted to be both higher and more persistent under experience-based expectations than under FIRE or diagnostic expectations. Young cohorts, who experienced only low inflation in the 2010s, are updating their memory of inflation upward for the first time, creating a cohort-specific anchoring shift. The model implies that the 2021 episode could have long-lasting effects on consumer price expectations even if the supply shocks that caused it are fully transitory.

Inflation Expectations and the Slope of the Phillips Curve: Evidence from Firm Surveys

Mon, 01 Jan 0001 00:00:00 +0000

Do the inflation expectations of firms — rather than households or financial markets — shift the slope of the Phillips curve? Using a new panel of firm-level surveys matched to price-setting behavior, the authors find that firms with higher expected inflation adjust prices more aggressively in response to demand shocks, steepening the local Phillips curve slope. The effect is concentrated among firms that review prices frequently, suggesting a mechanism through the frequency of price adjustment rather than through the level of markups.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the main empirical finding on expectations and the Phillips curve slope?

Firms with higher measured inflation expectations exhibit a steeper relationship between demand conditions and price adjustment — the estimated Phillips curve slope is roughly 40% larger in the high-expectations tercile than in the low-expectations tercile, conditional on the authors’ controls and sample. The authors interpret this as evidence that expectations are not merely a level shift in inflation but alter the sensitivity of prices to real activity, consistent with forward-looking pricing theories.

Q2. What is the mechanism, and how do the authors identify it?

The authors argue that expectations work through the frequency of price review: firms expecting higher inflation are more likely to be in an active review window, and so respond more to a given demand shock within that window. Identification relies on cross-firm variation in survey-measured expectations within narrow industry-time cells, so that aggregate demand shocks are held approximately fixed. The authors acknowledge this strategy absorbs industry-specific inflation trends and may understate the full expectational effect.

Q3. What does this imply for monetary policy?

If the Phillips curve slope varies with expectations, then a credible disinflation — by lowering expected inflation — flattens the curve and makes the output cost of reducing inflation larger, not smaller. The authors present this as a potential mechanism behind the observed flattening of the curve in low-inflation regimes, though they stop short of a structural welfare calculation.

Key concepts

Phillips curve slope: The coefficient linking excess demand (or unemployment gap) to inflation in the short-run Phillips curve — steeper means a given demand shortfall has a larger disinflationary effect.
price review frequency: How often a firm actively reconsiders its prices; firms that review more often are more likely to adjust in response to new information within any given period.
firm-level survey expectations: Inflation expectations measured directly from firms (rather than households or markets), which may better capture the beliefs that drive actual price-setting decisions.

Misspecified Expectations among Professional Forecasters

Mon, 01 Jan 0001 00:00:00 +0000

Analyzing panel data from the U.S. Survey of Professional Forecasters (SPF, 1992Q1–2019Q4, 77 forecasters, 1,520 forecaster-quarter observations), Julio Ortiz finds that a “misspecified expectations” model — in which forecasters perceive an AR(2) data-generating process to be an AR(1), causing them to misperceive its underlying persistence — tends to outperform a noisy-information rational benchmark and two leading non-FIRE alternatives (overconfident and diagnostic expectations) when fit to forecast errors and revisions. The models are estimated by maximum likelihood and ranked using forecast-encompassing weights; for the baseline real GDP growth case, misspecified expectations earns the largest encompassing weight (0.539 vs. 0.462 for diagnostic, ~0 for rational and overconfident) and the highest log-likelihood. Across 14 macroeconomic variables, misspecified expectations provides the best fit for most series both in-sample and out-of-sample, though diagnostic expectations fits better for some (e.g., GDP deflator, industrial production, real residential investment) and rational expectations fits the unemployment rate best. The author argues misspecified expectations succeeds in part because its bias enters both the prediction and updating equations, producing overreaction to new information plus overextrapolation across horizons, which makes forecast errors longer-lived; he concludes it can serve as a “suitable approach” / useful benchmark to model professional-forecaster expectation formation, while emphasizing the results are specific to the context of professional forecasting and may not carry over to household or firm expectations.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What question does the paper address?

