Transmission Lags of Monetary Policy:
A Meta-Analysis∗
Tomas Havraneka,b and Marek Rusnakb,cabResearch Department, Czech National Bank
Institute of Economic Studies, Charles University, PraguecFinancial Stability Department, Czech National Bank

The transmission of monetary policy to the economy is generally thought to have long and variable lags. In this paper
we quantitatively review the modern literature on monetarytransmission to provide stylized facts on the average lag lengthand the sources of variability. We collect sixty-seven publishedstudies and examine when prices bottom out after a monetary contraction. The average transmission lag is twenty-ninemonths, and the maximum decrease in prices reaches 0.9 percent on average after a 1-percentage-point hike in the policy rate. Transmission lags are longer in developed economies(twenty-ﬁve to ﬁfty months) than in post-transition economies(ten to twenty months). We ﬁnd that the factor most eﬀective in explaining this heterogeneity is ﬁnancial development:
greater ﬁnancial development is associated with slower transmission.
JEL Codes: C83, E52.

∗

We are grateful to Adam Elbourne, Bill Gavine, and Jakob de Haan forsending us additional data and Oxana Babecka-Kucharcukova, Marek Jarocinski,
Jacques Poot, and two anonymous referees of the International Journal of Central Banking for comments on previous versions of the manuscript. Tomas
Havranek acknowledges support from the Czech Science Foundation (grant#P402/12/G097). Marek Rusnak acknowledges support from the Grant Agencyof Charles University (grant #267011). An online appendix with data, R
and Stata code, and a list of excluded studies is available at http://metaanalysis.cz/lags/index.html. The views expressed here are ours and not necessarily those of the Czech National Bank. Corresponding author: Tomas Havranek,
tomas.havranek@cnb.cz or tomas.havranek@ies-prague.org.

39

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International Journal of Central Banking

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Introduction

Policymakers need to know how long it takes before their actions
fully transmit to the economy and what determines the speed oftransmission. A common claim about the transmission mechanismof monetary policy is that it has “long and variable” lags (Friedman
1972; Batini and Nelson 2001; Goodhart 2001). This view has been
embraced by many central banks and taken into account during
their decision making: most inﬂation-targeting central banks haveadopted a value between twelve and twenty-four months as theirpolicy horizon (see, for example, Bank of England 1999; European
Central Bank 2010). Theoretical models usually imply transmission
lags of similar length (Taylor and Wieland 2012), but the results of
empirical studies vary widely.
In this paper we quantitatively survey studies that employ vector
autoregression (VAR) methods to investigate the eﬀects of monetary
policy shocks on the price level. We refer to the horizon at which
the response of prices becomes the strongest as the transmissionlag, and collect 198 estimates from sixty-seven published studies.
The estimates of transmission lags in our sample are indeed variable, and we examine the sources of variability. The meta-analysis
approach allows us to investigate both how transmission lags diﬀer
across countries and how diﬀerent estimation methodologies within
the VAR framework aﬀect the results. Meta-analysis is a set of tools
for summarizing the existing empirical evidence; it has been regularly employed in medical research, but its application has only
recently spread to the social sciences, including economics (Stanley 2001; Disdier and Head 2008; Card, Kluve, and Weber 2010;
Havranek and Irsova 2011). By bringing together evidence from alarge number of studies that use diﬀerent methods, meta-analysis
can extract robust results from a heterogeneous literature.
Several researchers have previously investigated the crosscountry diﬀerences in monetary transmission. Ehrmann (2000)
examines thirteen member countries of the European Union andﬁnds relatively fast transmission to prices for most of the countries:
between two and eight quarters. Only France, Italy, and the United
Kingdom exhibit transmission lags between twelve and twenty quarters. In contrast, Mojon and Peersman (2003) ﬁnd that the eﬀects
of monetary policy shocks in European economies are much moreVol. 9 No. 4

Transmission Lags of Monetary Policy

41

delayed, with the maximum reaction occurring between sixteen andtwenty quarters after the shock. Concerning cross-country diﬀerences, Mojon and Peersman (2003) argue that the conﬁdence intervals are too wide to draw any strong conclusions, but they call forfurther testing of the heterogeneity of impulse responses. Boivin,
Giannoni, and Mojon (2009) update the results and conclude thatthe adoption of the euro contributed to lower heterogeneity in monetary transmission among the member countries.
Cecchetti (1999) ﬁnds that for a sample of advanced countries,
transmission lags vary between one and twelve quarters. He links
the country-speciﬁc strength of monetary policy to a number ofindicators of ﬁnancial structure but does not attempt to explain
the variation in transmission lags. In a similar vein, Elbourne andde Haan (2006) investigate ten new EU member countries and ﬁndthat the maximum eﬀects of monetary policy shocks on prices occur
between one and ten quarters after the shock. These papers typically look at a small set of countries at a speciﬁc point in time; incontrast, we collect estimates of transmission lags from a vast literature that provides evidence for thirty diﬀerent economies during
several decades. Moreover, while some of the previous studies seek toexplain the diﬀerences in the strength of transmission, they remainsilent about the factors driving transmission speed.
In this paper we attempt to ﬁll this gap and associate the diﬀerences in transmission lags with a number of country and study characteristics. Our results suggest that the transmission lags reported in
the literature really do vary substantially: the average lag, corrected
for misspeciﬁcation in some studies, is twenty-nine months, with astandard deviation of nineteen months. Post-transition economies inour sample exhibit signiﬁcantly faster transmission than advanced
economies, and the only robust country-speciﬁc determinant of thelength of transmission is the degree of ﬁnancial development. In
developed countries, ﬁnancial institutions have more opportunities to hedge against surprises in monetary policy stance, causing
greater delays in the transmission of monetary policy shocks. Concerning variables that describe the methods used by primary studies, the frequency of the data employed matters for the reported
transmission lags. Our results suggest that researchers who use
monthly data instead of quarterly data report systematically fastertransmission.

