US monetary policy is more powerful in low economic growth regimes.
Date: 2024-03-22
Institutions: European Central Bank
Subjects: United States, economic cycle, economic growth, economic model, monetary policy

Roberto A. De Santis, Tommaso Tornese 

Working Paper Series 

US monetary policy is more powerful 
in low economic growth regimes 

No 2919 

Disclaimer: This paper should not be reported as representing the views of the European Central Bank 
(ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. 

Abstract

We use nonlinear empirical methods to uncover non-linearities in the propagation

of monetary policy shocks. We find that the transmission on output, goods prices and

asset prices is stronger in a low growth regime, contrary to the findings of Tenreyro

and Thwaites (2016). The impact is stronger on private investment and durables and

milder on the consumption of nondurable goods and services. In periods of low growth,

a contractionary monetary policy implies lower expected Treasury rates and higher

premia along the entire Treasury yield curve. Similarly, the corporate excess bond

premium rises and the stock market drops substantially during recessions. We use the

monetary policy surprises and their predictors provided by Bauer and Swanson (2023a),

and identify an additional predictor, the National Financial Condition Index (NFCI),

which is relevant in the nonlinear setting. A Threshold VAR, a Smooth-Transition

VAR and nonlinear local projection methods all corroborate the findings.

Keywords: Asset prices, business cycles, monetary policy, non-linearities, TVAR,

STVAR, local projections JEL Classification: C32, E32

ECB Working Paper Series No 29191Non-technical summary

Is the transmission mechanism of the monetary policy on asset prices, output and goods

prices state dependent? Specifically, is there a differential effect of monetary policy depending

upon the position of the state in the business cycle? Should the policymaker force the

economy to expand below a certain threshold to be effective? We investigate whether the

efficacy of monetary policy depends upon the state of the real economy using a new available

set of monetary policy surprises computed by Bauer and Swanson (2023a) through high-

frequency identification; that is, focusing on interest rate surprises in a narrow window of

time around Federal Reserve’s Federal Open Market Committee (FOMC) announcements.

First, we remove the components of the monetary policy surprises that is predictable

with publicly available macroeconomic and financial market information in the nonlinear

setting. Then, we estimate a Bayesian Threshold Vector Autoregression (TVAR) model

and a Bayesian Smooth Transition Vector Autoregression (STVAR) model to uncover non-

linearities in the propagation of monetary policy shocks. Specifically, we investigate the state

dependence of monetary policy impulse response functions on a range of real, nominal and

asset price variables.

We show that monetary policy shocks are more powerful in the low growth regime, with

the negative response on output accompanied by a negative response on goods prices in a

textbook fashion; in the high growth regime, instead, the response of goods prices is muted.

Essentially, the policy makers cannot steer goods prices using traditional tools when the

economy is growing above the underlying real trend, but can still affect output growth.

Monetary policy is effective on goods prices when the real economy is weak and growing

below the underlying trend. This suggests that the FOMC should reduce output growth

below a certain threshold, before an additional monetary policy shock is effective on goods

prices. Across sub-components of real GDP, the impact is larger on private investment and

durable goods than on nondurables and services consumption, consistent with the fact that

spending on durables can be postponed in response to adverse shocks.

These findings are relevant for the design of stabilization policies.

If changes in the

monetary policy rates have limited impact on inflation in periods of expansions, central

banks may be required to cause a recession to be able to steer the inflation rate towards the

ECB Working Paper Series No 29192desired target.

With regard to asset prices, the response of expected Treasuries is positive in expansions

but negative in recessions along the entire yield curve. In periods of low economic growth, a

contractionary monetary policy is judged by markets as further conducive to lower nominal

economic growth and therefore a decline in the short term interest rates is expected by

investors in the medium- to the long-term. The response of the term premium is instead

countercyclical along the entire yield curve. It is strongly positive in the low growth regime

and mildly negative in the high growth regime. The impact on the term premia is largest for

Treasuries at longer maturity, after 16 month horizon and in the low growth environment.

Similarly, the component of corporate credit spreads not associated to credit risk (e.g. the

excess bond premium of Gilchrist and Zakrajˇsek (2012)) rises immediately in response to

monetary policy shocks in the low growth regime, while it is muted in the high growth

regime. All in all, bond holders demand higher premia for holding sovereign and corporate

bonds in recessions. As for stock prices, they decline in response to a monetary policy shock

and the drop is twice as deeper during recessions.

These results are broadly consistent with theoretical models assuming information asym-

metries in financial markets. During recessions, asset prices drop, business contracts, the

net worth of agents declines, the external finance premium rises and finance constraints are

more likely to bind. The financial accelerator amplifies the effects of a tightening in monetary

policy.

ECB Working Paper Series No 29193I

Introduction

Is the transmission mechanism of the monetary policy on asset prices, output and goods

prices state dependent? Specifically, is there a differential effect of monetary policy depending

upon the position of the state in the business cycle? Should the policymaker force the

economy to expand below a certain threshold to be effective? We investigate whether the

efficacy of monetary policy depends upon the state of the real economy using a new available

set of monetary policy surprises computed by Bauer and Swanson (2023a) through high-

frequency identification; that is, focusing on interest rate surprises in a narrow window of

time around Federal Reserve’s Federal Open Market Committee (FOMC) announcements.

As suggested by recent studies (Cieslak, 2018; Miranda-Agrippino and Ricco, 2021; Bauer

and Swanson, 2023a,b), monetary policy surprises are predictable with publicly available

macroeconomic and financial market information.1 Therefore, we remove the components of

the monetary policy surprises that is predictable following the recommendations in Bauer and

Swanson (2023a), but in a nonlinear setting. In particular, we regress the monetary policy

surprises on the economic and financial variables that predate the FOMC announcements,

allowing for possible nonlinearities in the relations, and take the residuals.

In addition

to the six variables suggested by Bauer and Swanson (2023a), we also propose a seventh

variable, the National Financial Condition Index (NFCI), available in real time (Amburgey

and McCracken, 2023) and relevant in the nonlinear setting. The NFCI was identified by

(Adrian et al., 2019) as a strong explanatory variable of the level of risk in economic activity.

Equipped with the unpredictable monetary policy surprises derived from the nonlinear

regression, we investigate the state dependence of monetary policy impulse response func-

tions on a range of real, nominal and asset price variables. We show that monetary policy

shocks are more powerful in the low growth regime, with the negative response on output ac-

companied by a negative response on goods prices in a textbook fashion; in the high growth

regime, instead, the response of goods prices is muted. Essentially, the policy makers cannot

1In Bauer and Swanson (2023b), the Fed’s responsiveness to the economy is both time-varying and
unobserved by the private sector, and the private sector must form beliefs about this parameter. Therefore,
monetary policy surprises are due not only to exogenous monetary policy shocks, but also to imperfect
information about the Fed’s response parameter. As a consequence, monetary policy surprises can be
correlated with economic variables observed prior to the policy announcements (i.e. “Fed response to news”
channel). A precondition for this effect is that the public systematically underestimated how strongly the
Fed would respond to economic news. Recent evidence supports this view (e.g. Schmeling et al., 2022).

ECB Working Paper Series No 29194steer goods prices using traditional tools when the economy is growing above the underlying

real trend, but can still affect output. Monetary policy is effective on goods prices when the

real economy is weak and growing below the underlying trend. This suggests that the FOMC

should reduce output growth below a certain threshold, before an additional monetary policy

shock is effective on goods prices. Across sub-components of real GDP, the impact is larger

on private investment and durable goods than on nondurables and services consumption,

consistent with the fact that spending on durables can be postponed in response to adverse

shocks (Browning and Crossley, 2009).

With regard to asset prices, the response of the expected component of Treasuries is

positive in expansions but negative in recessions along the entire yield curve.

In periods

of low economic growth, a contractionary monetary policy is judged by markets as further

conducive to lower nominal economic growth and therefore a decline in the short term interest

rates is expected by investors in the medium- to the long-term. The response of the term

premium component is instead countercyclical along the entire yield curve. It is strongly

positive in the low growth regime and mildly negative in the high growth regime. The impact

on the term premia is largest for Treasuries at longer maturity, after 16 month horizon and

in the low growth environment. Similarly, the component of corporate credit spreads not

associated to credit risk (i.e. the excess bond premium of Gilchrist and Zakrajˇsek (2012))

rises immediately in response to monetary policy shocks in the low growth regime, while it is

muted in the high growth regime. All in all, bond holders demand higher premia for holding

sovereign and corporate bonds in low growth regimes. As for stock prices, they decline in

response to a monetary policy shock and the drop is twice as deep during recessions.

The empirical literature on how monetary policy transmits in different phases of the

business cycle is rather small. To our knowledge, the most recent published paper for the US

is the study by Tenreyro and Thwaites (2016), who estimate the responses using the local

projection model of Jord`a (2005), combined with the smooth-transition regression method of

Granger and Ter¨asvirta (1993) and with shocks identified in the manner of Romer and Romer

(2004).2 Contrary to our findings, their results indicate that the effects of US monetary policy

2Angrist et al. (2018) evaluate the effect of monetary policy contractions and expansions on macroeco-
nomic outcomes proposing a propensity score method. They find that target rate increases reduce employ-
ment and industrial output, and somewhat less successfully, inflation. At the same time, target decreases
appear to have little stimulative effect on output or inflation. We instead condition the effects of the monetary

ECB Working Paper Series No 29195shocks are less powerful in recession. In this state of the economy, surprisingly, they found

that a contractionary monetary policy shock even increases aggregate output, real business

investment and real consumption in the first year. Moreover, the negative permanent effect

on goods price inflation in the linear model, during expansions and during recessions, is

striking. That would imply that goods prices unceasingly decline. Instead, theory suggests

that after a drop in goods prices, they stabilise to a lower level, which implies that the

response of inflation should revert to zero in the long run. Our findings are more in line with

textbook economic principles.

There is one main issue that undermines the reliability of Tenreyro and Thwaites (2016)’s

evidence. The method that Romer and Romer (2004) employs to identify the monetary

policy shocks used by Tenreyro and Thwaites (2016) consists of running a regression of

the change in the policy rate on central bank’s forecasts, motivated by an empirical Taylor

rule. Importantly, Miranda-Agrippino and Ricco (2021) show that such residuals are not

exogenous. They are autocorrelated and can be predicted using past information.3 We show

that the difference between Tenreyro and Thwaites (2016)’s results and the findings in our

paper is primarily driven by the approach used to identify the monetary policy shocks. A

key feature of a VAR is that it includes the lagged value of all regressors, which allows to

control for autocorrelation in monetary policy surprises and past information intrinsic in

all regressors. Interestingly, we detected that by employing the database of Tenreyro and

Thwaites (2016), including the Romer and Romer (2004)’s monetary policy shocks, a Smooth

Transition Vector Autoregressive model (STVAR), which closely resembles their model in a

VAR context, cannot replicate their results. The same conclusions can be drawn by using a

Threshold Vector Autoregression (TVAR), after having consistently re-estimated the Romer

and Romer (2004)’s monetary policy surprises. Instead, we can replicate qualitatively our

findings with our estimated nonlinear monetary policy shocks using both TVAR and STVAR

models as well as smooth-transition and threshold local projection methods.4

policy on the state of the business cycle.

