Alternative Explanation for the Fed Information Effect: Bauer & Swanson (2023)

What this is. The paper’s core results, the model (imperfect information about the Fed’s policy rule), and the method (OLS with economic news controls plus high-frequency financial-market evidence): enough to see what it found and how, without reading all 37 pages. To replicate or extend, read the full source at the original and the replication package.

TL;DR

Bauer and Swanson (2023) challenge the “Fed information effect” (FIE): the finding that monetary policy tightenings are associated with upward revisions in private-sector GDP and employment forecasts, which prior literature interpreted as the Fed revealing positive private information about the economy. They make four main arguments. First, economic news released in the weeks between the Blue Chip survey and the FOMC announcement is an important omitted variable in standard FIE regressions. Second, once that news is controlled for (regressions with full news vectors), the monetary policy surprise coefficients reverse sign and become consistent with standard macroeconomic theory: hawkish surprises reduce GDP forecasts and raise unemployment forecasts. Third, their direct survey of all 52 Blue Chip forecasters (Section III) confirms that forecasters do not revise in the information-effect direction. Fourth, high-frequency stock market and exchange rate responses to FOMC announcements are equally negative for the “most influential” FIE announcements as for all other announcements, with no sign of a positive information channel. The alternative explanation, which they call the “Fed response to news” channel, is that both the Fed and private forecasters respond to the same public economic data, but the Fed responded more strongly than markets anticipated, generating a spurious positive correlation in simple regressions.

Core results

Magnitudes and significance as reported. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Standard FIE regressions of Blue Chip forecast revisions on monetary policy surprises have very low R² and fragile coefficients with signs opposite to standard macro theory	Table 1, pp. 672-674	R² = 0.00-0.06 across unemployment, GDP, and CPI inflation and across samples (N=120, 129, 206, 217); estimates sensitive to sample period and variable forecast
R2	Economic news strongly predicts Blue Chip forecast revisions, confirming the omitted variable	Table 2, p. 677	R² = 0.64 (unemployment), 0.40 (GDP), 0.31 (CPI inflation); unemployment surprise coefficient = 0.308 (se=0.037); S&P500 change coefficient on GDP = 0.620 (se=0.167)
R3	Economic news predicts high-frequency monetary policy surprises, establishing omitted variable bias in FIE regressions	Table 3, pp. 679-680	R² = 0.12-0.20 across target, path, and NS surprise measures; S&P500 coefficient \approx 0.15 across all three MPS measures
R4	Controlling for economic news eliminates the FIE: MPS coefficients reverse to conventional signs and R² rises to 31-65%	Table 4, p. 682	Target factor on GDP = -0.241 (se=0.145); target on unemployment = +0.152 (se=0.074); target on inflation = +0.067 (se=0.088); R² rises from 0-6% to 31-65%
R5	Survey of 52 Blue Chip forecasters: no respondent revises GDP forecast upward after a hawkish surprise; 18 of 23 who revise do so conventionally	Table 5, pp. 683-686	0/36 upward GDP revision after hawkish surprise; 18/36 downward; 13/36 do not revise at all in response to the funds rate decision
R6	High-frequency stock market and exchange rate responses to FOMC announcements are equally negative for the ten “most influential” FIE observations	Table 7, p. 689	S&P500: -8.04 (se=1.91) for top-10 FIE observations vs. -7.14 (se=1.84) for remaining 110 obs; difference not significant
R7	Fed Greenbook and Blue Chip forecast RMSEs are essentially identical; no systematic Fed information advantage	Table 8, pp. 690-692	Unemployment 0-3Q avg RMSE: GB=0.42, BC=0.42; GDP 0-3Q avg: GB=1.64, BC=1.60; Diebold-Mariano p-values mostly above 0.05

Overall (paper’s conclusion). The response of Blue Chip macroeconomic forecast revisions to FOMC announcements can be fully explained by omitted economic news variables, without invoking the Fed information effect. Once news is controlled for, the monetary policy surprise coefficients become consistent with standard macro models and VARs. The “Fed response to news” channel, in which both the Fed and private forecasters respond to the same publicly available economic data, explains all the empirical patterns.