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The remainder of the paper is structured as follows. Section 2
presents descriptive evidence concerning the diﬀerences in transmission lags. Section 3 links the variation in transmission lags to thirty
country- and study-speciﬁc variables. Section 4 contains robustness
checks. Section 5 summarizes the implications of our key results.
2.

Estimating the Average Lag

We attempt to gather all published studies on monetary transmission that fulﬁll the following three inclusion criteria. First, the study
must present an impulse response of the price level to a shock in thepolicy rate (that is, we exclude impulse responses of the inﬂationrate). Second, the impulse response in the study must correspondto a 1-percentage-point shock in the interest rate, or the size of themonetary policy shock must be presented so that we can normalize the response. Third, we only include studies that present conﬁdence intervals around the impulse responses—as a simple indicatorof quality. The primary studies fulﬁlling the inclusion criteria arelisted in table 1. More details describing the search strategy can befound in a related paper (Rusnak, Havranek, and Horvath 2013),
examining which method choices are associated with reporting the“price puzzle” (the short-term increase in the price level following amonetary contraction).
After imposition of the inclusion criteria, our database contains
198 impulse responses taken from sixty-seven previously publishedstudies and provides evidence on the monetary transmission mechanism for thirty countries, mostly developed and post-transition
economies. The database is available in the online appendix (http://
meta-analysis.cz/lags/index.html). For each impulse response, we
evaluate the horizon at which the decrease in prices following themonetary contraction reaches its maximum. The literature reports
two general types of impulse responses, both of which are depicted
in ﬁgure 1. The left-hand panel shows a hump-shaped (also called
U-shaped) impulse response: prices decrease and bounce back aftersome time following a monetary policy shock; the monetary contraction stabilizes prices at a lower level or the eﬀect gradually diesout. The dashed line denotes the maximum eﬀect, and we label thecorresponding number of months passed since the monetary contraction as the transmission lag. In contrast, the right-hand panel
shows a strictly decreasing impulse response: prices neither stabilize

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Transmission Lags of Monetary Policy

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Table 1. List of Primary Studies
Andries (2008)
Anzuini & Levy (2007)
Arin & Jolly (2005)
Bagliano & Favero (1998, 1999)
Banbura, Giannone, & Reichlin (2010)
Belviso & Milani (2006)
Bernanke, Boivin, & Eliasz (2005)
Bernanke, Gertler, & Watson (1997)
Boivin & Giannoni (2009)
Borys, Horvath, & Franta (2009)
Bredin & O’Reilly (2004)
Brissimis & Magginas (2006)
Brunner (2000)
Buckle et al. (2007)
Cespedes, Lima, & Maka (2008)
Christiano, Eichenbaum, & Evans(1996, 1999)
Cushman & Zha (1997)
De Arcangelis & Di Giorgio (2001)
Dedola & Lippi (2005)
Eichenbaum (1992)
Eickmeier, Hofmann, & Worms (2009)
Elbourne (2008)
Elbourne & de Haan (2006, 2009)
European Forecasting Network (2004)
Forni & Gambetti (2010)
Fujiwara (2004)
Gan & Soon (2003)
Hanson (2004)
Horvath & Rusnak (2009)
Hulsewig, Mayer, & Wollmershauser(2006)

Jang & Ogaki (2004)
Jaroncinski (2010)
Jarocinski & Smets (2008)
Kim (2001, 2002)
Krusec (2010)
Kubo (2008)
Lagana & Mountford (2005)
Leeper, Sims, & Zha (1996)
Lange (2010)
Li, Iscan, & Xu (2010)
McMillin (2001)
Mertens (2008)
Minella (2003)
Mojon (2008)
Mojon & Peersman (2001)
Mountford (2005)
Nakashima (2006)
Normandin & Phaneuf (2004)
Oros & Roocea-Turcu (2009)
Peersman (2004, 2005)
Peersman & Smets (2001)
Peersman & Straub (2009)
Pobre (2003)
Raﬁq & Mallick (2008)
Romer & Romer (2004)
Shioji (2000)
Sims & Zha (1998)
Smets (1997)
Sousa & Zaghini (2008)
Vargas-Silva (2008)
Voss & Willard (2009)
Wu (2003)

Notes: The search for primary studies was terminated on September 15, 2010. A listof excluded studies, with reasons for exclusion, is available in the online appendix.

nor bounce back within the time frame reported by the authors(impulse response functions are usually constructed for a ﬁve-year
horizon). In this case the response of the price level becomes thestrongest in the last reported horizon, so we label the last horizon
as the transmission lag.