3Using quantile regressions, Mumtaz and Surico (2015) study the relationship between real consumption
growth and real interest rates, instrumented with Romer and Romer (2004)’s monetary policy shocks. They
also find that the effects are stronger when consumption growth is above its conditional average.

4It is preferable to extract the response functions from nonlinear VARs rather nonlinear local projec-
tion methods. Gon¸calves et al. (2024) show that, when the state of the economy is endogenous, the local
projections’ estimator of the response functions, applied by Tenreyro and Thwaites (2016), tends to be
asymptotically biased except for the impact response. A similar critique applies to the study of Alpanda

ECB Working Paper Series No 29196A recent working paper by Bruns and Piffer (2021), which makes use of a STVAR,

finds similar results to ours. However, their identification of the monetary policy shocks is

obtained by using as an external instrument the high frequency monetary policy surprises of

Gertler and Karadi (2015) and Jaroci´nski and Karadi (2020), which are autocorrelated (see

Miranda-Agrippino and Ricco, 2021). Moreover, their identification is obtained including

sign restrictions at impact on all financial variables and sign restrictions within six months

on real GDP and goods prices. Instead, the response of all variables in our model is always

left unrestricted. This allows us to be completely agnostic about the impact of the monetary

policy shocks on asset prices and the macroeconomy.5

Several other earlier studies make use of old-fashioned approaches to identify monetary

policy shocks, often based on assumptions that are difficult to maintain in light of more

recent advancements in the empirical and theoretical literature. Weise (1999) identifies

money supply shocks using the monetary aggregate M1 through a Choleski orthogonalization

ordering money last. The regime is indicated by the first lag of quarterly GDP growth and

therefore subject to high-frequency shifts. The results are difficult to interpret because

expansionary monetary shocks are contractionary in a high-growth regime. In other words,

monetary policy decisions would achieve exactly the opposite of the desired results. Thoma

(1994) also estimates a nonlinear VAR in output and monetary variables. Focusing on the

VAR coefficients, he finds that the relationship between M1 and output becomes stronger,

when real activity experience a cyclical decline, while it becomes weaker, when an upswing

in real activity occurs. In general, monetary aggregates have lost their appeal in signaling

monetary policy surprises.

Garcia and Schaller (2002) studies the response of industrial production growth to mon-

etary policy in a VAR for the United States from 1955:II to 1993:I with a two-state Markov

switching regime. They find that interest rate changes or monetary policy shocks identified

using a Choleski orthogonalization have a larger negative impact on output in recessions

et al. (2021), who use local projections in a panel setting with 18 advanced economies and monetary policy
shocks identified using sign restrictions on interest rates, output and prices. Francis et al. (2023) corroborate
the results that nonlinear local projections are biased and, in addition, find that state-dependent IRFs from
STVARs are typically more biased than those obtained from a TVAR, given the larger number of parameters
to estimate.

5A recent BIS paper provides a view of the inflation characterised by a low and a high inflation regime
(Borio et al., 2023). The study documents the stylised facts describing the two regimes and the transitions
between them based on disaggregated price dynamics and the joint behaviour of wages and prices.

ECB Working Paper Series No 29197than expansions. However, it is assumed that the persistence of GDP is the same in booms

and recessions, which can be an overly restrictive assumption (Acemoglu and Scott, 1997).

Peersman and Smets (2001) and Lo and Piger (2005) also study the asymmetric response

of industrial production growth to monetary policy respectively in seven Euro-area countries

and US, using a traditional recursive VAR where the policy variable is ordered after output,

and find that the impact is more negative during recessions than boom.

Chen (2007) studies the impact on stock returns with a two-state Markov switching

regime and finds that monetary policy has larger effects on stock returns in bear markets.

However, monetary policy is measured by interest rate instruments, which are endogenous,

and innovations to the Fed fund rate estimated from a linear recursive VAR model, thereby

generating an inconsistency given the nonlinear framework.6

Burgard et al. (2019) estimate a logit mixture VAR model to assess the effects of monetary

policy shocks in the euro area with monetary policy shocks identified using policy rates and

Choleski identification. They show that monetary policy transmission in the euro area can

be described as a mixture of two states. In both states, output and prices decrease after

monetary policy shocks. During crisis times, the contraction is much stronger, as the peak

effect of both variables is roughly one-and-a-half times as large when compared to normal

times. The key issue is that this type of models requires the estimation of time-varying

state weights and, therefore, the model should be very parsimonious. Burgard et al. (2019)

estimate their four variable nonlinear VAR with one lag only. In addition, the states depend

on the included variables and requires an interpretation.

All in all, Weise (1999), Garcia and Schaller (2002), Lo and Piger (2005), Burgard et al.

(2019) and Bruns and Piffer (2021) find that US monetary policy is more effective during

recessions than during expansions, while Thoma (1994) and Tenreyro and Thwaites (2016)

find the opposite results as in their papers monetary policy in the United States is less

powerful during recessions. However, all these studies can be challenged because of the

method used to identify the monetary policy shocks.

We revisit the analysis (i) by employing a Bayesian TVAR and a Beyesian STVAR, which

6Perez-Quiros and Timmermann (2000) study the asymmetric effects of short-term interest rates and
monetary growth on returns of size-sorted decile portfolios across recession and expansion states. However,
they do not identify monetary policy shocks.

ECB Working Paper Series No 29198define the states of interests and allow to compute state-dependent impulse response func-

tions, as well as nonlinear local projection methods, and (ii) by using the recently computed

monetary policy surprises by Bauer and Swanson (2023a) through the high-frequency identi-

fication, orthogonalized exploiting macroeconomic and financial data that are publicly avail-

able prior to the monetary policy announcement. Over the past two decades, high-frequency

identification of monetary policy surprises has become an important tool for identifying the

effects of monetary policy in a linear framework on asset prices (Kuttner, 2001; Bernanke

and Kuttner, 2005; G¨urkaynak et al., 2005; Hanson and Stein, 2015; Altavilla et al., 2019;

Jaroci´nski and Karadi, 2020; Swanson, 2021; Bauer and Swanson, 2023a,b) and the macroe-

conomy (Cochrane and Piazzesi, 2002; Faust et al., 2003, 2004; Gertler and Karadi, 2015;

Ramey, 2016; Stock and Watson, 2018; Jaroci´nski and Karadi, 2020; Bauer and Swanson,

2023b).

The paper is structured as follows. Section II presents the model. Section III describes the

method and the variables used to identify the monetary policy shocks. Section IV discusses

the key results. Section V explains the reasons behind the differences with Tenreyro and

Thwaites (2016). Section VI provides a number of robustness checks. Section VII concludes.

II Framework

II.A Model specification

Our baseline model is a TVAR. The results obtained with the STVAR and the nonlinear

local projections are provided in Section VI. The reduced form TVAR takes the following

form

Xt = (cl + Πl(L)Xt−1)Ft{zt−1 < z∗} + (ch + Πh(L)Xt−1)(1 − Ft){zt−1 ≥ z∗} + uT
t ,

(1)

uT

t ∼ N (0, Ωt),

Ωt = Ωl(Ft){zt−1 < z∗} + Ωh(1 − Ft){zt−1 ≥ z∗},

(2)

(3)

ECB Working Paper Series No 29199where uT

t denotes the n × 1 vector of reduced form residuals, Ωt the state-contingent covari-

ance matrix of the residuals, zt the state variable, z∗ = Pk(zt) the kth percentile of zt, cl and

ch the vector of intercepts in the two regimes (low (l) and high (h) growth, S ∈ {l, h}) and

Πl and Πh the lag polynomials. The regime switches are governed by the indicator function

Ft ∈ {0, 1} and are indexed by t − 1 to avoid endogeneity problems. A regime shift occurs

only if zt remains in the new regime for at least two periods. By doing so, we avoid high

frequency shifts when zt is close to z∗.

The vector Xt = [uT −mp

t

, rt, ien

t , itpn

t

, bt, et, yt, pt]′ defines the endogenous variables of the

baseline model, where uT −mp

t

denotes the orthogonolized monetary policy surprises, rt the

policy shadow rate, ien

t and itpn

t

are the expected Treasury rate and the correspondent term

premium at maturity n, respectively, bt the corporate excess bond premium, et the stock

market price, yt output and pt goods prices. The variables et, yt and pt are defined in log

levels. By construction, the 10-year Treasury rate in

t = ien

t + itpn

t

. Therefore, we can recover

the impulse response functions (IRFs) for the nominal Treasury yield.

We set the lag order p to 12. This helps estimating a trend-stationary process. As for

linear VARs, the stability condition of a TVAR requires that all the roots, r, of Πl and Πh

|ΠS(r)| = |In − ΠS,1r − ΠS,2r2 − .... − ΠS,prp| = 0, S ∈ (l, h),

lie outside the unit circle. This guarantees that the system of equations is stationary.

The state variable, zt, is assumed to depend on current and past month-on-month output

growth using an exponentially weighted moving average (EWMA), which gives larger weights,

α, to the most recent observations and geometrically declining weights to past growth rates,
zt = (cid:80)∞

i=0 α(1 − α)i(yt−i − yt−1−i). Hence, zt is a function of the entire history of yt and can

be written as:

zt = α(yt − yt−1) + (1 − α)zt−1, α ∈ (0, 1).

(4)

To construct the structural impulse response functions, the feedback from future changes

of zt−1 into the dynamics of macroeconomic system ought to be taken into account.

The reduced form TVAR is estimated in a Bayesian framework using a multivariate

version of the sampler developed in Chen and Lee (1995), which allows to draw from the

ECB Working Paper Series No 291910posterior of the model parameters. For the parameters of both regimes, we assume natural

conjugate Normal-Inverse-Wishart (N-IW) priors. The IW priors for Ωl and Ωh have n + 2

degrees of freedom and diagonal scale matrix with the i-th diagonal elements equal to the

mean squared error from estimating an AR(1) for the i-th variable. Conditional on Ωl and

Ωh, the priors for Πl and Πh are Normal with Minnesota-type mean and variance (Doan

et al., 1984), and complemented with a dummy-initial observation prior (Sims, 1993) that

is consistent with the assumption of cointegration.7 The sample spans over the monthly

period from February 1988 to December 2019 owing to the availability of the monetary

policy surprises.

As for the data, the interpolation of GDP to a monthly frequency using the Chow and Lin

(1971)’s method employs industrial production and real retail sales, while the GDP deflator

is interpolated using the consumer price index and the producer price index; thereby, both

variables are coincident indicators including supply and demand considerations. We show the

results with the consumer price index replacing the GDP deflator in the robustness section.

The policy shadow rate is equal to the Federal Fund Rate until May 2009 and to the shadow

rate provided by Wu and Xia (2016) for the rest of the sample. The excess bond premium

is provided by Gilchrist and Zakrajˇsek (2012). The expected yields and the term premia are

obtained from Adrian et al. (2013). All the other data underlying the vector Xt are obtained

from the Federal Reserve Economic Data (FRED) online database and HAVER analytics.