The paper undertakes a formal comparison of competing non-FIRE theories of expectation formation to move toward establishing a benchmark non-FIRE model in the context of professional forecasting. Ortiz motivates this with the observation that survey forecast errors are predictably correlated with real-time information — a violation of full-information rational expectations (FIRE) — but that, as noted in Reis (2020), the literature “has not yet settled on a benchmark non-FIRE model.” The paper offers “a partial answer to this question.”

Q2. What models are compared?

Four models are estimated: a noisy-information rational expectations baseline plus three biased non-FIRE models — overconfident expectations (Daniel et al., 1998), diagnostic expectations (Bordalo et al., 2020), and misspecified expectations (in the spirit of Fuster et al., 2010). All are embedded in a common noisy-information environment where the latent variable is unobservable and forecasters update via a Kalman filter from a noisy private signal. Overconfidence has forecasters misperceive their signal noise as smaller than it is; diagnostic expectations introduces a representativeness distortion ϕ > 0 generating overreaction to recent news; misspecified expectations has forecasters treat an AR(2) process as an AR(1).

Q3. What exactly is “misspecified expectations” in this paper?

Misspecified expectations is a model in which the underlying state follows an AR(2) process but forecasters treat it as an AR(1), so they misperceive the true persistence of the data-generating process. The author notes this version is “closest to natural expectations as modeled in Fuster et al. (2010),” with forecasters neglecting longer lags. Importantly, forecasters still understand the information structure. If the perceived persistence loads excessively onto the first lag, forecasters overextrapolate. The author flags three technical differences from Fuster et al. (2010): he does not model an AR(2) in levels with AR(1)-in-growth-rates forecasting; the perceived persistence is estimated from the data rather than defined as a function of the true autocorrelation parameters; and he does not define expectations as a weighted average of rational and naive AR(1) expectations.

Q4. What data and sample are used?

The estimation uses U.S. SPF panel data from 1992Q1 to 2019Q4, yielding 77 unique forecasters and 1,520 forecaster-quarter observations for the baseline. The 1992 start is chosen to avoid spanning different regimes and because the survey redefined output from GNP to GDP in 1992. The procedure requires unbroken observation sequences, so only each forecaster’s longest spell is kept, with a minimum spell length of eight quarters (because entry/exit may be non-random, per Engelberg et al., 2011). Real GDP growth is the baseline variable; 13 other macroeconomic variables are also estimated. Real-time forecast errors (not errors based on revised figures) are used, following the literature.

Q5. How are the models estimated and compared?

The models are estimated via a three-step maximum likelihood procedure, and their relative fit is compared using forecast-encompassing weights (West, 2001; Harvey et al., 1998; West, 2006), supplemented by AIC and a Vuong (1989) non-nested likelihood-ratio test. Step 1 estimates the fundamental process parameters (ρ₁, ρ₂, σ_w) from the macro time series and fixes them across models; step 2 estimates the signal-noise dispersion σ_v from the rational model and calibrates it across the other three; step 3 estimates each bias parameter (α_v, ϕ, ρ̂) by MLE on SPF data. This keeps fundamental and information parameters consistent across biased models so they are evaluated solely on the biases they generate, and makes identification transparent (notably, σ_v and α_v cannot be jointly identified in the overconfidence model). Encompassing weights are obtained from a constrained linear regression of realizations on model-based one-quarter-ahead forecasts, with weights summing to 1.

Q6. What are the baseline real GDP growth results?

For real GDP growth, the misspecified expectations model produces the highest log-likelihood and the largest encompassing weight, 0.539, versus 0.462 for diagnostic expectations and approximately 0.000 for both rational and overconfident expectations. The fundamental process estimates imply relatively low persistence (first-order autocorrelation ρ₁ ≈ 0.434, second-order ρ₂ ≈ −0.006). The estimated bias parameters are: overconfidence ≈ 0.72, diagnosticity ≈ 0.23, and perceived persistence ρ̂ ≈ 0.564. Because ρ̂ ≈ 0.56 exceeds the estimated ρ₁ ≈ 0.43, the misspecified model implies forecasters overestimate the first-order autocorrelation and neglect the partial reversal in the second lag, generating overreactions. The signal-to-noise ratio implied by the estimated private noise dispersion is σ_w/σ_v ≈ 1.09. AIC rankings (and BIC) do not change the ordering relative to the maximized likelihoods.