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Figure 1. Stylized Impulse Responses
Strictly decreasing response
0
−.5

−.5

−1

−1

−1.5

−1.5

−2

−2

Response of prices (%)

0

Hump−shaped response

0

6

12

18

24

30

36

0

6

12

18

24

30

36

Months after a 1−percentage−point increase in the interest rate

Notes: The ﬁgure depicts stylized examples of the price level’s response to a 1percentage-point increase in the policy rate. The dashed lines denote the numberof months to the maximum decrease in prices.

Researchers often discuss the number of months to the maximum
decrease in prices in the case of hump-shaped impulse responses. On
the other hand, researchers rarely interpret the timing of the maximum decrease in prices for strictly decreasing impulse responses,
as the implied transmission lag often seems implausibly long. Moreover, a strictly decreasing response may indicate non-stationarity ofthe estimated VAR system (Lütkepohl 2005). Nevertheless, we do
not limit our analysis to hump-shaped impulse responses since bothtypes are commonly reported: in the data set we have 100 estimatesof transmission lags taken from hump-shaped impulse responses and98 estimates taken from strictly decreasing impulse responses. We
do not prefer any particular shape of the impulse response and focus
on inference concerning the average transmission lag, but we additionally report results corresponding solely to hump-shaped impulse
responses.
Figure 2 depicts the kernel density plot of the collected estimates;
the ﬁgure demonstrates that the transmission lags taken from humpshaped impulse responses are, on average, substantially shorter thanthe lags taken from strictly decreasing impulse response functions.
Numerical details on summary statistics are reported in table 2.
The average of all collected transmission lags is 33.5 months, but
the average reaches 49.1 months for transmission lags taken fromstrictly decreasing impulse responses and 18.2 months for humpshaped impulse responses. In other words, the decrease in prices

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Transmission Lags of Monetary Policy

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.015
.005

.01

Density.02.025

Figure 2. Kernel Density of the EstimatedTransmission Lags

0

20

40

60

Transmission lags (in months)

Notes: The ﬁgure is constructed using the Epanechnikov kernel function. The
solid vertical line denotes the average number of months to the maximum decreasein prices taken from all the impulse responses. The dashed line on the left denotes
the average taken from the hump-shaped impulse responses. The dashed line on
the right denotes the average taken from the strictly decreasing impulse response
functions.

Table 2. Summary Statistics of the EstimatedTransmission Lags
VariableObs. Mean Median Std. Dev. Min. Max.

Estimates from all
Impulse Responses
Hump-Shaped Impulse
Responses
Strictly Decreasing
Impulse Responses

198

33.5

37

19.4

1

60

100

18.2

15

14.1

1

57

98

49.1

48

8.6

24

60

following a monetary contraction becomes the strongest, on average, after two years and three quarters. Our data also suggest thatthe average magnitude of the maximum decrease in prices following
a 1-percentage-point increase in the policy rate is 0.9 percent (for a

46International Journal of Central Banking

December 2013

Table 3. Transmission Lags Diﬀer across Countries
EconomyAverage Transmission Lag
Developed Economies
42.2
48.4
51.3
33.4
40.4
51.3
26.6

United States
Euro Area
Japan
Germany
United Kingdom
FranceItaly
Post-Transition Economies
PolandCzech RepublicHungary
Slovakia
Slovenia

18.7
14.8
17.9
10.7
17.6

Notes: The table shows the average number of months to the maximum decrease in
prices taken from all the impulse responses reported for the corresponding country.
We only show results for countries for which the literature has reported at least ﬁve
impulse responses.

detailed meta-analysis of the strength of monetary transmission at
diﬀerent horizons, see Rusnak, Havranek, and Horvath 2013).
The average of 33.5 is constructed based on data for thirty diﬀerent countries. To investigate whether transmission lags vary acrosscountries, we report country-speciﬁc averages in table 3 (we only
show results for countries for which we have collected at least ﬁve
observations from the literature). We divide the countries into twogroups: developed economies and post-transition economies.1 From
the table it is apparent that developed countries display much longer
transmission lags than post-transition countries. The developed
country with the fastest transmission of monetary policy actions is
1
The deﬁnition of the two groups is somewhat problematic. The Czech Republic, for example, has been considered a developed economy by the World Bank
since 2006. We include the country in the second group because pre-2006 timeseries constitute the bulk of the data used by studies in our sample.

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Italy: the corresponding transmission lag reaches 26.6 months. The
slowest transmission is found for Japan and France, with a transmission lag equal to 51.3 months. In general, the transmission lagsfor developed countries seem to vary between approximately twentyﬁve and ﬁfty months. These values sharply contrast with the resultsfor post-transition countries, where all reported transmission lags lie
between ten and twenty months. The result is in line with Jarocinski (2010), who investigates cross-country diﬀerences in transmission
and ﬁnds that post-communist economies exhibit faster transmission
than Western European countries. We examine the possible sourcesof the cross-country heterogeneity in the next section.

3.