Theory / model

Section V (pp. 692-696) presents a simple partial-equilibrium model with imperfect information about the Fed’s policy rule. It contains no Fed information effect by construction and illustrates the “Fed response to news” channel.

Output gap follows an exogenous AR(1) (eq. 10, p. 693):

$$x_t = \rho_x x_{t-1} + \eta_t, \qquad \eta_t \sim i.i.d.\, N(0, \sigma_\eta^2), \quad \rho_x \in [0,1)$$

Monetary policy rule: the central bank sets the interest rate linearly in the output gap (eq. 11, p. 693):

$$i_t = a x_t + \varepsilon_t, \qquad \varepsilon_t \sim i.i.d.\, N(0, \sigma_\varepsilon^2)$$

The parameter $a > 0$ denotes the central bank’s responsiveness to the output gap and is known to the central bank but NOT to the private sector. The private sector maintains prior beliefs $a \sim N(\hat{a}_t, \sigma^2_{a_t})$ updated from history $\mathcal{H}_{t-1} = \{i_s, x_s, i_{s-1}, x_{s-1}, \ldots\}$. The core uncertainty is about $a$, not about $x_t$ (which is observed by all).

Expected future rates (after observing $x_t$ but before the FOMC announcement; eq. 12, p. 694):

$$E[i_{t+j} | x_t, \mathcal{H}_{t-1}] = \hat{a}_t \rho_x^j x_t$$

Monetary policy surprise (the gap between announced $i_t$ and prior expectation; eq. 13, p. 694):

$$mps_t \equiv i_t - E[i_t | x_t, \mathcal{H}_{t-1}] = (a - \hat{a}_t) x_t + \varepsilon_t$$

This shows that $mps_t$ is driven both by the pure exogenous shock $\varepsilon_t$ and by the private sector’s uncertainty about $a$, captured by $(a - \hat{a}_t) x_t$. If markets have persistently underestimated $a$ (i.e., $\hat{a}_t < a$), then $mps_t$ will be positively correlated with $x_t$, exactly as found empirically in Tables 2 and 3. Cieslak (2018) provides direct evidence that financial markets systematically underestimated the Fed’s responsiveness to the economy over this period, consistent with $\hat{a}_t < a$ persisting for many periods.

Bayesian belief update (eq. 14, p. 694): after observing $mps_t$, the private sector updates:

$$E[a | \mathcal{H}_t] = \hat{a}_t + \omega_t \frac{1}{x_t} mps_t, \qquad \omega_t \equiv \frac{x_t^2 \sigma^2_{a_t}}{x_t^2 \sigma^2_{a_t} + \sigma^2_\varepsilon}$$

Interest rate forecast revision (eq. 15, p. 694): the updated belief implies a forecast path revision of:

$$E[i_{t+j} | \mathcal{H}_t] - E[i_{t+j} | x_t, \mathcal{H}_{t-1}] = \rho_x^j \omega_t \, mps_t$$

So interest rate path revisions are a positive, horizon-declining function of $mps_t$, replicating the empirical pattern of Gurkaynak, Sack, and Swanson (2005b) without any private Fed information.

Implication for identification. Even though $mps_t$ may be correlated with $x_t$ ex post, it can still be used without adjustment to estimate the causal effects of the exogenous monetary policy shock $\varepsilon_t$ on asset prices in narrow-window event-study regressions, because interest rate expectations respond only to $mps_t$ and not separately to $\varepsilon_t$ (p. 695). However, using $mps_t$ as an instrument in structural VARs or local projections is problematic because the exogeneity condition is violated: $mps_t$ is correlated ex post with structural shocks to $x_t$ (p. 695-696).

