C55 | Macro Paper Warehouse

Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements

Thu, 01 Jan 2026 00:00:00 +0000

This paper proposes a text-based measure of monetary policy stance by modelling FOMC post-meeting statements as convex combinations of the staff-drafted dovish (“alternative A”) and hawkish (“alternative C/D”) versions that accompany each meeting, providing a transparent and adaptive reference spectrum. The authors fine-tune the Universal Sentence Encoder—a pre-trained language model—using synthetic examples that mirror numerical information in policy actions, enabling the model to capture both semantic tone and quantitative precision. Stance is defined as the product of tone (alignment with the dovish/hawkish alternatives) and novelty (semantic shift from the previous statement), and is decomposed into expected and surprise components using intraday financial data. Surprises arise from shifts in tone relative to market expectations or from statement novelty. The resulting surprise measure aligns closely (correlations of 70–80%) with established high-frequency measures (Swanson 2017, Nakamura-Steinsson 2018, Bauer-Swanson 2023), and the framework enables counterfactual analysis of how alternative communication could have moved markets.

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 are the alternative FOMC statements and how are they used?

For each FOMC meeting, staff draft multiple versions of the policy statement—typically a more dovish “alternative A,” a baseline “alternative B,” and a more hawkish “alternative C” or “D”—and the paper uses these pre-structured alternatives as a reference spectrum against which to position the released statement. This institutional feature provides a transparent, adaptive measure of tone that evolves with the policy environment and internal deliberations, avoiding the rigidity of pre-fixed tone definitions. The released statement’s embedding in the language model space is compared to the dovish and hawkish alternatives to determine its location on the policy spectrum.

Q2. How is the policy stance measure constructed?

Stance is defined as the product of novelty and tone: novelty captures semantic shifts from previous statements, and tone reflects the alignment of the released statement with the dovish or hawkish alternatives; the observed stance reflects both the content of each position and the relative positioning of the Committee along the policy spectrum. A second, structural interpretation models the released statement as the outcome of internal deliberation—a weighted average of dovish and hawkish stances—linking textual variation to shifts in the internal balance of influence within the Committee.

Q3. How is the stance decomposed into expected and surprise components?

The decomposition into expected and surprise components uses intraday bond price movements to recover the market-expected dovish weight of the released statement, then defines the surprise as the deviation between the realized stance and the market-expected stance. Surprises arise from two sources: deviations in tone relative to expectations, and statement novelty. This framework shows that monetary policy surprises are not just about what the Fed did but also about how it communicated—capturing interpretable surprises that reveal shifts in the Committee’s internal balance.

Q4. How is the measure validated and what are its macroeconomic effects?

The surprise measure aligns closely with established high-frequency measures (correlations of 70–80% with Swanson 2017, Nakamura-Steinsson 2018, and Bauer-Swanson 2023); surprise tightenings reduce stock prices, raise short-term Treasury yields, dampen real activity and inflation, and raise credit risk premia. Local projection estimates corroborate that surprise contractionary shocks have the expected macroeconomic effects, providing a validation that the text-based measure captures meaningful monetary policy information beyond what is already priced in.

Q5. What counterfactual analysis does the framework enable?

The framework enables counterfactual analysis of how alternative FOMC communication could have moved markets—for example, estimating what asset price movements would have occurred had the Committee released the more dovish or hawkish alternative statement rather than the actual release. This counterfactual capability stems from the explicit modelling of stance as a position on a spectrum defined by the staff-drafted alternatives, so the market impact of any point on that spectrum can be estimated.

Key concepts

alternative FOMC statements : staff-drafted dovish (“alternative A”) and hawkish (“alternative C/D”) versions of the FOMC post-meeting statement prepared for each meeting; used as the reference spectrum for measuring the tone and position of the released statement. monetary policy stance : as defined in this paper, the product of tone (alignment with the dovish/hawkish alternatives) and novelty (semantic shift from the previous statement); captures both the direction and the information content of the released statement. tone : the alignment of a released FOMC statement with the dovish or hawkish alternative drafts in the Universal Sentence Encoder embedding space; reflects the direction of the Committee’s communication along the policy spectrum. novelty : the semantic distance of the released FOMC statement from the previous statement in the embedding space; captures how much new information or emphasis the statement introduces. Universal Sentence Encoder (USE) : the pre-trained language model applied by the paper; fine-tuned on synthetic examples that mirror numerical information in policy actions (e.g., rate-hike sizes) to capture both semantic tone and quantitative policy precision.

Financial shocks and leverage of financial institutions: When do they matter?