Explaining the Diﬀerences

Two general reasons may explain why the reported transmission
lags vary: First, structural diﬀerences across countries may cause
genuine diﬀerences in the speed of transmission. Second, characteristics of the data and other aspects of the methodology employed in
the primary studies, such as speciﬁcation and estimation characteristics, may have a systematic inﬂuence on the reported transmissionlag.
We collected thirty-three potential explanatory variables. Several
structural characteristics that may account for cross-country diﬀerences in the monetary transmission mechanism have been suggested
in the literature (Dornbusch et al. 1998; Cecchetti 1999; Ehrmann
et al. 2003). Therefore, to control for these structural diﬀerences weinclude GDP per Capita to represent the country’s overall level ofthe development, GDP Growth and Inﬂation to reﬂect other macroeconomic conditions in the economy, Financial Development to capture the importance of the ﬁnancial structure, Openness to cover theexchange rate channel of the transmission mechanism, and Central
Bank Independence to capture the inﬂuence of the institutional setting and credibility on monetary transmission. These variables arecomputed as averages over the periods that correspond to the estimation periods of the primary studies. The sources of the data forthese variables are Penn World Tables, the World Bank’s World
Development Indicators, and the International Monetary Fund’sInternational Financial Statistics; the central bank independence48

International Journal of Central Banking

December 2013index is extracted from Arnone et al. (2009). We also include variables that control for data, methodology, and publication characteristics of the primary studies. The deﬁnitions of the variables areprovided in table 4 together with their summary statistics.
Rather than estimating a regression with an ad hoc subset of
explanatory variables, we formally address the model uncertainty
inherent in meta-analysis (in other words, many method variables
may be important for the reported speed of transmission, but no theory helps us select which ones). There are at least two drawbacks tousing simple regression in situations where many potential explanatory variables exist. First, if we put all potential variables into one

Table 4. Description and Summary Statistics ofExplanatory Variables
Variable

Description

Mean

Std. Dev.

9.880

0.415

2.644

1.042

0.078

0.145

0.835

0.408

0.452

0.397

0.773

0.145

Monthly= 1 if monthly data are used.
0.626
No. of Observations The logarithm of the number of
4.876
observations used.
Average Year
The average year of the data used −9.053(2000 as a base).

0.485
0.661

Country Characteristics
GDP per Capita
GDP Growth
Inﬂation
Financial Dev.

Openness

CB Independence

The logarithm of the country’s
real GDP per capita.
The average growth rate of thecountry’s real GDP.
The average inﬂation of thecountry.
The ﬁnancial development of thecountry measured by (domestic
credit to private sector)/GDP.
The trade openness of the country measured by (exports +imports)/GDP.
A measure of central bankindependence (Arnone et al.
2009)
Data Characteristics

7.779(continued)

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Table 4. (Continued)
Variable

Description

Mean

Std. Dev.

0.172

0.378

0.293

0.456

0.614

0.373

0.626

0.485

0.545

0.499

0.444

0.498

0.131
0.146

0.339
0.354

1.748

0.391

0.429

0.496

0.030

0.172

0.121

0.327

0.121

0.327

0.051

0.220

0.313

0.465

0.152

0.359

Speciﬁcation Characteristics
GDP Deﬂator

Single Regime

No. of Lags

Commodity Prices
Money
Foreign Variables
Time Trend
Seasonal
No. of Variables

Industrial Prod.

Output Gap
Other Measures= 1 if the GDP deﬂator is usedinstead of the consumer priceindex as a measure of prices.
= 1 if the VAR is estimated overa period of a single monetary
policy regime.
The number of lags in the model,
normalized by frequency:
lags/frequency.
= 1 if a commodity price index isincluded.
= 1 if a monetary aggregate isincluded.
= 1 if at least one foreign variable
is included.
= 1 if a time trend is included.
= 1 if seasonal dummies areincluded.
The logarithm of the number ofendogenous variables includedin the VAR.
= 1 if industrial production isused as a measure of economic
activity.
= 1 if the output gap is used as ameasure of economic activity.
= 1 if another measure of economic activity is used (employment, expenditures).
Estimation Characteristics

BVAR
FAVAR
SVAR
Sign Restrictions= 1 if a Bayesian VAR is estimated.
= 1 if a factor-augmented VAR isestimated.
= 1 if non-recursive identiﬁcation
is employed.
= 1 if sign restrictions are
employed.

(continued)

50International Journal of Central Banking

December 2013

Table 4. (Continued)
Variable

Description

Mean

Std. Dev.

0.495

0.501

0.530

0.500

1.875

1.292

0.900

2.417

0.424

0.495

0.061

0.239

0.449

0.499

4.894

3.889

Publication Characteristics
Strictly Decreasing

Price Puzzle
Study Citations

ImpactCentral Banker
Policymaker

NativePublication YearThe reported impulse response
function is strictly decreasing(that is, it shows the maximum
decrease in prices in the last
displayed horizon).
The reported impulse response
exhibits the price puzzle.
The logarithm of [(Google
Scholar citations of thestudy)/(age of the study) + 1].
The recursive RePEc impact
factor of the outlet.
= 1 if at least one co-author isaﬃliated with a central bank.
= 1 if at least one co-author isaﬃliated with a Ministry of
Finance, IMF, OECD, or BIS.
= 1 if at least one co-author
is native to the investigatedcountry.
The year of publication (2000 as abase).