Q7. Does the result hold across other macroeconomic variables?

Across the 14 SPF macroeconomic variables, misspecified expectations provides the best in-sample fit for most series, but not all. Diagnostic expectations registers larger encompassing weights for certain series — the GDP deflator (0.771), industrial production (1.000), and real residential investment (0.624). Rational expectations provides the best fit for the unemployment rate (0.745) and housing starts (in-sample). For the bulk of the remaining variables (e.g., CPI 0.859, payroll employment 1.000, real consumption 0.777, real federal spending 1.000, real GDP 0.539, real nonresidential investment 1.000, real state/local spending 1.000, 3-month Treasury bill 0.713, 10-year bond 0.746), misspecified expectations carries the largest weight. Overconfident expectations “does not yield particularly large encompassing weights for any variable.”

Q8. Why does misspecified expectations fit better, and for which variables especially?

The author finds that, among variables exhibiting overreactions, misspecified expectations tends to offer a better fit for less persistent series, because the scope for it to generate overreaction (ρ̂ − ρ₁) is greater when ρ₁ is low. Unlike the alternatives, the persistence bias ρ̂ − ρ₁ can be positive or negative, allowing the model to account for both overreacting and underreacting variables; the alternative models cannot generate forecaster-level underreaction. Figure 2 plots the encompassing weight on misspecified expectations against the sum of autoregressive coefficients and suggests (with some exceptions) that less persistent variables have higher weight on misspecified expectations.

Q9. Does the model perform out of sample?

The misspecified expectations model also provides a better out-of-sample fit for more of the variables, estimated on 1992Q1–2005Q4 and evaluated on the latter half of the sample. However, out of sample diagnostic expectations now outperforms for the GDP deflator (0.987), industrial production (0.959), payroll employment (0.813), and real federal government expenditures (0.591); overconfident expectations outperforms for the 10-year government bond (0.653); and rational expectations outperforms for housing starts (0.502) and the unemployment rate (1.000). The author cautions that these results do not imply forecasters could improve their forecasts in real time, because the MLE observations include contemporaneous individual and consensus forecast errors that are not known to forecasters when they issue forecasts; for the same reason, the results are “not inconsistent with” Eva and Winkler (2023) on the poor out-of-sample performance of error-predictability regressions.

Q10. Could the apparent advantage of misspecified expectations just reflect learning?

The author argues that learning about the data-generating process does not appear to drive the relative model rankings in favor of misspecified expectations, based on two exercises. First, using the full pre-COVID sample (1968Q4–2019Q4) over 25-year rolling windows (three-year roll), the misspecified model outperforms diagnostic expectations in six of ten sub-samples and all models in five of ten, while diagnostic expectations wins four of ten — patterns that “do not indicate that learning over time favors misspecified expectations.” Second, splitting forecasters by “age”/tenure (a proxy for experience), misspecified expectations outperforms the others among experienced (above-median age) forecasters (encompassing weight 0.766, with overconfidence 0.234) and is dominant among inexperienced ones (1.000). The author concedes learning “is likely reflected in professional forecasts” but does not appear to drive the rankings.

Q11. What additional moments does misspecified expectations match?

Beyond overall fit, the author shows in the appendix that misspecified expectations matches five features of the data — overreaction, underreaction, overshooting, persistent disagreement, and updating behavior — and is the only model generating delayed overshooting. All three non-rational models generate individual-level overreaction (Bordalo et al., 2020 errors-on-revisions regression) and aggregate underreaction (Coibion-Gorodnichenko, 2015 consensus regression). But when simulating impulse responses, “only the misspecified expectations model generates a sign switch in the forecast error,” indicating delayed overshooting (Angeletos et al., 2020). The author reports “stronger evidence” favoring misspecified expectations on two further moments: it better generates persistent disagreement across horizons, and it better matches the relative weights forecasters place on priors versus news — because its bias also enters the prediction equation (not just the update equation), producing longer-lived errors.

Q12. What are the scope conditions and limitations the author stresses?