II.B The state variable

In several studies, the state variable is computed using a moving average of the last months

of the variable of interest (e.g. Tenreyro and Thwaites, 2016; Ramey and Zubairy, 2018;

Knotek and Zaman, 2021). This approach tends by construction to postpone the potential

change in regime, if the shock is not relatively large. The solution proposed by others is to

take a centered moving average, between t − k and t + k (e.g. Auerbach and Gorodnichenko,

2012; Ascari and Haber, 2022). However, this provides inconsistent estimates, because the

state variable ought to be predetermined, so that it is uncorrelated with the shock happening

7The hyperparameters take standard values from the literature. The hyperparameter, which determines
the tightness of the Minnesota prior, is set equal to 0.2. The parameter, which governs the variance decay
with increasing lag order, is set equal to 2. The hyperparameter, which determines the tightness of the
”dummy-initial-observation” prior is set equal to 1, a value recommended by Sims and Zha (1998).

ECB Working Paper Series No 291911at time t or in future periods.

Therefore, we construct the state variable, zt, using Equation 4 and setting α = 0.125.

This attributes a relatively larger weight to the most recent observations and allows to better

capture the timing of the starts and the end of a recession as defined by the NBER (see Figure

1). Specifically, zt is calculated using month-on-month output growth starting from February

1959, as a number of observations are required to compute the underlying output growth.

The state variable is highly correlated with year-on-year real GDP growth (94%) and its

median which defines the threshold for the regime switch is 2.53% in annualised terms over

the sample period 1988-2019. To put this figure in perspective, the median of year-on-year

real GDP growth using quarterly data is 2.56%.

As an alternative benchmark, we estimate the threshold by maximizing the marginal

likelihood through a grid search. The percentile is set at 0.25, which suggests setting the

threshold for underlying real GDP growth at 1.83% annualised. We show in the robustness

section that the results are the same using either of the thresholds.

II.C Nonlinear Structural Impulse Responses

Structural shocks, ϵt, may have nonlinear effects on Xt. Responses then depend on the

history of the data and on the sign and magnitude of the structural shocks with effects from

t to t + k. zt−1 is a function of yt−1 and, therefore, zt, zt+1,...., zt+k−1 are endogenously

determined in the TVAR. To construct the structural response functions, the feedback from

future changes of the state variable into the dynamics of the macroeconomic system is taken

into account.

Following Balke (2000) and Koop et al. (1996),8 who proposed the construction of the

response functions through the conditional expectations, we compute the nonlinear structural

IRFs, IRF X

S (ϵS,t, Γt−1(zt−1)), as the difference between the expectations of the realizations

XS,t+k at horizon k, conditional on ϵt and the information set at time t − 1, Γt−1, and the

8Koop et al. (1996) were not concerned about structural identification, they used the reduced form
residuals. Given that we focus on structural identification, the algorithm differs from Koop et al.’s approach.
See also Kilian and L¨utkepohl (2017, Chapter 18) for a discussion of state dependent IRFs.

ECB Working Paper Series No 291912expectations of the realizations XS,t+k conditioned only on Γt−1:

IRF X

S (ϵS,t, Γt−1(zt−1)) ≡ E(XS,t+k|( Γt−1(zt−1), ϵS,t)) − E(XS,t+k| Γt−1(zt−1)),

(5)

where S ∈ {l, h} indicates whether the economy is in the low- or high-growth regime at time

t + k − 1. The conditional expectations are calculated by simulating forward the model.

It is worth emphasizing that the switch among regimes is treated as endogenous, as the

economy can shift from low to high growth regimes or viceversa over the simulation horizon,

depending on the sign, the size of the shock, the estimated parameters and the specific

history of the system prior to the shock. The starting points are assumed to be the mean

of all the in-sample observations in each regime, in order to obtain the most representative

picture of the dynamics associated to each regime.

III

Identification of the Monetary Policy Shocks

We use the high-frequency monetary policy surprises provided by Bauer and Swanson (2023a)

based on the work of Swanson and Jayawickrema (2021). The surprises are estimated follow-

ing the approach of G¨urkaynak et al. (2005) and Nakamura and Steinsson (2018) computing

the changes in quarterly Eurodollar future contracts over a 30-minute window starting 10

minutes before each FOMC announcement and ending 20 minutes afterwards. Following

Nakamura and Steinsson (2018), Bauer and Swanson (2023a,b) take the first principal com-

ponent of the changes in the first four quarterly Eurodollar future contracts. As pointed

out by the authors, taking the first principal component is reminiscent of taking a weighted

average of the target and path factors, thereby parsimoniously capturing the main features

of both conventional and forward guidance monetary policy surprises. The dataset covers

the period from 1988 to 2019 and includes 322 FOMC announcements.

Event study regressions are useful to assess the impact of monetary policy shocks on

asset prices, with the underlying surprises measured over tight windows around the policy

announcement. With intradaily data and the usual 30-minute announcement windows, plus

the fact that FOMC decisions are taken few hours before the actual announcement, the

likelihood that monetary policy surprises are exogenous to contemporaneous movements

ECB Working Paper Series No 291913in asset prices is arguably high. Yet, such regressions cannot provide information about

the subsequent asset price dynamics and persistence. Furthermore, event study regressions

cannot be used to assess the macroeconomic implications.

To study the transmission mechanism of monetary policy shocks on asset prices, output

and goods prices, we rely on a Bayesian VAR. At the same time, by making use of Bauer and

Swanson (2023a) surprises, we avoid the difficulties associated with structural identification

and narrative-based efforts. However, we have to make sure that these surprises are in

fact monetary policy shocks; that is, there are no omitted variables that are correlated

with the monetary policy surprises and independently affect the endogenous variables under

investigation. Therefore, we remove the components of the monetary policy surprises that

is predictable following the recommendations in Bauer and Swanson (2023a), using data

publicly available in real time, but in a nonlinear setting.

III.A Monetary Policy Surprises: Nonlinear Regression

We regress the monetary policy surprises on the economic and financial variables that predate

the FOMC announcements and have predictive power for them as well as on the same set of

variables interacted with a dummy defining the low and high economic growth regimes, and

then take the residuals.9

The regressions take the following form:

mpst = α0 + α1 × Ft + α′

2Xt− + α′

3Xt− × Ft + uT −mp

t

,

(6)

where mpst are the monetary policy surprises in the 30-minute announcement window, Xt−

the vector of economic news observed prior to the FOMC announcement at time t, Ft is

the indicator function equal to unity if the underlying GDP growth is above the threshold,

9Miranda-Agrippino and Ricco (2021) argue that high frequency surprises are likely to combine the
monetary policy shocks with information about the state of the economy disclosed through the policy action
and, therefore, they orthogonolize these surprises with respects to the FED’s internal ”Greenbook” forecast.
Bauer and Swanson (2023a,b) show that Blue Chip forecasts have predictive power for monetary policy
surprises that is just as strong as the predictive power of the Fed’s ”Greenbook” forecasts. This implies that
the Fed is unlikely to have significantly private information, and that Fed information effects may not be
an important source of that predictability. The approach suggested by Bauer and Swanson (2023a,b) is to
employ data for the orthogonolization publicly available in real time, such as the employment report, which
is a strong predictor of the Blue Chip forecast revision. Instead, the Fed’s ”Greenbook” forecasts is publicly
available only five years after the FOMC meeting.

ECB Working Paper Series No 291914Ft{zt−1 ≥ z∗} = 1, and zero otherwise, Ft{zt−1 < z∗} = 0, and uT −mp

t

are the residuals

of the monetary policy surprises. Predictors, which are observed prior to the FOMC an-

nouncement, are those suggested by Bauer and Swanson (2023a), which have an intuitive

relationship to the Fed’ monetary policy rule: (i) the surprise component of the most recent

nonfarm payrolls release, (ii) employment growth over the last year, (iii) the log change in

the Standard & Poor’s 500 index (S&P 500) from 3 months (65 trading days) before the

FOMC announcement to the day before the FOMC announcement, (iv) the change in the

yield curve slope over the same period, (v) the log change in a commodity price index over

the same period and (iv) the option-implied skewness of the 10-year Treasury yield, averaged

over the proceeding month, from Bauer and Chernov (2021).

Column 1 of Table 1 replicates exactly the results of Bauer and Swanson (2023a) in a

linear setting. News about nonfarm payrolls, employment growth, commodity prices and

skewness of the 10-year Treasury yield are statistically significant at the 5% level predicting

a hawkish monetary policy surprise and explain 16.2% of the variation of the monetary

policy surprises. Column 3 adds the interaction terms. The prediction of the monetary

policy surprises is independent of the state of the economy, as the interaction terms are not

statistically significant based on the Wald test performed jointly on all ˆα′

3 = 0.

In addition to the six variables proposed by Bauer and Swanson (2023a), we also find

that the NFCI, proposed by Adrian et al. (2019) to assess the risk around the real GDP

growth forecasts, and provided in real time by Amburgey and McCracken (2023), is a good

predictor of the monetary policy surprises in the nonlinear model. Specifically, any change

in the NFCI from two up to eleven weeks before the FOMC announcement to the day before

the FOMC announcement is strongly statistically significant. We select for the analysis the

change corresponding to the period with the largest t-statistics, that is seven weeks. The

results for the regressions including the NFCI as a predictor are provided in column 2 for

the linear model, where the coefficient is not statistically significant, and in column 4 for

the nonlinear model, where the coefficients for both the low and high growth regime are

statistically significant at the 1% level with both t-statistics above 3, but with opposite sign.

Because the NFCI is a generated regressor, we report in parentheses bootstrapped t-stat

using 50,000 bootstrap replications.

ECB Working Paper Series No 291915Increasing risk, tighter credit conditions and declining leverage are consistent with in-

creases in the NFCI. The FED seems to respond to the dynamics in the NFCI more strongly

than expected by market participants in the low growth regime, as tighter financial condi-

tions predict a hawkish monetary policy surprise. In the high growth regime, the opposite

occurs. Investors expect relatively high policy rates after a tightening in credit condition,

whereas the FOMC delivers a lower increase in rates, as ˆα2,N F CI + ˆα3,N F CI < 0 and the Wald

test performed on the coefficients’ sum set equal to zero, ˆα2,N F CI + ˆα3,N F CI = 0, has a ˜χ(1)2

= 5.99 with the correspondent P-value equal to 0.014.

Moreover, the results indicate that market based factors are better predictors within

the nonlinear setting and by controlling for the NFCI, as the change in the stock market

price and the change in slope of the yield curve, with the intuitive positive and negative

signs respectively, become strongly statistically significant, and together with the change

in commodity prices, have a larger ˆα2. More specifically, when the yield curve becomes

more upward-sloping (i.e., when short-term interest rates fall relative to long-term rates, as

they do during monetary easing cycles), the Fed is likely to follow with an easing surprise.

Interestingly, once controlling for the NFCI, the nonfarm payrolls is a weaker predictor of

the monetary policy surprises. Overall, the R2 of the nonlinear model including the NFCI

(22.3%) increases by about five percentage points relative to the same model that does not

control for the NFCI (17.1%).

The high-frequency residuals ump

t

are converted to a monthly series by summing over all of

the figures within the same month and set them to zero in absence of FOMC announcements

(see Figure 2). Equivalent aggregation methods are adopted in the literature (e.g. Stock and

Watson, 2012; Miranda-Agrippino and Ricco, 2021; Bauer and Swanson, 2023a).