Note: The sources of data for country characteristics are Penn World Tables, theWorld Bank’s World Development Indicators, and the International Monetary Fund’sInternational Financial Statistics.

regression, the standard errors get inﬂated since many redundantvariables are included. Second, sequential testing (or the “general-tospeciﬁc” approach) brings about the possibility of excluding relevant
variables.
To address these issues, Bayesian model averaging (BMA) is
employed frequently in the literature on the determinants of economic growth (Fernandez, Ley, and Steel 2001; Sala-I-Martin,
Doppelhofer, and Miller 2004; Durlauf, Kourtellos, and Tan 2008;
Feldkircher and Zeugner 2009; Eicher, Papageorgiou, and Raftery
2011). Recently, BMA has been used to address other questions as

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well (see Moral-Benito 2011 for a survey). The idea of BMA is togo through all possible combinations of regressors and weight themaccording to their model ﬁt. BMA thus provides results robust tomodel uncertainty, which arises when little or nothing is known
ex ante about the correct set of explanatory variables. An accessible introduction to BMA can be found in Koop (2003); technicaldetails concerning the implementation of the method are provided
by Feldkircher and Zeugner (2009).
Because we consider thirty-three potential explanatory variables,
it is not technically feasible to enumerate all 233 of their possible combinations; on a typical personal computer this would takeseveral months. In such cases, Markov chain Monte Carlo methods are used to go through the most important models. We employ
the priors suggested by Eicher, Papageorgiou, and Raftery (2011),
who recommend using the uniform model prior and the unit information prior for the parameters, since these priors perform well in
forecasting exercises. Following Fernandez, Ley, and Steel (2001),
we run the estimation with 200 million iterations, ensuring a good
degree of convergence. Appendix 1 provides diagnostics of our BMA
estimation; the online appendix provides R and Stata codes.
The results of the BMA estimation are reported graphically inﬁgure 3. The columns represent individual regression models where
the transmission lag is regressed on variables for which the corresponding cell is not blank. For example, the explanatory variablesin the ﬁrst model from the left are Financial Development, Strictly
Decreasing, Monthly, CB Independence, Impact, and Price Puzzle.
The width of the columns is proportional to the so-called posterior
model probabilities; that is, it captures the weight each model gets in
the BMA exercise. The ﬁgure only shows the 5,000 models with thehighest posterior model probabilities. The best models are displayedon the left-hand side and are relatively parsimonious compared withthose with low posterior model probabilities. Explanatory variablesin the ﬁgure are displayed in descending order according to theirposterior inclusion probabilities (the sum of the posterior probabilities of the models they are included in). In other words, the variablesat the top of the ﬁgure are robustly important for the explanationof the variation in transmission lags, whereas the variables at thebottom of the ﬁgure do not matter much.
The color (shading in the print version of this paper) of thecell corresponding to each variable included in a model represents

International Journal of Central Banking

Notes: Response variable: transmission lag (the number of months to the maximum decrease in prices taken from the impulse
responses). Columns denote individual models; variables are sorted by posterior inclusion probability in descending order. Bluecolor (darker in grayscale) = the variable is included and the estimated sign is positive. Red color (lighter in grayscale) = thevariable is included and the estimated sign is negative. No color = the variable is not included in the model. The horizontal
axis measures cumulative posterior model probabilities. Only the 5,000 models with the highest posterior model probabilities
are shown.

Figure 3. Bayesian Model Averaging, Model Inclusion
52
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the estimated sign of the regression parameter. Blue (darker ingrayscale) denotes a positive sign, and red (lighter in grayscale)denotes a negative sign. For example, in the ﬁrst model from theleft the estimated regression sign is positive for Financial Development, positive for Strictly Decreasing, negative for Monthly, positive
for CB Independence, negative for Impact, and positive for Price
Puzzle. As can be seen from the ﬁgure, variables with high posterior inclusion probabilities usually exhibit quite stable regression
signs. Nevertheless, for a more precise discussion of the importanceof individual variables (analogous to statistical signiﬁcance in thefrequentist case), we need to turn to the numerical results of the
BMA estimation, reported in table 5.
Table 5 shows the posterior means (weighted averages of themodels displayed in ﬁgure 3) for all regression parameters andthe corresponding posterior standard deviations. According to
Masanjala and Papageorgiou (2008), variables with the ratio of theposterior mean to the posterior standard deviation larger than 1.3
can be considered eﬀective (or “statistically signiﬁcant” in the frequentist case). There are only three such variables: Financial Development, Monthly, and Strictly Decreasing. First, our results suggestthat a higher degree of ﬁnancial development in the country is associated with slower transmission of monetary policy shocks to the pricelevel. Moreover, when researchers use monthly data in the VAR system, they are more likely to report shorter transmission lags. The
BMA exercise also corroborates that the transmission lags takenfrom strictly decreasing impulse responses are much longer than thelags taken from hump-shaped impulse responses; the diﬀerence isapproximately twenty-six months.
While many of the method characteristics appear to be relatively
unimportant for the explanation of the reported transmission lags, afew (for example, Sign Restrictions or Output Gap) have moderate
posterior inclusion probabilities. Because some of the method choices
are generally considered misspeciﬁcations in the literature, we use
the results of the BMA estimation to ﬁlter out the eﬀects of thesemisspeciﬁcations from the average transmission lag. In other words,
we deﬁne an ideal study with “best-practice” methodology and maximum publication characteristics (for example, the impact factor andthe number of citations). Then we plug the chosen values of the54International Journal of Central Banking

December 2013

Table 5. Why Do Transmission Lags Vary?

Variable

PIP

Posterior
Mean

PosteriorStd. Dev.

Standardized
Coef.