Method

The paper applies three empirical designs. This section states the estimating equations; the identifying assumptions are discussed in the Empirical specifications section. The method builds on panel-regression and event-study.

Design 1: Blue Chip forecast revision regressions (replication and extension, eqs. 2-3, pp. 672-673). Following Campbell et al. (2012) (henceforth CEFJ):

$$\text{BCrev}_t = \alpha + \beta \, \text{target}_t + \gamma \, \text{path}_t + \varepsilon_t \tag{2}$$

where $\text{BCrev}_t$ is the one-month revision in the Blue Chip consensus forecast, averaged over the 1-, 2-, and 3-quarter-ahead horizons; $\text{target}_t$ and $\text{path}_t$ are GSS high-frequency factors computed from short-maturity federal funds and Eurodollar futures in a 30-minute window around the FOMC announcement. The Nakamura and Steinsson (2018) variant uses a single composite surprise $\text{mps}_t$ (first principal component):

$$\text{BCrev}_t = \phi + \theta \, \text{mps}_t + \eta_t \tag{3}$$

Standard errors are bootstrapped (50,000 replications) to account for the generated-regressor nature of the GSS factors.

Design 2: Economic news controls (eqs. 4-6, p. 676). The corrected specification adds a news vector $\text{news}_t$:

$$\text{BCrev}_t = \alpha + \beta \, \text{target}_t + \gamma \, \text{path}_t + \delta'\text{news}_t + \varepsilon_t \tag{4}$$

and analogously for the NS version. $\text{news}_t$ includes: unemployment surprise, payrolls surprise, GDP surprise, BBK composite business-cycle index, lagged core CPI measures, core CPI surprise, log change in the S&P500, change in yield curve slope, and log change in commodity prices, all pre-dating the FOMC announcement. Economic news also predicts the monetary policy surprises themselves (eq. 7, p. 679):

$$mps_t = \alpha + \beta'\text{news}_t + \varepsilon_t \tag{7}$$

Design 3: Financial market event study (eq. 8, p. 688). High-frequency stock and exchange rate regressions:

$$\Delta \log x_t = \phi + \theta \, \text{mps}_t + \eta_t \tag{8}$$

run separately over the ten most influential FIE observations and the remaining observations.

Design 4: Greenbook encompassing regressions (eq. 9, p. 691). Following Romer and Romer (2000):

$$X_{t+h} = \alpha + \beta \hat{X}^{GB}_{t+h|t} + \gamma \hat{X}^{BC}_{t+h|t} + \varepsilon_{t+h} \tag{9}$$

where $X_{t+h}$ is the realized macro variable, $\hat{X}^{GB}$ and $\hat{X}^{BC}$ are the Greenbook and Blue Chip forecasts. A coefficient test asks whether either forecast dominates. Hansen-Hodrick standard errors with $2(h+1)$ lags for overlapping horizons.

Empirical specifications

Section I (Table 1, R1). Regressions (2) and (3) are run on four samples: CEFJ replication sample (1990-2007, N=129); NS replication sample (1995-2014, N=120); full sample including unscheduled FOMC announcements (1990-2019, N=217); full sample excluding unscheduled (N=206). Three outcomes: unemployment, GDP, CPI inflation. Identification rests on the assumption that the 30-minute monetary policy surprise window is exogenous to monthly forecast revision determinants; the paper shows this assumption is violated by the economic news omitted variable.

Section II (Tables 2-4, R2-R4). Full sample, N=217. Table 2 runs eq. (6) with $\text{BCrev}$ as outcome; confirms news has R² of 31-64% for forecast revisions. Table 3 runs eq. (7) with MPS as outcome; confirms news has R² of 12-20% for monetary policy surprises. Table 4 runs eqs. (4) and (5) simultaneously, adding all news controls. Identifying assumption: conditional on economic news, the residual variation in the FOMC surprise is uncorrelated with other determinants of forecast revisions (selection on observables). Standard errors bootstrapped (50,000 reps) throughout.