III.B Additional Issues

It could be argued that most high-frequency policy surprises occur around FOMC announce-

ments during low growth regimes (as suggested by Cieslak (2018) and Schmeling et al. (2022).

In other words, it could be argued that the size of the shocks differs across regimes; partly

affecting some of the results. mpst has a standard deviation equal to 5.7 basis points in the

low growth regime and 4.9 basis points in the high growth regime. However, ump

t has a stan-

ECB Working Paper Series No 291916dard deviation equal to 4.6 basis points in the low growth regime and 4.4 basis points in the

high growth regime. We thus use a new measure of monetary policy surprises that is both

more relevant and more exogenous than those used by previous researchers and investigate

whether monetary policy is more effective in recessions or expansions.

Once a shock is available, the typical approach is to use it as an external instrument on

the residuals of a VAR (Gertler and Karadi, 2015; Stock and Watson, 2012, 2018; Caldara

and Herbst, 2019; Miranda-Agrippino and Ricco, 2021; Bauer and Swanson, 2023b). In our

nonlinear setting, this would require estimating two different VARs with an external instru-

ment depending upon the regime. A TVAR achieves this objective with the advantage of

removing any remaining potential autocorrelation structures in the residuals of the monetary

policy surprises (e.g. Miranda-Agrippino and Ricco, 2021). By so doing, we account for the

slow absorption of information by the agents characterising models of imperfect information

(e.g. Coibion and Gorodnichenko, 2015).

An alternative approach consists of studying the transmission of the monetary policy

shocks using local projections (e.g. Ramey, 2016; Miranda-Agrippino and Ricco, 2021; Bauer

and Swanson, 2023a). In our context, the nonlinear IRFs depend on the future state of the

economy, which could be steered by the policymaker, which we can trace with a TVAR.

Gon¸calves et al. (2024) show that, when the state of the economy is endogenous, the local

projections’ estimator of the response function tends to be asymptotically biased except for

the impact response. Francis et al. (2023) draw a very similar conclusion. Moreover, in

a linear setting, Plagborg-Møller and Wolf (2021) prove that local projections and VARs

estimate the same population impulse responses. The authors also recommend including the

instrument in the VAR, ordering it first, and using a recursive ordering to estimate its effects,

which makes results robust to non-invertibility of the structural shock of interest. Following

this suggestion, we estimate a TVAR including the monthly announcement-frequency resid-

ual series aggregated from uT −mp

t

, order it first and investigate the potential heterogeneous

transmission mechanism of the monetary policy shocks. Yet, we also show the results using

nonlinear local projections.

ECB Working Paper Series No 291917IV The Impact of Monetary Policy Shocks

IV.A Nonlinear Transmission Mechanism of Monetary Policy

We start the analysis with the 8-variable monthly VAR, Xt = [uT −mp

t

, rt, ien

t , itpn

t

, bt, et, yt, pt]′,

and by considering the effects of the monetary policy shocks in the linear model using the

recursive identification with the residuals of the monetary policy surprises ordered first.10

This allows to further clean the variable of interest from autocorrelation structures and makes

results robust to non-invertibility of the monetary policy shock. The resulting IRFs of the

linear model are displayed in Panel A of Figure 3. The blue line provides the median IRFs

and the shaded bands around the median IRFs provide the corresponding posterior 68%

credible sets. A monetary policy shock amounting to 25 basis points increases at impact

the effective federal fund rate by 9 basis points and the excess bond premium by 14 basis

points, and causes a drop in the stock market by 4%. Conversely, the impact on the expected

component of the 10-year Treasury rate is rather volatile, while the credible set of the term

premium is almost all in the positive territory after about two years. The monetary policy

shocks reduce output reaching the trough at -0.4% after four years and goods prices at -0.2%

after few months.

We now turn to the key research question of the paper: does the transmission of the

monetary policy shocks to financial and macroeconomic variables differ across the low and

high growth regimes? Panel B of Figure 3 provides an answer to this question. The solid

blue line provides the median IRFs in the low growth regime and the shaded bands around

the median IRFs provide the corresponding posterior 68% credible sets. The solid red line

provides the median IRFs in the high growth regime and the dashed red lines around the

median IRFs provide the corresponding posterior 68% credible sets.

The first point to note in Figure 3 is that, despite a similar response at impact amounting

to about 5-10 basis points, the persistence of the effective federal fund rate is much lower in

the low growth regime, returning back to steady state in few months, rather than two years

and half in the high growth regime. The response of the interest rates is intuitive, because a

contractionary monetary policy in a low growth environment could push the economy into

10In the case of the linear model, the dummy in Equation (6) is set equal to zero.

ECB Working Paper Series No 291918a recession and, in such circumstances, interest rates are expected to decline.

The second key point is that a contractionary monetary policy exercised in the low growth

regime causes an immediate increase in the excess bond premium of the corporate sector by

22 basis points and a large decline in the the stock market price by 5.2%.

In the high

growth regime, instead, the excess bond premium is not really affected and the stock market

price declines by only 2.7%. In an environment characterised by low economic growth, the

corporate sector is already relatively weak. Therefore, companies have to offer higher premia

for investors to hold their assets in their portfolio in response to a contractionary monetary

policy shock.

The third point is that output and goods prices are strongly affected in a low growth

regime. A 25 basis point contractionary monetary policy implies a drop in GDP by 0.5% at

through after about two years and in goods prices by 0.2% already after four months. In the

high growth regime, about 85% of the credible set is below zero at two year horizon, while a

large fraction of the credible sets of goods prices include zero. Contrary to the most recent

conclusions reached by the literature provided by Tenreyro and Thwaites (2016), we find

that monetary policy is more powerful in states of low economic growth. When the economy

is already weak, the balance sheets of companies and households are rather fragile. In such

an environment, the tightening of the financing conditions by the monetary authorities has

stronger macroeconomic implications. We will show that these results are corroborated when

using the quarterly frequency without interpolation or when substituting the GDP deflator

with the Personal Consumption Expenditure (PCE) goods inflation. In the quarterly model,

the response of GDP to monetary policy shocks is also more negative in the high growth

regime relative to the monthly model.

The fourth point is the different response of the long-term expected yields and term

premia. The expected yields decline after a contractionary monetary policy shock in a low

growth regime, while they rise in a high growth regime (see Figure 3). The distribution of

their IRFs in the high growth regime shifts downwards with the upper bound of the credible

set declining very close to zero at 2-year horizon. Conversely, the term premia increase after

a contractionary monetary policy shock in a low growth regime, while they decrease in a high

growth regime. The response of the long term expected interest rates is intuitive, because a

ECB Working Paper Series No 291919contractionary monetary policy in a low growth environment could push the economy into

a recession and, in such circumstances, the long term interest rates are expected to decline.

Also the response of the term premia is intuitive, because they are countercyclical; they rise

in a low growth environment, but remain unaffected if the economy is booming. The overall

impact on nominal yields, which is obtained by summing the two IRFs for each draw (see

Panel C of Figure 4), shows that the long-term interest rates rise in the high growth regime,

but mean-revert in the low growth regime declining in the first few months.

We replicate the above exercise changing the maturity of the Treasury yield (see Figure

4). The results are essentially the same in both regimes when using the 5-year Treasury yield

and qualitatively similar with shorter rates, with the shape of the IRFs being very similar

across the yield curve. As for the term premium, it rises countercyclically in the low growth

regime along the entire yield curve after about 6 months, reaching the peak after about 16

months, and the impact is larger the higher is the maturity of the Treasury.

A summary of the results is also provided in Table 2 at impact and 6-month horizon.

Focusing on the median value, there is a clear hump-shape response in the high growth regime

along the expected yield curve, which is rising together with the horizon with the largest

value recorded at the 3-year maturity. Conversely, the 3-year expected yield already tends

to decline at impact in the low growth regime. As for the countecyclical term premium,

the impact is largest for Treasuries at longer maturity, at longer horizon and in the low

growth environment. Also the corporate excess bond premium rises most immediately and

in the low growth environment. Similarly, the stock market prices decline immediately and

mean-revert especially in the low growth environment.

Following the analysis carried out by Tenreyro and Thwaites (2016), we also study

the differential impact of the monetary policy shocks on the volume of fixed private busi-

ness investment, yf

t , the volume of durable goods and housing investment, yd

t , and the

consumption of nondurable goods and services in real terms, yn

t . The Bayesian VAR,

Xt = [uT −mp

t

, rt, yt, pt, yf

t , yd

t , yn

t ]′, uses quarterly data and in addition it includes real GDP,

DGP deflator, the shadow rate the and the orthogolonized monetary policy surprises, which

are ordered first. The high-frequency ut−mp

t

are converted to a quarterly series by summing

over all of the figures within the same quarter and set them to zero in absence of FOMC

ECB Working Paper Series No 291920announcements.11 Figure 5 plots the impulse response of the volumes of the three expendi-

ture aggregates to the same monetary policy shock. In line with the response of aggregate

output, all the volume indices decline in low and high growth regimes. The effect on pri-

vate investment and durable goods is three times the size of the impact on non-durables

and services. A 25 basis point standard deviation monetary policy shock implies at through

after half year a 1.4% drop in both the volume of fixed private business investment and the

volume of durable goods and housing investment. The consumption of nondurable goods

and services in real terms records a 0.4% drop at through after one year, which resembles the

impact on aggregate real GDP. The impact, however, is more uncertain for the consumption

of durable and housing investment volume in the low growth regime. The results across

aggregates are consistent with the fact that spending on investment and durables can be

postponed in response to adverse shocks, as existing stocks of investment and durable goods

can still provide utility given their longer lifespans.

Our results are in line with the findings by Tenreyro and Thwaites (2016) in periods of

expansions, while they are just the opposite in recession periods. Tenreyro and Thwaites

(2016) find a positive response of all disaggregated volume components after a monetary

policy contraction in recessions in the first year and in the case of fixed business investment

over three years. Hence, their results are difficult to interpret in recession phases.

We also show the results on the other variables used in the quarterly model. The results

are broadly similar to the monthly model for the shadow rate and real GDP and are the

same for the GDP deflator. Focusing on the differences, the response of the shadow rate is

less persistent and the response of real GDP is strong and clearly negative also in the high

growth regime. Overall, the results of the quarterly model corroborate our findings that

the monetary policymaker is able to bring down goods prices only when the real economy

is already weak. Consistently with economic theory, the monetary multiplier on private

investment and durable good is larger than the multiplier on the consumption of nondurable

goods and services.

11We construct the state variable, zt, using Equation 4 and setting α = 0.2. When using quarterly data,
a relatively larger weight to the most recent observations allows to better capture the timing of the starts
and the end of a recession as defined by the NBER. The median which defines the threshold for the regime
switch is 2.56% in annualised terms in line with the monthly model.

ECB Working Paper Series No 291921V On the Difference with Tenreyro and Thwaites (2016)

The aim of this section is to provide an explanation behind the opposite conclusions obtained

by Tenreyro and Thwaites (2016). Two are the potential reasons. First, the differences

could be due to the identification of the monetary policy surprises, high-frequency versus

the Romer and Romer (2004)’s identification. To check if this is the case, we estimate

the TVAR as well as a smooth-transition VAR (STVAR), the latter being even closer to the

method of Tenreyro and Thwaites (2016), using the Romer-Romer monetary policy surprises

and the overall database of Tenreyro and Thwaites (2016). If the results of Tenreyro and

Thwaites (2016) do not hold, the findings will be suggestive that the use of Romer and Romer

(2004)’s monetary policy surprises as exogenous monetary policy shocks is inappropriate.