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

This paper investigates the role of leverage of financial institutions in amplifying the transmission of financial shocks to the macroeconomy, with particular attention to whether that amplification differs across economic regimes. The authors develop a new endogenous regime-switching structural vector autoregression (RS-SVAR) model with time-varying transition probabilities, in which the probability of switching regime depends on the contemporaneous state of the economy (endogenous switching). The model extends the Sims and Zha (2006) and Sims, Waggoner, and Zha (2008) Markov-switching SVAR framework by: (1) incorporating a time-varying transition matrix in which the probability of staying in a regime is a logistic function of lagged endogenous variables; and (2) introducing new identification techniques for RS-SVARs, including non-recursive zero restrictions, sign restrictions, and narrative sign restrictions, which can in some cases uniquely identify structural shocks rather than merely set-identify them.

The leverage measure is market-based — book assets divided by market equity — constructed from CRSP/Compustat institution-level data covering publicly listed depository institutions, bank holding companies, and nonbank financial institutions. The sample runs monthly from December 1988 to December 2019. The five-variable VAR includes industrial production growth, core CPI inflation, the 2-year Treasury rate, market leverage of financial institutions, and the Chicago Fed’s National Financial Conditions Index (NFCI). The authors estimate three model variants that substitute in turn the leverage of: (i) all depository institutions, (ii) Global Systemically Important Banks (GSIBs), and (iii) securities brokers and dealers.

The model identifies two coefficient regimes — a “financial constraint” regime and “normal times” — using the criterion that the first regime has higher smoothed probability during September 2008 to August 2009. The financial constraint regime covers the end of the Savings and Loan crisis, the 1990/91 recession, the Russian debt default, the Global Financial Crisis (GFC), and the European sovereign debt crisis.

The core finding is that real effects of financial shocks are amplified in the financial constraint regime but not in normal times. In the financial constraint regime, the output response to a financial shock is significantly negative, large, and protracted; GSIB leverage initially rises sharply (as falling asset prices erode equity) and then declines as institutions deleverage. In normal times, the output growth response is negative but non-persistent, and market leverage remains insignificant over the entire horizon.

The counterfactual experiment holding GSIB market leverage constant as of October 2008 is the sharpest quantitative result: if GSIB leverage had not risen further at the onset of the GFC, the decline in industrial production growth would have been approximately 20 percentage points smaller, with a faster subsequent recovery in output growth and inflation and higher short-term interest rates. The counterfactual probability of staying in the financial constraint regime would have fallen as low as 0.1 for some draws, compared to the actual probability remaining elevated. By contrast, for a system using depository institution leverage, the lower-bound counterfactual probability of staying in the constraint regime does not fall below 0.90, indicating substantially weaker heterogeneity effects for the broader depository sector.

Securities brokers and dealers show leverage that rises more on impact than other institutions and then declines immediately, consistent with their willingness to expand balance sheets going into the crisis amplifying losses and forcing a sharp post-crisis contraction.

A separate counterfactual holding the NFCI constant (rather than leverage) shows that the probability of staying in the constraint regime does not decline, confirming that market leverage and the financial conditions index provide distinct characterizations of the financial system and have different implications for shock propagation and regime persistence. Results are robust to substituting the GZ corporate spread for the NFCI and to imposing narrative restrictions for shock identification.

Q: What is the central research question? A: The paper asks whether and how the leverage of financial institutions amplifies the transmission of financial shocks to the real economy, and whether this amplification differs between a financial constraint regime and normal times. A secondary question concerns heterogeneity: do GSIBs, depository institutions broadly, and nonbank securities dealers transmit shocks differently?

Q: What is novel about the econometric framework? A: The RS-SVAR model allows the probability of remaining in a given coefficient regime to vary over time as a logistic function of lagged endogenous variables, so regime switching is endogenous to the state of the economy rather than governed by a fixed transition matrix. The paper also introduces sign restrictions, zero restrictions, and narrative sign restrictions into the RS-SVAR class, enabling identification of both structural shocks and regimes within a single framework; in roughly 20 percent of posterior draws these sign restrictions uniquely identify the financial shock.

Q: Why does the paper use market leverage rather than book leverage? A: Market leverage (book assets divided by market equity) is argued to be more timely than book leverage because book equity incorporates losses with a delay, giving institutions time to adjust book leverage to avoid regulatory limits. Market capitalization reflects market participants’ assessment of an institution’s creditworthiness, and low market-to-book ratios signal that institutions are more leveraged than their books indicate. Market leverage is therefore a more informative early-warning indicator of financial fragility and the need for rapid deleveraging.