Country Characteristics
GDP per Capita
GDP Growth
Inﬂation
Financial Dev.
Openness
CB Independence

0.099
0.087
0.053
1.000
0.029
0.705

−0.4470.111−0.33712.492−0.05613.370

1.647
0.444
1.918
3.166
0.631
10.412−0.00960.0059−0.0025
0.2630−0.00110.1002

Data Characteristics
MonthlyNo. of Observations
Average Year

0.730
0.127
0.032

−4.175−0.362
0.003

3.036
1.136
0.030

−0.1045−0.01230.0012

Speciﬁcation Characteristics
GDP Deﬂator
Single Regime
No. of Lags
Commodity Prices
Money
Foreign Variables
Time Trend
Seasonal
No. of Variables
Industrial Prod.
Output Gap
Other Measures

0.035
0.031
0.023
0.022
0.026
0.030
0.472
0.020
0.028
0.025
0.189
0.059

−0.0520.039
0.014−0.009−0.0110.039
3.681−0.0040.036
0.008−1.464
0.199

0.584
0.395
0.436
0.246
0.286
0.385
4.480
0.307
0.400
0.352
3.566
1.038

−0.00100.00090.0003−0.0002−0.00030.00100.0643−0.0001
0.00070.0002−0.0130
0.0034

Estimation Characteristics
BVAR
FAVAR
SVAR
Sign Restrictions

0.096
0.068
0.153
0.200

0.337
0.304
−0.468
0.954

1.278
1.444
1.303
2.232

0.00570.0034−0.01120.0177(continued)

Vol. 9 No. 4

Transmission Lags of Monetary Policy

55

Table 5. (Continued)

Variable

PIP

Posterior Posterior Standardized
MeanStd. Dev.
Coef.

Publication Characteristics
Strictly Decreasing
Price Puzzle
Study Citations
ImpactCentral Banker
Policymaker
NativePublication Year

1.000
0.383
0.039
0.423
0.044
0.149
0.091
0.048

26.1221.359−0.005−0.3050.075
0.858−0.221
0.011

1.798
1.999
0.205
0.414
0.497
2.426
0.865
0.070

0.67570.0351−0.0003−0.03810.00190.0106−0.0057
0.0022

Constant

1.000

7.271

NA

0.3752

Notes: Estimated by Bayesian model averaging. Response variable: transmissionlag (the number of months to the maximum decrease in prices taken from impulse
responses). PIP = posterior inclusion probability. The posterior mean is analogous
to the estimate of the regression coeﬃcient in a standard regression; the posterior
standard deviation is analogous to the standard error of the regression coeﬃcient ina standard regression. Variables with posterior mean larger than 1.3 posterior standard deviations are typeset in bold; we consider such variables eﬀective (following
Masanjala & Papageorgiou 2008).

explanatory variables into the results of the BMA estimation andevaluate the implied transmission lag.
For the deﬁnition of the “ideal” study we prefer the use of more
observations in the VAR system (that is, we plug in the sample maximum for variable No. of Observations), more recent data (Average
Year ), the estimation of the VAR system over a period of a single
monetary policy regime (Single Regime), the inclusion of commodity prices in the VAR system (Commodity Prices), the inclusion offoreign variables (Foreign), the inclusion of seasonal dummies (Seasonal ), the inclusion of more variables in the VAR (No. of Variables),the use of the output gap as a measure of economic activity (Output Gap; Industrial Production and Other Measures are set to zero),the use of Bayesian VAR (BVAR), the use of sign restrictions (Sign
Restrictions; FAVAR and SVAR are set to zero), more citations of
the study (Study Citations), and a higher impact factor (Impact).
All other variables are set to their sample means.

56International Journal of Central Banking

December 2013

The average transmission lag implied by our deﬁnition of theideal study is 29.2 months, which is less than the simple average
by approximately 4 months. The estimated transmission lag hardly
changes when FAVAR or SVAR are chosen for the deﬁnition of bestpractice methodology; the result is also robust to other marginalchanges to the deﬁnition. On the other hand, the implied transmission lag decreases greatly if one prefers hump-shaped impulse
responses: in this case the estimated value is only 16.3 months.
Moreover, if one prefers impulse responses that do not exhibit theprice puzzle, the implied value diminishes by another month. In sum,when the eﬀect of misspeciﬁcations is ﬁltered out and one does notprefer any particular type of impulse response, our results suggestthat prices bottom out approximately two-and-a-half years after amonetary contraction.

4.

Robustness Checks and Additional Results

Our analysis, based on the results of BMA, attributes the differences in transmission lags between (and within) developed andpost-transition countries to diﬀerences in the level of ﬁnancial development. The BMA exercise carried out in the previous section controls for methodology and other aspects associated with estimating
impulse responses. Nevertheless, it is still useful to illustrate that thediﬀerences in results between developed and post-transition countries are not caused by diﬀerences in the frequency of reporting
strictly decreasing impulse responses or impulse responses showing
the price puzzle. To this end, we replicate table 3 but only focus on
the sub-samples of impulse responses that are hump shaped (table 6)or that do not exhibit the price puzzle (table 7).
The tables show that developed countries exhibit longer transmission lags even if strictly decreasing impulse responses or impulse
responses showing the price puzzle are disregarded. But the difference is smaller for the sub-sample of hump-shaped impulse
responses, where some developed countries (for example, Italy)
exhibit shorter transmission lags than some post-transition countries(for example, Poland). There are two potential explanations of thisresult. First, compared with table 3, now we only have approximately
half the number of observations, and for some countries we are evenVol. 9 No. 4