Second, the differences could be due to the different methodologies. Therefore, we also

estimate the macroeconomic response of the monetary policy shocks following Tenreyro

and Thwaites (2016) by using nonlinear local projection methods, but employing our high-

frequency monetary policy surprises as exogenous shocks.

To make the results comparable, in this section only, we move away from our baseline

model and adopt the strategy of Tenreyro and Thwaites (2016). The variables are (the log of)

real GDP, PCE inflation, the Federal fund rate and the monetary policy shock. The model

is estimated with two quarter lags and the transition to the other regime is not allowed. The

latter is an important deficiency. In fact, Gon¸calves et al. (2024) and Francis et al. (2023)

show that, when the state of the economy is endogenous, the local projections’ estimator of

the response function tends to be asymptotically biased.

V.A Tenreyro and Thwaites (2016) and the STVAR

Tenreyro and Thwaites (2016) argue that an important advantage of a smooth transition-

local projection model is that it does not require to take a stand on how the economy shifts

from one regime to another, except for the parameters of a logistic function. However, a

local projection model is an appropriate method to estimate response functions, only if the

surprises are exogenous (Jord`a, 2005), which is not the case for Romer and Romer (2004)

monetary policy surprises (Miranda-Agrippino and Ricco, 2021).

ECB Working Paper Series No 291922Therefore, we estimate the STVAR, employed in Auerbach and Gorodnichenko (2012)

and Ramey and Zubairy (2018) to analyze fiscal policy, whose reduced-form has the same

features of the smooth transition-local projection model used by Tenreyro and Thwaites

(2016), but has the advantage to control for autocorrelation as well as past dynamics of

other regressors:

Xt = (1 − F (zt−1))ΠST

l

(L)Xt−1 + F (zt−1)ΠST

h (L)Xt−1 + uST
t

,

t ∼ N (0, ΩST
uST
t

),

ΩST

t = ΩST

l

(1 − F (zt−1)) + ΩST

h F (zt−1),

F (zt) =

exp(γ(zt − c)/σz)
1 + exp(γ(zt − c)/σz)

, γ > 0,

(7)

(8)

(9)

(10)

where Xt denotes the vector of endogenous variables and zt the state variable. The logistic

function F (zt) = [0, 1] determines the smooth transition from one regime to the other with

γ being the logistic growth rate, c the Sigmoid point and σz the standard deviation of zt.

If zt = c, F (zt) = 0.5. If the difference between zt and c is positive (i.e. high growth) and

large, F (zt) ≈ 1. If, instead, the difference between zt and c is negative (i.e.

low growth)

and (in absolute value) large, F (zt) ≈ 0. As in Tenreyro and Thwaites (2016), γ is set equal

to 3, c is set at the worst 20 percent of the periods in the sample, P r(F (zt) < 0.5 = 0.2),

and zt is defined as a seven quarter lagging moving average of real quarterly GDP growth.

The differences in the propagation of shocks across regimes is due to differences in co-

variance matrices for disturbances ΩST

l

and ΩST

h and differences in lag polynomial ΠST

l

and

ΠST

h . ΩST

l

and ΠST

l

describe the behaviour of the system in a sufficiently low-growth regime

(i.e. 1 − F (zt) ≈ 1) and ΩST

h and ΠST

h describe the behaviour of the system in a sufficiently

high-growth regime (i.e. F (zt) ≈ 1). uST

t

is the vector of reduced form residuals and ΩST

t

the

time-varying, state-contingent variance-covariance matrix. The first vector of uST

t

provides

the orthogonolized monetary policy surprises.

Given that we use the database of Tenreyro and Thwaites (2016) over the same sample

period 1969Q1 - 2002Q4, we can use the nonlinear monetary policy shocks that they have

estimated, as they depend upon the probability of expansion, F (zt), which we can replicate.

ECB Working Paper Series No 291923The vector Xt = [uRR

t

, rt, yt, πt]′ defines the endogenous variables used by Tenreyro and

Thwaites (2016), where uRR

t

, are the nonlinear Romer-Romer monetary policy surprises

estimated by Tenreyro and Thwaites (2016), rt the Federal Fund Rate, yt the log of real GDP

and πt the quarterly PCE inflation rate. The STVAR is estimated in a Bayesian framework

using the Monte Carlo Markov Chain sampler developed in Galv˜ao and Owyang (2018).

Shocks are identified using the recursive method with the Romer-Romer monetary policy

surprises ordered first. This allows to clean the variable of interest from the autocorrelation

and from the lagged structure of the VAR. Following Tenreyro and Thwaites (2016), the

model uses two quarter lags.

The key results are reported in Panel A of Figure 6. The impulse responses conditional

on remaining in the same regime in all horizons can be compared with those in Figure 2

of Tenreyro and Thwaites (2016). A monetary policy shock causes a temporary increase

in real GDP during recessions and a permanent decline in real GDP during expansions.

Both results are in contradiction with a temporary decline in real GDP expected in theory.

As for prices, monetary policy shocks have no impact on inflation during recessions and

expansions. All in all, the results obtained by Tenreyro and Thwaites (2016) cannot be

replicated using a STVAR. This can be explained by the fact that Romer-Romer surprises

are autocorrelated (Miranda-Agrippino and Ricco, 2021) and depend upon the past variables

of the VAR. A STVAR removes the autocorrelation structure in the Romer-Romer residuals

and their predictability.

We carry out the same exercise using the same STVAR structure with four variables

and two quarter lags to corroborate the previous findings obtained with the Bauer-Swanson

monetary policy surprises. First, we remove the components of the Bauer and Swanson

(2023b)’s monetary policy surprises that is predictable using a logistic function:

mpst = (1 − F (zt−1))α′

lXt− + F (zt−1)α′

hXt− + uST −mp

t

,

(11)

where Xt− is the vector of economic news observed prior the FOMC announcement at time

t including the NFCI. To make the results comparable, as in Tenreyro and Thwaites (2016),

the Sigmond point of F (zt−1), c, is set at 20% and γ = 3. The results, shown in Panel

A of Figure 6, confirm the baseline findings that monetary policy is powerful in recessions,

ECB Working Paper Series No 291924as after a 25 basis point monetary policy shock output decline by about 0.8% with rather

persistent effects and inflation declines temporarily by about 0.3 percentage points. However,

in contrast with our findings obtained with the baseline model, in expansion phases, there

is no impact on GDP and inflation rises marginally and temporarily. As we show in the

robustness section, where the baseline model is used, including forward looking variables in

the systems of equations, as raccomended by the literature, is important.

V.B Tenreyro and Thwaites (2016) and the TVAR

To re-evaluate the aforementioned conclusions, we also estimate the TVAR with the same

set of variables proposed by Tenreyro and Thwaites (2016). In this case, we first need to

estimate the nonlinear Romer-Romer monetary policy surprises consistently with the TVAR.

Therefore, we regress the FFR against the Fed’s internal ”Greenbook” forecast, provided

by Wieland and Yang (2020), as well as on the same set of variables interacted with a dummy

defining the low and high economic growth regimes. We follow Tenreyro and Thwaites (2016)

in defining the low growth regime (i.e a recession), as the worst 20% of the periods in the

sample.

The original Romer and Romer (2004) regression is modified as follows:

∆F F Rt = α0 + α1 × Ft + α′

2Xt + α′

3Xt × Ft + uRR

t

,

(12)

where Xt are the control variables employed by Romer and Romer (2004),12 Ft is the indicator

function which is equal to unity if the underlying GDP growth is above the threshold that

guarantees 80% of the observations and uRR

t

are the residuals of the Romer-Romer surprises.

When estimated linearly over a common sample, we replicate the results exactly (see Figure

7). The state-dependent Romer-Romer surprises have a 0.890 correlation with the linear

series over the 1969-2002 sample. The R2 of the FFR equation increases from 28.1% in the

linear setting to 43.0% in the nonlinear model, suggesting that the response of the Federal

Reserve Board to the business cycle is state-dependent. The surprises are then aggregated

12Romer and Romer (2004) regress the change in the intended federal funds rate against the initial level
of intended funds rate, the forecasted output growth, inflation and unemployment rate, and the change in
forecasted output growth and inflation since the previous meeting over the subsequent two quarters.

ECB Working Paper Series No 291925to quarterly frequency.

The impulse responses resulting from the TVAR are depicted in Panel B of Figure 6.

The results on real GDP resemble those of the STVAR. In addition, there is a price puzzle

in expansions. This suggests again that the Romer and Romer (2004)’s monetary policy

surprises cannot be treated as exogenous monetary policy shocks.

We carry out the same exercise using the same TVAR structure with four variables and

two quarter lags and use the Bauer-Swanson monetary policy surprises. First, we remove the

components of the Bauer and Swanson (2023b)’s monetary policy surprises that is predictable

using the threshold function 12 and the above mentioned regressors. The results, shown in

Panel B of Figure 6, confirm the findings obtained with the STVAR. Monetary policy shocks

are more powerful in recession regimes.

V.C High-frequency policy surprises and local projections

How important is the local projection methodology adopted by Tenreyro and Thwaites (2016)

for the results? Local projections are semi-parametric in nature and do not assume a specific

model. They are more flexible, but the estimation uncertainty is higher (Kilian and Kim,

2011). Moreover, local projections treat the orthogonolized monetary policy surprises as a

shock. Instead, a VAR cleans the monetary policy surprises from autocorrelation and makes

it orthogonal to the lags of other variables of the system. Despite these warnings, we test

whether the results summarized in Section IV survive using local projections.

The consistently transformed and orthogonolized monetary policy surprises, uST −mp

t

, are

then used as shocks to obtain the response functions from the regime-switching local projec-

tion model `a la Tenreyro and Thwaites (2016):

yt+k = (1 − F (zt−1))(cl + βl,kuST −mp

t

+ η′

l,k(L)Xt−1)+

(13)

F (zt−1)(ch + βh,kuST −mp

t

+ η′

h,k(L)Xt−1) + uST −LP

t+k

,

where the coefficients βS,k measure the average effect of a shock at horizon k = 0, ..., 48 as a

function of the state of the economy F (zt−1) at the time of the shock, cS are state-dependent

intercepts, Xt are controls, F (zt) is the aforedefined logistic function.13 The regressand yt+k

13We use Jord`a (2005)’s code and extend it to the nonlinear case.

ECB Working Paper Series No 291926in this case empirically accounts for the possible regime changes between periods t and t + k.

Moreover, we run a threshold local projection, which is more comparable to our TVAR,

running the following model

yt+k = (1−Ft)(cl +βl,kuT −mp

t

+η′

l,k(L)Xt−1)+Ft(ch+βh,kuT −mp

t

+η′

h,k(L)Xt−1)+uT −LP

t+k

, (14)

where Ft is the indicator function equal to unity if the underlying GDP growth is above the

threshold, Ft{zt−1 ≥ z∗} = 1, and zero otherwise, Ft{zt−1 < z∗} = 0 and uT −mp

t

are the

residuals of the monetary policy surprises, as obtained from Equation 6.