Q: How are the two regimes identified? A: For each estimated regime, the authors count the number of months between September 2008 and August 2009 (inclusive) for which the smoothed probability of being in that regime exceeds 0.70; the regime with the higher count is labeled “financial constraint” and ordered first. Shock identification uses sign restrictions: in the financial constraint regime, a positive financial shock must have a contemporaneously negative effect on output, inflation, and the short-term interest rate, but positive effects on the financial conditions index and leverage; in normal times, only the financial conditions index is required to respond positively on impact.

Q: What regimes does the model assign historically? A: The smoothed probability of the financial constraint regime is elevated during the end of the Savings and Loan crisis, the 1990/91 recession, the Russian debt default, the GFC and associated recession (where the probability reaches 1.0 at end-2008 and beginning-2009 before declining sharply to approximately 0.6 percent in 2009/2010), and the European sovereign debt crisis.

Q: What do the impulse responses show in the financial constraint regime? A: In the financial constraint regime, the output response to a positive financial shock (tightening) is significantly negative, large, and protracted. GSIB leverage initially rises due to a sharp decline in asset prices eroding market equity, then falls as GSIBs deleverage in response. The authors interpret this pattern as evidence that deleveraging produces procyclical financial amplification effects with adverse real consequences.

Q: What do the impulse responses show in normal times? A: In normal times, the output growth response is large and negative but non-persistent, in contrast to the financial constraint regime. Market leverage remains statistically insignificant across the entire horizon in normal times, indicating that the leverage amplification channel is inactive outside of financial constraint episodes.

Q: What does the GSIB leverage counterfactual show quantitatively? A: Holding GSIB market leverage constant as of October 2008 implies a decline in industrial production growth that is approximately 20 percentage points smaller than actually occurred, along with a faster recovery in output growth and inflation and higher short-term interest rates. The counterfactual probability of staying in the financial constraint regime declines to as low as 0.1 for some posterior draws, compared to remaining elevated in the actual data.

Q: How do depository institutions compare to GSIBs in the counterfactual? A: For the model using broad depository institution leverage, the lower-bound counterfactual probability of staying in the financial constraint regime does not fall below 0.90, compared to as low as 0.1 for the GSIB specification. This implies that GSIB deleveraging has substantially more detrimental macroeconomic effects and a much larger effect on regime persistence than the broader depository sector.

Q: What is distinctive about securities brokers and dealers? A: Broker-dealer market leverage rises more on impact than leverage of other financial institutions following a financial shock, and then immediately declines due to rapid deleveraging. The authors interpret this as reflecting that dealers’ willingness to expand balance sheets ahead of the crisis amplified growth and losses, followed by a sharp post-crisis contraction — a pattern consistent with the procyclical leverage mechanism described in Adrian and Shin (2014).

Q: How do the authors distinguish the role of market leverage from the financial conditions index? A: A counterfactual holding the NFCI constant (rather than leverage) as of October 2008 shows that the probability of staying in the financial constraint regime does not decline, unlike the leverage counterfactual. This demonstrates that market leverage and the NFCI provide distinct characterizations of financial conditions and have different implications for the propagation of shocks and the persistence of the constraint regime.

Q: How robust are the results? A: Substituting the GZ corporate bond spread for the NFCI yields very similar results, specifically that the probability of staying in the constraint regime declines much more in the counterfactual than in the actual data, suggesting the findings are not driven by the choice of financial conditions proxy. Imposing narrative restrictions for shock identification (exploiting the known high-stress period around Lehman’s failure in September 2008) yields results that are “rather robust” relative to the baseline sign-restriction identification.

Q: What are the policy implications? A: The results confirm the leverage ratio as a useful financial stability indicator, with particular emphasis on market leverage as providing timely information for monitoring. The heterogeneity findings suggest that regulatory attention to GSIB leverage is especially warranted, since GSIB deleveraging can have substantially more detrimental macroeconomic effects and a much larger influence on the persistence of financial constraint regimes than deleveraging by the broader depository sector. The leverage ratio is characterized as complementary to the risk-weighted capital ratio as a regulatory tool.

Market leverage: Measured as book assets divided by market equity (not book equity), constructed from CRSP/Compustat institution-level data at monthly frequency. The paper argues market leverage is more timely than book leverage because market equity immediately reflects losses, preventing institutions from masking fragility through delayed book adjustments.

Financial constraint regime: One of two identified coefficient regimes in the RS-SVAR, characterized by a significantly negative, large, and protracted output response to financial shocks and by active leverage amplification. Identified empirically as the regime with the highest smoothed probability during September 2008 to August 2009.

Endogenous regime switching: A modeling approach in which the probability of transitioning between regimes depends on lagged values of the endogenous variables themselves (via a logistic function), rather than being governed by a fixed constant transition matrix. This allows regime dynamics to respond to the state of the economy.