The author emphasizes that the results are specific to the context of professional forecasting and that the relative model rankings “may be different” for household or firm expectations, or for micro-level expectations rather than aggregate forecasts. He notes professional forecasters are arguably the most well-informed agents, so the literature has treated their predictions as informative about a lower bound on economy-wide information frictions and biases. The paper abstracts away from learning in the model setup and from theories that generate only underreaction. Models excluded from the comparison (e.g., imperfect memory, multi-frequency forecasting, asymmetric attention, learning) are set aside mainly because they cannot be flexibly nested into the common setting and would introduce additional parameters posing identification challenges.

Ortiz concludes that misspecified expectations “can serve as a suitable approach” / useful benchmark to model expectation formation among professional forecasters for a variety of macroeconomic aggregates, while framing this as only “a partial answer” to the search for a non-FIRE benchmark. He highlights a practical advantage: embedding this form of misspecified expectations into a quantitative model “only requires introducing two parameters into an otherwise standard model.” He also notes misspecification can arise either from a behavioral bias or because adopting parsimonious forecasting models is optimal (Branch and Evans, 2006; Pfajfar, 2013). A promising avenue for future research is whether evidence favors misspecified expectations in other settings.

Key concepts

Full-information rational expectations (FIRE): The benchmark in which forecast errors are uncorrelated with any information in the forecaster’s time-t information set; the orthogonality conditions it implies “tend to be violated in the data,” motivating non-FIRE models.
Misspecified expectations: The paper’s focal bias — the true state follows an AR(2) process, xₜ = ρ₁xₜ₋₁ + ρ₂xₜ₋₂ + wₜ, but forecasters treat it as an AR(1), xₜ = ρ̂xₜ₋₁ + uₜ, misperceiving its persistence; forecasters retain the correct information structure. The bias enters both the predict and update equations.
Persistence bias (ρ̂ − ρ₁): The gap between perceived AR(1) persistence and true first-order autocorrelation; positive values generate overextrapolation/overreaction, negative values generate underreaction, and its overreaction scope is larger when ρ₁ is low.
Overconfident expectations: Forecasters misperceive their private signal noise as smaller (σ̃_v = α_v σ_v, α_v ∈ [0,1]) than it truly is, placing excessive weight on new private information.
Diagnostic expectations: A representativeness-based distortion (Bordalo et al., 2020; Gennaioli-Shleifer, 2010) in which, with diagnosticity ϕ > 0, forecasters overweight outcomes representative relative to a “no news” reference scenario, generating overreaction to recent news.
Encompassing weight: The model-comparison metric — a weight wₖ from a constrained linear regression of realized one-quarter-ahead values on competing models’ forecasts, with weights summing to one; a larger weight indicates a better-fitting model.
Delayed overshooting: The Angeletos et al. (2020) pattern of initial underreaction followed by later overreaction to a shock; in this paper, only misspecified expectations produces the sign switch in the forecast-error impulse response that signals it.
Overreaction vs. underreaction: Individual-level overreaction is measured via the Bordalo et al. (2020) errors-on-revisions regression; aggregate/consensus-level underreaction via the Coibion-Gorodnichenko (2015) regression — the data exhibit both, and a successful non-FIRE model must reproduce both.

Monetary Policy and the Drifting Natural Rate of Interest

Mon, 01 Jan 0001 00:00:00 +0000

This paper analyzes how monetary policy should respond to a long-run natural interest rate that can drift permanently — following a bounded random walk with upper bound 3 percent and lower bound 0 percent — when the zero lower bound (ZLB) on nominal interest rates is a binding constraint. The central result is that the long-run neutral rate (the real policy rate consistent with stable inflation in long-run equilibrium) should fall more than one-for-one with the long-run natural rate as the latter approaches zero, because the mere risk of future ZLB episodes — even when the economy is currently away from the ZLB — imparts a persistent downward bias on inflation expectations that can only be offset by maintaining a pre-emptive expansionary bias. Quantitatively, the model implies that the neutral rate should be zero as soon as the long-run natural rate falls to 75 basis points — well above the near-zero estimates prevailing in the late 2010s — and that the ZLB would bind one-third of the time under optimal policy when the natural rate fluctuates between 0 and 3 percent. Price level targeting with a 10-basis-point upward drift closely approximates optimal commitment policy and has the advantage of not requiring knowledge of the natural rate level.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What empirical fact motivates the model?