Section III (Table 5, R5). Original survey of 52 Blue Chip Economic Indicators forecasting firms, conducted July-August 2019; 36 responses (70% response rate). Each firm was asked how it revises its GDP, unemployment, and CPI forecasts in response to four components of FOMC announcements: (i) the funds rate decision, (ii) the FOMC statement, (iii) the dot plot, and (iv) the SEP forecasts. Survey answers are self-reported and directional (up/down/no change), not quantitative.

Section IV (Tables 6-8, R6-R7). The ten “most influential” FIE observations in the NS regression are identified by the change in the regression t-statistic when that observation is excluded. Regression (8) is run separately for these ten and the remaining 110 NS observations (heteroskedasticity-consistent standard errors). Greenbook comparison (eq. 9) uses 1990-2013 (N=192 observations matched on timing), horizons h=0,1,2,3 quarters, plus the 0-3 quarter average.

Datasets used

Dataset	Role in paper	Wiki page
Blue Chip Economic Indicators survey (Wolters Kluwer)	Monthly consensus forecasts of GDP growth, unemployment, CPI inflation used as $\text{BCrev}_t$ dependent variable; monthly revisions in Sections I-III	Blue Chip Financial Forecasts (licensed)
GSS monetary policy surprise factors (Gurkaynak, Sack, Swanson 2005b)	Target and path surprise factors from federal funds and Eurodollar futures in 30-minute FOMC windows; also NS first-principal-component surprise	No page yet
Money Market Services survey	Market expectations of upcoming BLS/BEA data releases; used to compute the “surprise” component of each macro release	No page yet
BLS employment report, BEA GDP release, BLS CPI release	Unemployment surprise, payrolls surprise, GDP surprise, core CPI surprise (components of $\text{news}_t$)	No page yet
BBK composite business cycle index (Brave, Butters, Kelley 2019)	Comprehensive single monthly business activity index; included in $\text{news}_t$	No page yet
S&P500, USD/EUR exchange rate, commodity price index	Financial news controls (lagged in $\text{news}_t$) and event-study outcomes in Section IV	No page yet
Federal Reserve Greenbook forecasts	Fed’s internal forecasts of unemployment, GDP, CPI; compared against Blue Chip accuracy in Section IV.B; public after 5-year lag, available through Dec 2013	No page yet
Authors’ own survey of Blue Chip forecasters (July 2019)	Hand-collected survey of 52 professional forecasting firms on how they revise forecasts in response to FOMC announcements; Section III; published in the replication package	No page yet

Sample: monthly FOMC announcement months, January 1990 to June 2019 (N=217 including unscheduled; N=206 excluding). NS subsample: January 1995 to March 2014 (N=120). CEFJ subsample: January 1990 to June 2007 (N=129). Greenbook comparison: 1990-2013 (N=192).

When to read the full paper

Read the original if you are: (i) using high-frequency monetary policy surprises as instruments in a structural VAR or local projections framework, where the paper’s recommendation to purge the “Fed response to news” component before using them as instruments is directly relevant (Section V, pp. 695-696 and the forthcoming companion paper); (ii) studying whether FOMC announcements transmit private central-bank information to private forecasters (the core question); (iii) replicating or extending the Nakamura and Steinsson (2018) or Campbell et al. (2012) results (exact sample construction details and bootstrap procedure are in Sections I-II and online appendices); or (iv) comparing Fed Greenbook and Blue Chip forecast accuracy (Section IV.B, Table 8 panel structure). The replication package at ICPSR contains the data and code.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(3), March 2023. AEA copyright; freely accessible on AEAweb.org past the 3-year embargo (elapsed March 2026). No Creative Commons licence; extract-only. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced.

Bauer, Michael D., and Eric T. Swanson. “An Alternative Explanation for the ‘Fed Information Effect’.” American Economic Review 113, no. 3 (March 2023): 664-700. DOI: 10.1257/aer.20201220. Copyright 2023 American Economic Association.