Panels C and D of Figure 6 provide the results based on the smooth transiton local pro-

jections and the threshold local projections, respectively. The results suggest that monetary

policy shocks are more powerful in recessions, but with no clear impact on inflation, when

using the Romer-Romer surprises. Conversely, the decline in output is accompanied with

a decline in inflation in recessions when using the Bauer-Swanson surprises. All in all, the

main difference we find with respect to the results reported in Tenreyro and Thwaites (2016)

are not generated by the different response functions’ estimators adopted.

VI Robustness Checks

All the robustness checks are carried out using eight variables, including the forward looking

variables of the baseline, and focus on the Bauer-Swanson monetary policy surprises.

As an alternative benchmark, the threshold is estimated through a grid search over

possible values of the observations’ percentiles. The marginal likelihood is maximised at

percentile 0.25, which suggests setting the threshold for underlying real GDP growth at 1.83%

annualised. The number of observations in the low growth regime declines to 90 at monthly

frequency. The results are displayed in Panel A of Figure 8 and resemble those reported in

the baseline Figure 3 for both regimes, corroborating the conclusions that monetary policy

shocks are more powerful in recessions.

It is useful to emphasise the difference with the

results obtained with the four variables model (Panel B of Figure 6), which suggests that

forward looking variables are important in extracting the shocks.

It could be argued that monetary policy is better illustrated using PCE inflation, as

ECB Working Paper Series No 291927employed by Tenreyro and Thwaites (2016). Results and conclusions remain broadly in-

variant, if substituting the GDP deflator with PCE inflation (see Panel B of 8). Following

a contractionary monetary policy shock in the low growth regime, monthly PCE inflation

declines and after few months it returns back to its long-run trend in line with economic

theory. Conversely, monetary policy does not seem to succeed in bringing goods prices down

in high growth regimes.

The literature recommends to use the Minnesota prior together with the dummy-initial-

observation prior with VAR in levels, because sampling errors make results erratic in larger

models under a flat prior (Sims, 1993; Sims and Zha, 1998). We provide a robustness check

using a flat prior, but we reduce the number of lags to six, so that the number of parameters

to estimate reduce substantially. The results, which are provided in Panel C of 8, corroborate

the key findings of the paper.

The final sets of robustness checks refer to alternative nonlinear methods in estimating the

baseline model. The STVAR and the local projection methods corroborate the key finding

that monetary policy is more successful in reducing goods prices in low growth regimes (see

Figure 9).

VII Conclusions

We investigate whether the transmission mechanism of the monetary policy on asset prices,

output and goods prices are state dependent. This question has been already addressed in

the literature. However, we revisit the issue using monetary policy surprises obtained from

high-frequency data (e.g. Bauer and Swanson, 2023a,b).

First, we remove the components of the monetary policy surprises that is predictable

following the recommendations in Bauer and Swanson (2023a), but in a nonlinear setting. At

the same time, we propose the National Financial Condition Index (NFCI) as an additional

predictor of the monetary policy surprises, which is relevant in the nonlinear model.

We show that monetary policy shocks are more powerful in the low growth regime, with

the negative response on output accompanied by a negative response on goods prices; in the

high growth regime, instead the response of goods prices is muted. A Threshold VAR, a

ECB Working Paper Series No 291928Smooth-Transition VAR and nonlinear local projection methods all corroborate the findings.

Across sub-components of real GDP, the impact is a larger on private investment and durable

goods than on nondurables and services consumption, consistent with the fact that spending

on durables can be postponed in response to adverse shocks.

Regarding asset prices, the response of expected Treasuries along the entire yield curve

is positive in expansions but negative in recessions. In periods of low economic growth, a

contractionary monetary policy is judged by markets as further conducive to lower nominal

economic growth and therefore a decline in the short term interest rates is expected by

investors. Bond holders demand higher premia for holding sovereign and corporate bonds in

recessions, confirming that the primary driver of the term premia is the real economy and

associated risks. As for stock prices, they decline in response to a monetary policy shock, as

suggested by textbooks, and the drop is relatively somewhat deeper during recessions.

These results are broadly consistent with theoretical models assuming information asym-

metries in financial markets (Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997). During

recessions, asset prices drop, business contracts, the net worth of agents declines, the ex-

ternal finance premium rises and finance constraints are more likely to bind. The financial

accelerator amplifies the effects of a tightening in monetary policy.

These findings are relevant for the design of stabilization policies.

If changes in the

monetary policy rates have limited impact on inflation in periods of expansions, central

banks may be required to strongly slowdown economic activity with a risk of causing a

recession to be able to steer the inflation rate towards the desired target.

References

Acemoglu, Daron and Andrew Scott (1997) “Asymmetric business cycles: Theory and time-

series evidence,” Journal of Monetary Economics, 40 (3), 501–533.

Adrian, Tobias, Nina Boyarchenko, and Domenico Giannone (2019) “Vulnerable Growth,”

American Economic Review, 109 (4), 1263–1289.

ECB Working Paper Series No 291929Adrian, Tobias, Richard Crump, and Emanuel Moench (2013) “Pricing the term structure

with linear regressions,” Journal of Financial Economics, 110 (1), 110–138.

Alpanda, Sami, Eleonora Granziera, and Sarah Zubairy (2021) “State dependence of mone-

tary policy across business, credit and interest rate cycles,” European Economic Review,

140 (C).

Altavilla, Carlo, Luca Brugnolini, Refet S. G¨urkaynak, Roberto Motto, and Giuseppe Ragusa

(2019) “Measuring euro area monetary policy,” Journal of Monetary Economics, 108 (C),

162–179.

Amburgey, Aaron J. and Michael W. McCracken (2023) “On the real-time predictive content

of financial condition indices for growth,” Journal of Applied Econometrics, 38 (2), 137–

163.

Angrist, Joshua D., `Oscar Jord`a, and Guido M. Kuersteiner (2018) “Semiparametric Es-

timates of Monetary Policy Effects: String Theory Revisited,” Journal of Business &

Economic Statistics, 36 (3), 371–387.

Ascari, Guido and Timo Haber (2022) “Non-Linearities, State-Dependent Prices and the

Transmission Mechanism of Monetary Policy [Dynamic effects of persistent shocks],” The

Economic Journal, 132 (641), 37–57.

Auerbach, Alan J. and Yuriy Gorodnichenko (2012) “Measuring the Output Responses to

Fiscal Policy,” American Economic Journal: Economic Policy, 4 (2), 1–27.

Balke, Nathan (2000) “Credit and Economic Activity: Credit Regimes and Nonlinear Prop-

agation of Shocks,” The Review of Economics and Statistics, 82 (2), 344–349.

Bauer, Michael D and Mikhail Chernov (2021) “Interest Rate Skewness and Biased Beliefs,”

Working Paper 28954, National Bureau of Economic Research.

Bauer, Michael D. and Eric T. Swanson (2023a) “A Reassessment of Monetary Policy Sur-

prises and High-Frequency Identification,” NBER Macroeconomics Annual, 37, 87–155.

(2023b) “An Alternative Explanation for the ”Fed Information Effect”,” American

Economic Review, 113 (3), 664–700.

ECB Working Paper Series No 291930Bernanke, Ben and Mark Gertler (1989) “Agency Costs, Net Worth, and Business Fluctua-

tions,” American Economic Review, 79 (1), 14–31.

Bernanke, Ben S. and Kenneth N. Kuttner (2005) “What Explains the Stock Market’s Re-

action to Federal Reserve Policy?,” Journal of Finance, 60 (3), 1221–1257.

Borio, Claudio, Marco Jacopo Lombardi, James Yetman, and Egon Zakrajsek (2023) “The

two-regime view of inflation,” Working Paper 133, Bank for International Settlements.

Browning, Martin and Thomas Crossley (2009) “Shocks, Stocks, and Socks: Smoothing

Consumption Over a Temporary Income Loss,” Journal of the European Economic Asso-

ciation, 7 (6), 1169–1192.

Bruns, Martin and Michele Piffer (2021) “Monetary policy shocks over the business cycle:

Extending the Smooth Transition framework,” University of East Anglia School of Eco-

nomics Working Paper Series 2021-07.

Burgard, Jan Pablo, Matthias Neuekirch, and Matthias Noeckel (2019) “State-Dependent

Transmission of Monetary Policy in the Euro Area,” Journal of Money, Credit and Bank-

ing, 51 (7), 2053–2070.

Caldara, Dario and Edward Herbst (2019) “Monetary Policy, Real Activity, and Credit

Spreads: Evidence from Bayesian Proxy SVARs,” American Economic Journal: Macroe-

conomics, 11 (1), 157–92.

Chen, Cathy W. S. and Jack C. Lee (1995) “Bayesian Inference of Threshold Autoregressive

Models,” Journal of Time Series Analysis, 16 (5), 483–492.

Chen, Shiu-Sheng (2007) “Does Monetary Policy Have Asymmetric Effects on Stock Re-

turns?” Journal of Money, Credit and Banking, 39 (2-3), 667–688.

Chow, Gregory C and An-loh Lin (1971) “Best Linear Unbiased Interpolation, Distribu-

tion, and Extrapolation of Time Series by Related Series,” The Review of Economics and

Statistics, 53 (4), 372–375.

Cieslak, Anna (2018) “Short-Rate Expectations and Unexpected Returns in Treasury

Bonds,” Review of Financial Studies, 31 (9), 3265–3306.

ECB Working Paper Series No 291931Cochrane, John H. and Monika Piazzesi (2002) “The Fed and Interest Rates - A High-

Frequency Identification,” American Economic Review, 92 (2), 90–95.

Coibion, Olivier and Yuriy Gorodnichenko (2015) “Information Rigidity and the Expec-

tations Formation Process: A Simple Framework and New Facts,” American Economic

Review, 105 (8), 2644–78.

Doan, Thomas, Robert Litterman, and Christopher Sims (1984) “Forecasting and conditional

projection using realistic prior distributions,” Econometric Reviews, 3 (1), 1–100.

Faust, Jon, John H. Rogers, Eric Swanson, and Jonathan H. Wright (2003) “Identifying

the Effects of Monetary Policy Shocks on Exchange Rates Using High Frequency Data,”

Journal of the European Economic Association, 1 (5), 1031–1057.

Faust, Jon, Eric Swanson, and Jonathan Wright (2004) “Identifying VARS based on high

frequency futures data,” Journal of Monetary Economics, 51 (6), 1107–1131.

Francis, Neville, Michael Owyang, and Daniel F. Soques (2023) “Impulse Response Functions

for Self-Exciting Nonlinear Models,” NBER Working Papers 31709, National Bureau of

Economic Research, Inc.

Galv˜ao, Ana Beatriz and Michael T. Owyang (2018) “Financial Stress Regimes and the

Macroeconomy,” Journal of Money, Credit and Banking, 50 (7), 1479–1505.

Garcia, Ren´e and Huntley Schaller (2002) “Are the Effects of Monetary Policy Asymmetric?”

Economic Inquiry, 40 (1), 102–119.

Gertler, Mark and Peter Karadi (2015) “Monetary Policy Surprises, Credit Costs, and Eco-

nomic Activity,” American Economic Journal: Macroeconomics, 7 (1), 44–76.