Transmission Lags of Monetary Policy

57

Table 6. Transmission Lags Diﬀer across Countries(hump-shaped impulse responses)
EconomyAverage Transmission Lag
Developed Economies

United States
Euro Area
Japan
Germany
United Kingdom
France
Italy

23.2
39.5
40.5
19.4
10.0
24.0
9.2
Post-Transition Economies

PolandCzech RepublicHungary
Slovakia
Slovenia

15.4
14.8
14.4
5.0
13.0

Notes: The table shows the average number of months to the maximum decreasein prices taken from the impulse responses reported for the corresponding country.
Strictly decreasing impulse responses are omitted from this analysis.

left with less than ﬁve impulse responses, which makes the average number imprecise. Second, strictly decreasing impulse responses,
which are associated with longer transmission lags, are more often
reported for developed economies than for post-transition economies.
The reason is that shorter data spans are available for post-transition
countries, which makes researchers often choose monthly data. Since
monthly data are associated with shorter reported lags, researchersinvestigating monetary transmission in post-transition countries areless likely to report strictly decreasing impulse responses. Nevertheless, in the BMA estimation we control for data frequency as well as
for the shape of the impulse response, and ﬁnancial development
still emerges as the most important factor causing cross-country
diﬀerences in transmission lags.
In our baseline model from the previous section we combine
data from hump-shaped and strictly decreasing impulse response58International Journal of Central Banking

December 2013

Table 7. Transmission Lags Diﬀer across Countries(responses not showing the price puzzle)
EconomyAverage Transmission Lag
Developed Economies

United States
Euro Area
Japan
Germany
United Kingdom
France
Italy

40.5
49.2
57.0
34.5
10.0
52.8
30.0
Post-Transition Economies

PolandCzech RepublicHungary
Slovakia
Slovenia

14.0
8.8
15.4
10.7
17.8

Notes: The table shows the average number of months to the maximum decreasein prices taken from the impulse responses reported for the corresponding country.
Impulse responses exhibiting the price puzzle are omitted from this analysis.

functions. For strictly decreasing impulse responses, however, ourdeﬁnition of the transmission lag (the maximum eﬀect of a monetarycontraction on prices) is inﬂuenced by the reporting window chosen
by researchers. To see whether the result concerning ﬁnancial development is robust to omitting data from strictly decreasing impulse
response functions, we repeat the BMA estimation from the previous
section using a sub-sample of hump-shaped impulse responses.
The results are presented graphically in ﬁgure 4. The variable corresponding to ﬁnancial development retains its estimated sign fromthe baseline model and still represents the most important countrylevel factor explaining the diﬀerences in monetary transmission lags.
Compared with the baseline model, in this speciﬁcation additional
method variables seem to be important. The use of other measuresthan GDP, the output gap, or industrial production as a proxy foreconomic activity is associated with slower reported transmission.

Transmission Lags of Monetary Policy

Notes: Response variable: transmission lag (the number of months to the maximum decrease in prices taken from the impulse
responses). Only transmission lags from hump-shaped impulse responses are included in the estimation. Columns denote individual models; variables are sorted by posterior inclusion probability in descending order. Blue color (darker in grayscale) =the variable is included and the estimated sign is positive. Red color (lighter in grayscale) = the variable is included and theestimated sign is negative. No color = the variable is not included in the model. The horizontal axis measures cumulative
posterior model probabilities. Only the 5,000 models with the highest posterior model probabilities are shown.

Figure 4. Bayesian Model Averaging, Model Inclusion (hump-shaped impulse responses)
Vol. 9 No. 4
59

60

International Journal of Central Banking

December 2013

The choice to represent prices by the GDP deﬂator instead of theconsumer price index on average translates into longer transmission lags. Also, the inclusion of foreign variables in the VAR system
makes researchers report slower transmission.
By excluding all strictly decreasing impulse responses, however,
we lose half of the information contained in our data set. For thisreason we consider a second way of taking into account the eﬀect of
the reporting window: censored regression. The reporting window ofprimary studies is often set to ﬁve years, so we use sixty months as
the upper limit and estimate the regression using the Tobit model.
(Changing the upper limit to three or four years, the amount oftime sometimes used as the reporting window, does not qualitatively aﬀect the results.) Unfortunately, it is cumbersome to estimate
Tobit using BMA. Thus, we estimate a general model with all potential explanatory variables and then employ the general-to-speciﬁc
approach. The general model is reported in table 12 in appendix 2.
The inclusion of all potential explanatory variables, many of which
may not be important for explanation of the diﬀerences in transmission lags, inﬂates the standard errors of the relevant variables.
Hence, in the next step we eliminate the insigniﬁcant variables one
by one, starting from the least signiﬁcant variable. As mentioned
before, the general-to-speciﬁc approach is far from perfect—but inthis case it represents an easy alternative to BMA.
The results presented in table 8 and table 12 corroborate that,even using this methodology, ﬁnancial development is highly important for the explanation of transmission lags; in both speciﬁcations
it is signiﬁcant at the 1 percent level. The use of monthly data is
associated with faster reported transmission, which is also consistent
with the baseline model. In line with our results from the previous
sections, table 8 suggests that impulse responses exhibiting the pricepuzzle are likely to show longer transmission lags. In contrast to thebaseline model, some other variables seem to be important as well:
GDP per Capita, Inﬂation, and Openness, among others. Because,
however, the results concerning these variables are not conﬁrmed byother speciﬁcations, we do not want to put much emphasis on thesevariables. The variable Strictly Decreasing, which was crucial forthe baseline BMA estimation, is omitted from the present analysis
because it deﬁnes the censoring process.