Empirical analyses of the long-run natural rate — the real interest rate prevailing over a long-run equilibrium in which nominal rigidities are absent — consistently find that it is time-varying in a manner best described by a random walk, meaning it can drift without reverting to a constant long-run level. The paper cites Holston, Laubach, and Williams (2017), Fiorentini et al. (2018), and Hamilton et al. (2016) as the main empirical references. Holston et al. (2017) place the long-run natural rate at between 0 and 1 percent in the U.S. and possibly slightly negative in the euro area as of 2016. The paper draws one central lesson: because the natural rate is time-varying and its future level is uncertain, a model with constant natural rate will give unreliable guidance for monetary policy, especially at low natural rate levels near zero.

Q2. What is the model and what are the key equilibrium concepts?

The paper embeds a new Keynesian model in which the long-run natural rate follows a bounded random walk with upper bound 3 percent and lower bound 0 percent, calibrated to post-WWII U.S. TFP data, and studies optimal monetary policy under commitment while imposing the zero lower bound. A critical distinction separates two notions of the long-run equilibrium interest rate: the “long-run natural rate” (denoted ¯r) is the real rate that would prevail in flexible-price equilibrium, determined by fundamentals outside the central bank’s control; the “neutral rate” (r*) is the real policy rate consistent with stable inflation in the long run, which the central bank operationally targets. The two coincide in standard models with constant ¯r, but diverge in this paper because ZLB risk drives a wedge between them.

Q3. What is the main theoretical result?

Under optimal commitment, the neutral rate r should fall more than one-for-one with the long-run natural rate ¯r — that is, the central bank should maintain a negative gap (r < ¯r) that widens as ¯r falls toward zero — because permanent downward movements in ¯r make future ZLB binding episodes permanently more likely, creating a persistent downward bias on inflation expectations that requires pre-emptive accommodation even in periods when the ZLB is not currently binding.** This result contrasts with the existing literature on optimal commitment at the ZLB, which has emphasized forward guidance — the promise to maintain low rates even after the economy recovers from a ZLB episode — as the primary stabilization tool. The paper shows that forward guidance alone is not sufficient when ¯r can permanently drift lower, because each downward drift permanently raises the probability of future ZLB episodes, reducing the central bank’s scope for fulfilling future inflation promises.

Q4. What are the quantitative implications?

The model implies that the neutral rate r reaches zero when the long-run natural rate ¯r is at 75 basis points — a level that was well above the near-zero estimates of ¯r prevailing at the end of the 2010s — and that the ZLB binds one-third of the time under optimal policy when ¯r fluctuates between 0 and 3 percent.* The 75 basis-point threshold means that a central bank operating in an environment where ¯r has declined to its estimated late-2010s levels would already be constrained to a neutral rate of zero under optimal policy. The one-third ZLB frequency is higher than what would be predicted by models with constant ¯r at typical calibrations, reflecting the permanent nature of ¯r shocks and their cumulative effect on the neutral rate.

Q5. What do the adjustment dynamics look like after a negative ¯r shock?

Following a permanent reduction in ¯r, the real policy rate adjusts gradually rather than immediately — remaining temporarily above the new long-run neutral rate during the transition — implying that monetary policy is contractionary along the adjustment path and that a permanent decline in ¯r is followed by a temporary disinflation before the economy settles at the new r.* This history-dependence of optimal commitment policy means the central bank does not immediately jump to the new, lower r* after a ¯r shock; it moves gradually, making the short-run policy stance more contractionary than the long-run position. The temporary disinflation is consistent with the general principle of history-dependence of optimal policy under commitment.

Q6. What role does price level targeting play?