Gilchrist, Simon and Egon Zakrajˇsek (2012) “Credit Spreads and Business Cycle Fluctua-

tions,” American Economic Review, 102 (4), 1692–1720.

Gon¸calves, S´ılvia, Ana Mar´ıa Herrera, Lutz Kilian, and Elena Pesavento (2024) “State-

dependent local projections,” Journal of Econometrics, 105702.

ECB Working Paper Series No 291932Granger, Clive and Timo Ter¨asvirta (1993) Modelling Non-Linear Economic Relationships:

Oxford University Press.

G¨urkaynak, Refet S, Brian Sack, and Eric Swanson (2005) “Do Actions Speak Louder Than

Words? The Response of Asset Prices to Monetary Policy Actions and Statements,”

International Journal of Central Banking, 1 (1).

Hanson, Samuel and Jeremy Stein (2015) “Monetary policy and long-term real rates,” Jour-

nal of Financial Economics, 115 (3), 429–448.

Jaroci´nski, Marek and Peter Karadi (2020) “Deconstructing Monetary Policy Surprises—The

Role of Information Shocks,” American Economic Journal: Macroeconomics, 12 (2), 1–43.

Jord`a, `Oscar (2005) “Estimation and Inference of Impulse Responses by Local Projections,”

American Economic Review, 95 (1), 161–182.

Kilian, Lutz and Yun Jung Kim (2011) “How Reliable Are Local Projection Estimators of

Impulse Responses?,” The Review of Economics and Statistics, 93 (4), 1460–1466.

Kilian, Lutz and Helmut L¨utkepohl (2017) Structural Vector Autoregressive Analysis: Cam-

bridge University Press.

Kiyotaki, Nobuhiro and John Moore (1997) “Credit Cycles,” Journal of Political Economy,

105 (2), 211–248.

Knotek, Edward and Saeed Zaman (2021) “Asymmetric responses of consumer spend-

ing to energy prices: A threshold VAR approach,” Energy Economics, 95 (C),

S0140988321000323.

Koop, Gary, M Pesaran, and Simon Potter (1996) “Impulse Response Analysis in Nonlinear

Multivariate Models,” Journal of Econometrics, 74 (1), 119–147.

Kuttner, Kenneth N. (2001) “Monetary policy surprises and interest rates: Evidence from

the Fed funds futures market,” Journal of Monetary Economics, 47 (3), 523–544.

ECB Working Paper Series No 291933Lo, Ming and Jeremy Piger (2005) “Is the Response of Output to Monetary Policy Asym-

metric? Evidence from a Regime-Switching Coefficients Model,” Journal of Money, Credit

and Banking, 37 (5), 865–86.

Miranda-Agrippino, Silvia and Giovanni Ricco (2021) “The Transmission of Monetary Policy

Shocks,” American Economic Journal: Macroeconomics, 13 (3), 74–107.

Mumtaz, Haroon and Paolo Surico (2015) “The Transmission Mechanism in Good and Bab

Times,” International Economic Review, 56 (4), 1237–1260.

Nakamura, Emi and J´on Steinsson (2018) “High-Frequency Identification of Monetary Non-

Neutrality: The Information Effect,” The Quarterly Journal of Economics, 133 (3), 1283–

1330.

Peersman, Gert and Frank Smets (2001) “Are the effects of monetary policy in the euro area

greater in recessions than in booms?” Working Paper Series 52, European Central Bank.

Perez-Quiros, Gabriel and Allan Timmermann (2000) “Firm Size and Cyclical Variations in

Stock Returns,” Journal of Finance, 55 (3), 1229–1262.

Plagborg-Møller, Mikkel and Christian K. Wolf (2021) “Local Projections and VARs Esti-

mate the Same Impulse Responses,” Econometrica, 89 (2), 955–980.

Ramey, Valerie (2016) “Macroeconomic Shocks and Their Propagation,” 2, Chap. Chapter

2, 71–162: Elsevier.

Ramey, Valerie A. and Sarah Zubairy (2018) “Government Spending Multipliers in Good

Times and in Bad: Evidence from US Historical Data,” Journal of Political Economy, 126

(2), 850–901.

Romer, Christina D. and David H. Romer (2004) “A New Measure of Monetary Shocks:

Derivation and Implications,” American Economic Review, 94 (4), 1055–1084.

Schmeling, Maik, Andreas Schrimpf, and Sigurd A.M. Steffensen (2022) “Monetary policy

expectation errors,” Journal of Financial Economics, 146 (3), 841–858.

ECB Working Paper Series No 291934Sims, Christopher (1993) “A Nine-Variable Probabilistic Macroeconomic Forecasting

Model,” in Business Cycles, Indicators, and Forecasting, 179–212: National Bureau of

Economic Research, Inc.

Sims, Christopher and Tao Zha (1998) “Bayesian Methods for Dynamic Multivariate Mod-

els,” International Economic Review, 39 (4), 949–68.

Stock, James H. and Mark Watson (2012) “Disentangling the Channels of the 2007-09 Re-

cession,” Brookings Papers on Economic Activity, 43 (1 (Spring)), 81–156.

Stock, James H. and Mark W. Watson (2018) “Identification and Estimation of Dynamic

Causal Effects in Macroeconomics Using External Instruments,” Economic Journal, 128

(610), 917–948.

Swanson, Eric (2021) “Measuring the effects of federal reserve forward guidance and asset

purchases on financial markets,” Journal of Monetary Economics, 118 (C), 32–53.

Swanson, Eric T. and Vishuddhi Jayawickrema (2021) “Speeches by the Fed Chair Are More

Important than FOMC Announcements: An Improved High-Frequency Measure of U.S.

Monetary Policy Shocks,” working paper, UC Irvine.

Tenreyro, Silvana and Gregory Thwaites (2016) “Pushing on a String: US Monetary Policy

Is Less Powerful in Recessions,” American Economic Journal: Macroeconomics, 8 (4),

43–74.

Thoma, Mark (1994) “Subsample instability and asymmetries in money-income causality,”

Journal of Econometrics, 64 (1-2), 279–306.

Weise, Charles L (1999) “The Asymmetric Effects of Monetary Policy: A Nonlinear Vector

Autoregression Approach,” Journal of Money, Credit and Banking, 31 (1), 85–108.

Wieland, Johannes F. and Mu-Jeung Yang (2020) “Financial Dampening,” Journal of

Money, Credit and Banking, 52 (1), 79–113.

Wu, Jing Cynthia and Fan Dora Xia (2016) “Measuring the Macroeconomic Impact of Mon-

etary Policy at the Zero Lower Bound,” Journal of Money, Credit and Banking, 48 (2-3),

253 – 291.

ECB Working Paper Series No 291935Figures and Tables

Figure 1: Underlying Real GDP Growth and 18-month Moving Average

(annualised and %)

Note: The state variable is calculated as zt = α(yt − yt−1) + (1 − α)zt−1, where yt is the log of real GDP and α = 0.125.
The exponential weighted moving average is shown annualised and in percent. Real GDP growth (18-m MA) is computed as
100(yt − yt−18)2/3. Sample period: Feb. 1988 - Dec. 2019.

199019952000200520102015-4-3-2-10123456Underlying real GDP growth50th percentile25th percentileReal GDP growth (18-m MA)ECB Working Paper Series No 291936Figure 2: Underlying Real GDP Growth and Orthogonolised Monetary Policy Surprises

(left: annualised and %; right: percentage points)

Note: The state variable is calculated as zt = α(yt − yt−1) + (1 − α)zt−1, where yt is the log of real GDP and α = 0.125.
The exponential weighted moving average is shown annualised and in percent. The monetary policy shocks are defined as the
residuals of an OLS equation (see column 4 of Table 1), where the monetary policy surprises are regressed on (i) nonfarm
payrolls surprises; (ii) employment growth from one year earlier to the most recent release before the FOMC announcement;
(iii) the log change on the S&P500 stock price index from three months before the FOMC announcement to the day before
the FOMC announcement; (iv) the change in the slope of the yield curve from three months before the FOMC announcement
to the day before the FOMC announcement; (v) the log change of commodity prices from three months before the FOMC
announcement to the day before the FOMC announcement; (vi) the implied skewness of the ten-year Treasury yield; (vii) the
change in the NFCI from seven weeks before the FOMC announcement to the day before the FOMC announcement as well as
on the same set of variables interacted with a dummy defining the high economic growth regime. Monetary policy surprises and
the first six factors are provided by Bauer and Swanson (2023a), the NFCI real time vintages are provided by Amburgey and
McCracken (2023). The monetary policy shocks are summed over the month and are evaluated in percentage points. Sample
period: Feb. 1988 - Dec. 2019.

199019952000200520102015-4-3-2-10123456-0.4-0.3-0.2-0.100.10.20.30.40.50.6Underlying real GDP growth (left)50th percentile (left)25th percentile (left)Orthogonalized monetary policy surprises (right)ECB Working Paper Series No 291937Figure 3: Nonlinear Impact of Monetary Policy Shocks

(response to a 25 basis points monetary policy shock)

MP shock (bps)

Shadow rate (bps)

Expected 10-yr Treasury (bps)

Panel A: Linear IRFs

Term Premium 10-yr Treasury
(bps)

Excess bond premium (bps)

Stock prices (%)

Real GDP (%)

GDP deflator (%)

MP shock (bps)

Shadow rate (bps)

Expected 10-yr Treasury (bps)

Term Premium 10-yr Treasury
(bps)

Panel B: State-Dependent IRFs

Excess bond premium (bps)

Stock prices (%)

Real GDP (%)

GDP deflator (%)

Notes: The vector Xt = [uT −mp
, bt, et, yt, pt]′ defines the endogenous variables (see text for definitions). The monetary
policy shocks are defined as the residuals of an OLS equation (see column 4 of Table 1 and notes in Figure 2). The threshold is set
at the median. Each panel shows the median IRFs and the corresponding posterior 68% credible sets (dashed red lines and shaded
bands). The red (blue) lines in Panel B are associated to the high (low) growth regime. Sample period: Feb. 1988 - Dec. 2019.

, rt, ien
t

, itpn
t

t

ECB Working Paper Series No 291938Figure 4: Nonlinear Impact of Monetary Policy Shocks on the Yield Curve

(response to a 25 basis points monetary policy shock)

Panel A: Expected interest rates (basis points)

Expected 1-yr Treasury

Expected 2-yr Treasury

Expected 3-yr Treasury

Expected 5-yr Treasury

Expected 10-yr Treasury

Term premium 1-yr
Treasury

Term premium 2-yr
Treasury

Term premium 3-yr
Treasury

Term premium 5-yr
Treasury

Term premium 10-yr
Treasury

Panel B: Term premia (basis points)

1-yr Treasury

2-yr Treasury

3-yr Treasury

5-yr Treasury

10-yr Treasury

Panel C: Interest rates (basis points)

Notes: The vector Xt = [uT −mp
and
itpn
can take 1-yr, 2-yr, 3-yr, 5-yr and 10-yr maturity. The monetary policy shocks are defined as the residuals of an OLS
t
equation (see column 4 of Table 1 and notes in Figure 2). The threshold is set at the median. Each panel shows the median
IRFs and the corresponding posterior 68% credible sets (dashed red lines and shaded bands). The red (blue) lines are associated
to the high (low) growth regime. Sample period: Feb. 1988 - Dec. 2019.