Time-varying transition probabilities: The diagonal elements of the coefficient-regime transition matrix follow a logistic transformation of a linear function of lagged endogenous variables, so the probability of remaining in any given regime changes each period as a function of current financial and macroeconomic conditions.

Procyclical financial amplification: The mechanism by which financial institution deleveraging in response to falling asset prices further tightens financial conditions and reduces real output, generating a feedback loop. The paper provides empirical evidence for this channel operating specifically in financial constraint regimes.

Heterogeneity of financial institutions: The finding that GSIBs, broad depository institutions, and securities brokers and dealers differ substantially in how their leverage affects the transmission of financial shocks. GSIB deleveraging is shown to have much more detrimental macroeconomic effects and a much larger influence on the probability of remaining in the financial constraint regime than depository institution deleveraging more broadly.

Narrative sign restrictions in RS-SVARs: An identification technique extended from Antolin-Diaz and Rubio-Ramirez (2018) to the regime-switching context, which uses known historical episodes (here, the Lehman failure in September 2008) to impose restrictions on which regime the economy was in or on the sign of structural shocks at particular dates, thereby aiding identification of both shocks and regimes.

Mixing It Up: Inflation at Risk

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

This paper introduces a Bayesian Gaussian mixture density regression framework that estimates the complete forecast distribution of inflation — not just selected quantiles — and decomposes the entire risk outlook into contributions from individual economic predictors. The methodology accommodates multimodality, skewness, and fat tails without parametric restrictions, and allows construction of risk measures calibrated to the central bank’s own loss function rather than generic percentile-based measures. Applied to the recent U.S. inflation surge, the framework finds that post-pandemic inflation risk was primarily driven by the recovery of the U.S. business cycle and surging commodity prices, while adjustments in monetary policy contributed negatively — partially mitigating the increase in right-tail inflation risk — and credit spreads also offset some risk. The Gaussian mixture structure enables fast MCMC estimation and produces well-calibrated density forecasts across a range of macroeconomic variables.

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 key methodological contribution relative to existing inflation-at-risk approaches?

Existing approaches to macroeconomic at-risk measures focus on specific quantiles of the forecast distribution — typically the 5th or 25th percentile — discarding information contained in the rest of the distribution; this paper redirects attention to the full forecast distribution while retaining the nonparametric flexibility of quantile regression. The Gaussian mixture density regression estimates a conditional distribution that is a weighted mixture of Gaussians, capturing multimodality, asymmetry, and fat tails simultaneously. The key innovation is decomposability: each predictor’s contribution to any region of the forecast distribution can be quantified, enabling a driver-level accounting of what generates tail risk in any given period.

Q2. What does the U.S. application reveal about the inflation surge?

The framework attributes the increase in right-tail U.S. inflation risk during 2021–2023 primarily to surging commodity prices and the recovery of the domestic business cycle, while monetary policy tightening contributed negatively — its effect partially offset the upward pressure from commodity and cycle drivers. Credit spreads also partially mitigated the risk. The decomposition implies that the dominant drivers of inflation risk were supply-side and aggregate-demand factors, and that monetary policy, when it tightened, reduced the right-tail risk as intended — providing quantitative support for the interpretation that policy was reactive but directionally correct.

Q3. How does the framework construct policy-relevant risk measures?

The framework allows weighting probability mass over the forecast distribution by any user-specified loss function, including asymmetric central bank preferences, yielding risk measures that integrate the full distributional information in proportion to the policymaker’s actual valuation of different inflation outcomes. A central bank that penalizes above-target inflation more heavily than below-target inflation (consistent with empirical evidence on CB loss functions) would weight the upper tail more, producing a risk statistic that is higher than a symmetric measure for the same distribution. This policy-preference-aligned risk measure could have provided a more accurate signal of the urgency of the 2021–2023 inflation risk than standard percentile measures.

Key concepts

inflation at risk : the quantile-based or distribution-based characterization of future inflation uncertainty; extended in this paper from a single quantile to the complete forecast distribution and its risk decomposition by driver.

density regression : a regression model in which the conditional distribution of the outcome — not just its mean or a specific quantile — is the object of estimation; the paper uses a Gaussian mixture density regression to capture non-standard distributional shapes.

risk decomposition : the attribution of shifts in the full forecast distribution to individual predictor variables; the paper’s key tool for identifying which economic factors drive right-tail inflation risk in any period.

CB-preference-aligned risk measure : a summary statistic constructed by weighting probability mass over the forecast distribution by the central bank’s loss function; captures asymmetric preferences and goes beyond standard percentile measures.