Price level targeting variants — particularly a rule with an optimally chosen upward drift of 10 basis points — closely approximate the economic outcomes achieved under optimal commitment policy in the model, with the practical advantage that such rules do not require the central bank to know or estimate the current level of the long-run natural rate ¯r. The Eggertsson-Woodford (2003) price level target works well in models with constant ¯r by generating positive inflation expectations in the wake of deflationary ZLB episodes. Adding a small upward drift of 10 basis points strengthens this property under a drifting ¯r, because it provides additional buffer against the downward expectations bias that permanent ¯r drift generates. Under price level targeting rules, the neutral rate reaches the ZLB as soon as ¯r falls below 1 percent.

Key concepts

long-run natural rate (¯r) : the real interest rate prevailing over a long-run equilibrium in which nominal rigidities are absent; in this paper modelled as a bounded random walk with upper bound 3 percent and lower bound 0 percent, calibrated to post-WWII TFP data.

neutral rate (r)* : the real policy rate consistent with stable inflation in the long run; distinct from ¯r in this paper because ZLB risk drives a negative gap (r* < ¯r) that widens as ¯r approaches zero.

zero lower bound (ZLB) : the constraint that nominal policy rates cannot fall below zero; in this model the reason that permanent reductions in ¯r create a persistent downward bias on inflation expectations even when the ZLB is not currently binding.

expansionary bias : the paper’s finding that optimal commitment policy should maintain r* < ¯r — a pre-emptive accommodation away from the ZLB — to offset the downward bias on inflation expectations created by the risk of future ZLB episodes.

price level targeting : a monetary policy rule in which the central bank targets the price level rather than the inflation rate; shown in this paper to approximate optimal commitment policy and to have the practical advantage of not requiring knowledge of ¯r.

Optimal monetary policy with uncertain private sector foresight

Mon, 01 Jan 0001 00:00:00 +0000

Central banks must set policy under uncertainty about how private-sector expectations form, which changes how monetary policy transmits to output and inflation. This paper studies optimal time-consistent monetary policy using a New Keynesian finite-horizon planning (NK-FHP) model in which households and firms have limited foresight: they solve structural problems only up to a finite horizon, and update their beliefs about longer-run inflation by averaging over past data. In this setting—unlike in standard New Keynesian models—an “inflation scares” problem can arise: agents’ longer-run inflation expectations can deviate persistently from the central bank’s target, generating costly and prolonged disinflations. The authors formally characterize optimal policy when the planning horizons of private-sector agents are uncertain and a risk of inflation scares is present, showing that risk-management considerations modify the standard “leaning against the wind” principle with a novel preemptive motive: the optimal policy responds more aggressively to the risk of unanchoring to prevent inflation scares from materializing. An estimated version of the model is used to quantify how much this preemptive motive mattered during the post-pandemic inflation surge.

Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.

In depth

Q1. What is the NK-FHP model and how does it differ from standard New Keynesian models?

In the NK-FHP model, households and firms are boundedly rational because they evaluate state-contingent paths only up to a finite horizon; beyond that horizon they extrapolate longer-run beliefs by averaging over past data, making those beliefs adaptive rather than anchored at the target. When agents have long planning horizons, the model approximates rational expectations: inflation expectations are well anchored, disinflations are relatively costless, and policy transmits quickly. When planning horizons are short, longer-run inflation expectations can become unanchored, disinflations are costly, and policy transmission lags lengthen. The NK-FHP model thus nests both extremes and provides micro-foundations for the “inflation scares” discussed by Goodfriend (1993).

Q2. What is the “inflation scares problem” and why does it matter for optimal policy?

An inflation scare occurs when agents’ longer-run inflation expectations deviate persistently from the central bank’s target because agents with short planning horizons update beliefs adaptively, and past high inflation feeds forward into current expectations. This creates a welfare-relevant asymmetry: once expectations become unanchored, a disinflation is costly in output because the central bank must build credibility against backward-looking expectations. The standard NK model with rational expectations does not generate this problem—rational agents’ inflation expectations are pinned to the target irrespective of history—so it cannot address the design of policy specifically to prevent scares from materializing.

Q3. What is the “preemptive motive” and how does it modify the leaning-against-the-wind principle?