, bt, et, yt, pt]′ defines the endogenous variables (see text for definitions). ien
t

, rt, ien
t

, itpn
t

t

ECB Working Paper Series No 291939Figure 5: Nonlinear Impact of Monetary Policy Shocks on Expenditute Aggregates

(response to a 25 basis points monetary policy shock)

MP shock (bps)

Shadow rate (bps)

Real GDP (%)

GDP deflator (%)

Panel A: Linear IRFs

Nonresidential fixed business
investment volume (%)

Consumption of durables and
housing investment volume (%)

Consumption of nondurables and
services volume (%)

MP shock (bps)

Panel B: State-Dependent IRFs
Real GDP (%)

Shadow rate (bps)

GDP deflator (%)

Nonresidential fixed business
investment volume (%)

Consumption of durables and
housing investment volume (%)

Consumption of nondurables and
services volume (%)

The vector Xt = [uT −mp
t ]′ defines the endogenous variables (see text for definitions). The monetary policy shocks
are defined as the residuals of an OLS equation (see column 4 of Table 1 and notes in Figure 2). The threshold is set at the median.
Each panel shows the median IRFs and the corresponding posterior 68% credible sets (dashed red lines and shaded bands). The red
(blue) lines in Panel B are associated to the high (low) growth regime. Sample period: Q1 1988 - Q4 2019.

, rt, yt, pt, yf

t , yn

t , yd

t

ECB Working Paper Series No 291940Figure 6: Policy Surprises versus Methods in Recessions and Expansions

(response to a 25 basis points monetary policy shock)

Romer-Romer surprises

Bauer-Swanson surprises

Real GDP (%)

PCE inflation (%)

Real GDP (%)

PCE inflation (pp)

Panel A: STVAR

Real GDP (%)

PCE inflation (pp)

Real GDP (%)

PCE inflation (pp)

Panel B: TVAR

Panel C: Smooth Transition Local projections

Real GDP (%)

PCE inflation (pp)

Real GDP (%)

PCE inflation (pp)

Real GDP (%)

Panel D: Threshold Local projections
Real GDP (%)

PCE inflation (pp)

PCE inflation (pp)

t

Notes: The vector Xt = [ump
, rt, yt, πt]′ defines the endogenous variables (see text for definitions). ump
varies depending upon the
nonlinear model employed. The first and the second columns use Romer-Romer surprises and the data set of Tenreyro and Thwaites
(2016) over the quarterly sample period 1969Q1 - 2002Q4. The third and the fourth columns use Bauer-Swanson surprises and the
data set over the monthly sample period Feb. 1988 - Dec. 2019. Panels A and B provide the state-dependent responses based on
the STVAR and TVAR. Panels C and D provide the state-dependent responses based on the smooth transition and threshold local
projections. The Romer-Romer surprises are defined as the residuals of an OLS equation following Romer and Romer (2004) and
consistent with the nonlinear models. The Bauer and Swanson surprises are defined as the residuals of an OLS equation using the
factors of column 4 of Table 1 and consistent with the nonlinear models. As in Tenreyro and Thwaites (2016), the threshold is set
at the 20th percentile of the real GDP growth distribution. Each panel shows the median IRFs and the corresponding posterior 68%
credible sets. The transition to the other regime is not allowed. The red (blue) lines are associated to the high (low) growth regime.

t

-2-10101234Years-.4-.20.2.401234Years-3-2-101201234Years-.6-.4-.20.2.401234Years-1.5-1-.50.5101234Years-.2-.10.1.2.301234Years-3-2-10101234Years-.6-.4-.20.2.401234YearsECB Working Paper Series No 291941Figure 7: Romer-Romer Surprises in Recessions and Expansions

Notes: The orange solid line is the series of monetary policy surprises in Romer and Romer (2004) between March 1969
and December 1996. The black dotted line is the series of monetary policy surprise in Romer and Romer (2004) extended
to December 2002. The remaining line is the monetary policy surprises in high growth (expansions, blue) and low growth
(recessions, red) regimes. See main text for details. Sample period: Mar. 1969 - Dec. 2002.

1970198019902000-4-202Romer-Romer (2004) originalRomer-Romer (2004) extendedRomer-Romer (2004) ExpansionsRomer-Romer (2004) RecessionsECB Working Paper Series No 291942Figure 8: Robustness Checks of the Baseline Model: State-Dependent IRFs

(response to a 25 basis points monetary policy shock)

Panel A: Lower Threshold Defining the Low and High Growth Regimes

MP shock (bps)

Shadow rate (bps)

Exp. 10-yr Treasury (bps)

Term Premium 10-yr
Treasury (bps)

Excess bond premium
(bps)

Stock prices (%)

Real GDP (%)

GDP deflator (%)

Panel B: The Use of Personal Consumption Expenditure Price Index

MP shock (bps)

Shadow rate (bps)

Exp. 10-yr Treasury (bps)

Term Premium 10-yr
Treasury (bps)

Excess bond premium
(bps)

Stock prices (%)

Real GDP (%)

PCE inflation (%)

MP shock (bps)

Panel C The Use of a Flat Prior
Exp. 10-yr Treasury (bps)

Shadow rate (bps)

Term Premium 10-yr
Treasury (bps)

Excess bond premium
(bps)

Stock prices (%)

Real GDP (%)

GDP deflator (%)

Notes: Panel A provides the state-dependent IRFs when setting the threshold of underlying real GDP growth at the worst 25 percent
of the periods in the sample. Panel B provides the state-dependent IRFs when substituting the GDP deflator with PCE inflation.
Panel C provides the state-dependent IRFs when using a flat prior. See also notes of Figure 3.

ECB Working Paper Series No 291943Figure 9: State-Dependent IRFs using alternative Nonlinear Models

(response to a 25 basis points monetary policy shock)

Panel A: STVAR

Real GDP (%)

GDP deflator (%)

Panel B: Smooth transition local projections

Real GDP (%)

GDP deflator (%)

Panel C: Threshold local projections
GDP deflator (%)

Real GDP (%)

t

, rt, ien
t

Notes: The vector Xt = [ump
varies de-
pending upon the nonlinear model employed. The monetary policy shocks are defined as the residuals of an OLS equation using the
factors of column 4 of Table 1, The monetary policy shocks are obtained from Equation 11 in the case of the smooth transition model
and Equation 6 in the case of the threshold model. Both the Sigmond point of the logistic function and the threshold are set at the
median. Each panel shows the median IRFs and the corresponding posterior 68% credible sets (dashed red lines and shaded bands).
The red (blue) lines in Panel B are associated to the high (low) growth regime. Sample period: Feb. 1988 - Dec. 2019.

, bt, et, yt, pt]′ defines the endogenous variables (see text for definitions). ump

, itpn
t

t

-3-2-101201234Years-1-.50.501234Years-2-10101234Years-1-.50.501234YearsECB Working Paper Series No 291944Table 1: Predictive Regressions using Macroeconomic and Financial Data

Nonfarm payrolls

Empl. growth (12m)

∆ log S&P 500 (3m)

∆ Slope (3m)

∆ log Comm. Price (3m)

Treasury skewness

∆ NFCI (7w)

Nonfarm payrolls ×D

Empl. growth (12m) ×D

∆ log S&P 500 (3m) ×D

∆ Slope (3m) ×D

∆ log Comm. Price (3m) ×D

Treasury skewness ×D

∆ NFCI (7w) ×D

R2
Sample
N
Wald test (P-value)
Wald test on NFCI (P-value)

Linear
BS Surprises
(1)
0.094**
(2.390)
0.005**
(2.090)
0.084
(1.430)
-0.010
(-1.370)
0.119**
(2.350)
0.032***
(2.99)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.162
1988:2-2019:12
322
-
-

Linear
BS & NFCI
(2)
0.092**
(2.330)
0.004*
(1.750)
0.100
(1.630)
-0.010
(-1.380)
0.123**
(2.460)
0.033***
(3.090)
0.014
(0.620)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
0.164
1988:2-2019:12
322
-
-

Nonlinear
BS Surprises
(3)
0.108*
(1.860)
0.005
(1.560)
0.083
(1.290)
-0.017
(-1.600)
0.139**
(2.000)
0.032**
(2.030)
-
-
-0.010
(-0.120)
0.002
(0.320)
0.003
(0.020)
0.010
(0.660)
-0.052
(-0.560)
0.002
(0.090)
-
-
0.171
1988:2-2019:12
322
0.990
-

Nonlinear
BS & NFCI
(4)
0.082
(1.540)
0.002
(0.610)
0.160**
(2.400)
-0.020**
(-2.040)
0.170***
(2.870)
0.033**
(2.360)
0.066***
(3.000)
0.010
(0.120)
0.006
(1.230)
-0.144
(-1.060)
0.013
(0.850)
-0.082
(-0.980)
-0.003
(-0.170)
-0.135***
(-3.790)
0.223
1988:2-2019:12
322
0.020
0.014

Note: This table shows the coefficient estimates β and γ from the predictive regressions mpst = α0+α1×D+β′Xt−+γ′Xt−×D+ut,
where t indexes FOMC announcements. Columns 1 replicates the results of Bauer and Swanson (2023a), column 2 controls also
for the NFCI, columns 3 and 4 includes an interaction dummy D, which is equal to 1 if the underlying real GDP growth is
above the threshold (I{zt−1 ≥ z∗}). Predictors X are observed prior to the FOMC announcement: the surprise component of
the most recent nonfarm payrolls release, employment growth over the last year, the log change in the Standard & Poor’s 500
index (S&P 500) from 3 months before, to the day before, the FOMC announcement, the change in the yield curve slope over the
same period, the log change in a commodity price index over the same period and and the option-implied skewness of the 10-year
Treasury yield are obtained from Bauer and Swanson (2023a); the change in the NFCI from 9 weeks before, to the day before, the
FOMC announcement is computed using the real time data of Amburgey and McCracken (2023). Bootstrapped t-stat using 50,000
bootstrap replications are reported in parentheses. The Wald test of the joint significance of coefficients is performed on all the
interaction terms: ˆγ′ = 0. The Wald test on ∆ NFCI is a test on ˆβN F CI + ˆγN F CI = 0. ***, ** , * denote statistical significance
at 1%, 5% and 10% level.

ECB Working Paper Series No 28xx44d
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Acknowledgements 

We would like to thank Klaus Adam, Marek Jarociński and Giorgio Primiceri for helpful comments. 
The views expressed in this paper are those of the authors and do not necessarily reflect those of the European Central Bank or the 
Eurosystem. The authors declare that they have no relevant or material financial interests that relate to the research described in this 
paper. 

Roberto A. De Santis (corresponding author) 
European Central Bank, Frankfurt am Main, Germany; email: roberto.de_santis@ecb.europa.eu 

Tommaso Tornese 
Bocconi University, Milan, Italy; email: tommaso.tornese@unibocconi.it 

© European Central Bank, 2024 

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This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or 
from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found 
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ISBN 978-92-899-6399-2 

ISSN 1725-2806 

doi:10.2866/6667 

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