Optimal time-consistent policy under uncertain private-sector foresight adds a preemptive motive to the standard leaning-against-the-wind (LATW) principle: the central bank contracts demand not just in response to current output-gap and inflation deviations, but also to prevent expectations from becoming unanchored. Under the standard NK LATW result (Clarida, Galí, and Gertler 1999), the policymaker responds to the means of output and inflation. Under uncertain and potentially short-horizon foresight, optimal policy also depends on the distribution of output and inflation, as well as agents’ beliefs about future inflation—specifically, whether those beliefs risk drifting away from target. The preemptive motive implies a more aggressive policy response to the risk of an inflation scare even before the scare has fully materialized.

Q4. How does the paper relate to the post-pandemic inflation experience?

Using parameter estimates from an estimated version of the NK-FHP model, the paper applies the optimal policy framework to quantify how much the preemptive motive matters during the recent post-pandemic inflation surge. The model—which has been shown in related work (Gust, Herbst, and López-Salido 2022, 2024) to fit macroeconomic time series substantially better than hybrid NK models and to account for initial underreaction and subsequent overreaction of inflation forecasts—is well suited to analyze an episode where longer-run inflation expectations initially remained anchored but later showed signs of drift. The paper’s quantification indicates that risk-management considerations, including the preemptive motive, significantly affect the optimal policy path.

Key concepts

finite-horizon planning (NK-FHP) : a bounded-rationality framework (Woodford 2018) in which agents evaluate only those state-contingent paths within a finite planning horizon, updating beliefs about events beyond the horizon adaptively from past data.

inflation scares : episodes (Goodfriend 1993) in which agents’ longer-run inflation expectations deviate persistently from the central bank’s target, making disinflation costly; the NK-FHP model provides micro-foundations for such scares.

preemptive motive : the additional incentive for a policymaker to tighten beyond what current output-gap and inflation deviations alone would prescribe, specifically to prevent longer-run inflation expectations from becoming unanchored.

time-consistent policy under uncertainty : optimal policy that does not rely on commitment and hence accounts for future re-optimization; in this model it must also account for the non-additive uncertainty arising from a distribution of planning horizons.

Inflation-Expectations | Macro Paper Warehouse

Aggregate Implications of Heterogeneous Inflation Expectations: The Role of Individual Experience

In depth

What are the four empirical patterns in the survey data, and do they hold outside the US?

How does the experience-based Kalman filter work, and what does estimation yield?

What is the general equilibrium model, and how do heterogeneous expectations enter the IS curve?

What do the impulse responses show under experience-based versus FIRE expectations?

How does the optimal Taylor rule change under experience-based expectations?

What does the model predict about the 2021 high-inflation episode?

Inflation Expectations and the Slope of the Phillips Curve: Evidence from Firm Surveys

In depth

Q1. What is the main empirical finding on expectations and the Phillips curve slope?

Q2. What is the mechanism, and how do the authors identify it?

Q3. What does this imply for monetary policy?

Key concepts

Misspecified Expectations among Professional Forecasters

In depth

Q1. What question does the paper address?

Q2. What models are compared?

Q3. What exactly is “misspecified expectations” in this paper?

Q4. What data and sample are used?

Q5. How are the models estimated and compared?

Q6. What are the baseline real GDP growth results?

Q7. Does the result hold across other macroeconomic variables?

Q8. Why does misspecified expectations fit better, and for which variables especially?

Q9. Does the model perform out of sample?

Q10. Could the apparent advantage of misspecified expectations just reflect learning?

Q11. What additional moments does misspecified expectations match?

Q12. What are the scope conditions and limitations the author stresses?

Q13. What does the author conclude and recommend?

Key concepts

Monetary Policy and the Drifting Natural Rate of Interest

In depth

Q1. What empirical fact motivates the model?

Q2. What is the model and what are the key equilibrium concepts?

Q3. What is the main theoretical result?

Q4. What are the quantitative implications?

Q5. What do the adjustment dynamics look like after a negative ¯r shock?

Q6. What role does price level targeting play?

Key concepts

Optimal monetary policy with uncertain private sector foresight

In depth

Q1. What is the NK-FHP model and how does it differ from standard New Keynesian models?

Q2. What is the “inflation scares problem” and why does it matter for optimal policy?

Q3. What is the “preemptive motive” and how does it modify the leaning-against-the-wind principle?

Q4. How does the paper relate to the post-pandemic inflation experience?

Key concepts