Vol. 9 No. 4

Transmission Lags of Monetary Policy

61

Table 8. Censored Regression, Speciﬁc Model
Response Variable: Transmission Lag
GDP per Capita
Price Puzzle
Inﬂation
Financial Dev.
Openness
CB IndependenceMonthlyNo. of Observations
Policymaker
Constant
Observations−11.48∗∗4.667∗∗−17.25∗∗21.61∗∗∗−12.67∗∗∗29.38∗∗∗−12.04∗∗∗6.526∗∗
12.37∗∗
86.58∗∗(4.793)(2.343)(8.739)(5.375)(4.670)(10.64)(3.821)(2.951)(5.012)(43.69)198

Notes: Standard errors in parentheses. Estimated by Tobit with the upper limit fortransmission lags equal to sixty months. The speciﬁc model is a result of the backward stepwise regression procedure applied to the general model, which is reported inappendix 2 (the cut-oﬀ level for p-values was 0.1). ***, **, and * denote signiﬁcanceat the 1 percent, 5 percent, and 10 percent levels, respectively.

So far we have analyzed the time it takes before a monetary contraction translates into the maximum eﬀect on the price level. The
extent of the maximum eﬀect, however, varies a lot across diﬀerent impulse responses. Therefore, as a complement to the previous
analysis, we collect data on how long it takes before a 1-percentagepoint increase in the policy rate leads to a decrease in the price levelof 0.1 percent. This number was chosen because most of the impulse
response functions in our sample (173 out of 198) reach this levelat some point. In contrast, if we chose a value of 0.5 percent, forexample, we would have to disregard almost two-thirds of all theimpulse responses.
The results of the BMA estimation using the new response
variable are reported in ﬁgure 5. Again, the shape of the impulse
response and the frequency of the data used in the VAR systemseem to be associated with the reported transmission lag. Financial
development still belongs among the most important country-levelvariables, together with central bank independence and trade openness. According to this speciﬁcation, monetary transmission is fasterin countries that are more open to international trade and that haveInternational Journal of Central Banking

Notes: Response variable: the number of months to a –0.1 percent decrease in prices following a 1-percentage-point increase in
the policy rate. Columns denote individual models; variables are sorted by posterior inclusion probability in descending order.
Blue color (darker in grayscale) = the variable is included and the estimated sign is positive. Red color (lighter in grayscale) =the variable is included and the estimated sign is negative. No color = the variable is not included in the model. The horizontal
axis measures cumulative posterior model probabilities. Only the 5,000 models with the highest posterior model probabilities
are shown.

Figure 5. Bayesian Model Averaging, Model Inclusion (time to –0.1 percent
decrease in prices)62
December 2013

Vol. 9 No. 4

Transmission Lags of Monetary Policy

63

a more independent central bank; these results may point at theimportance of the exchange rate and expectation channels of monetary transmission. Additionally, some method variables matter forthe estimated transmission lag: for example, the use of sign restrictions, structural VAR, and seasonal adjustment. Our results alsosuggest that articles published in journals with a high impact factor
tend to present faster monetary transmission.
5.

Concluding Remarks

Building on a sample of sixty-seven previous empirical studies, we
examine why the reported transmission lags of monetary policy vary.
Our results suggest that the cross-country variation in transmission
is robustly associated with diﬀerences in ﬁnancial development. To
explain the variation of results between diﬀerent studies for the same
country, the frequency of the data used is important: the use ofmonthly data makes researchers report transmission faster by fourmonths, holding other things constant. This is in line with Ghysels(2012), who shows that responses from low- and high-frequency
VARs may indeed diﬀer due to mixed-frequency sampling or temporal aggregation of shocks. The shape of the impulse response matters
as well. Strictly decreasing impulse responses, which may suggestthat the underlying VAR system is not stationary, exhibit much
longer transmission lags.
The key result of our meta-analysis is that a higher degree ofﬁnancial development translates into slower transmission of monetary policy. The ﬁnding can be interpreted in the following way.
If ﬁnancial institutions lack opportunities to protect themselves
against unexpected monetary policy actions (due to either low levels of capitalization or low sophistication of ﬁnancial instruments
provided by the undeveloped ﬁnancial system), they need to react
immediately to monetary policy shocks, thus speeding up the transmission. In ﬁnancially developed countries, in contrast, ﬁnancial
institutions have more opportunities to hedge against surprises inmonetary policy stance, causing greater delays in the transmissionof monetary policy shocks.
More generally, our results imply that monetary transmission
may slow down as the ﬁnancial system of emerging countries
develops, since ﬁnancial innovations allow banks to protect better
against surprise shocks in monetary policy.

64International Journal of Central Banking

December 2013

Appendix 1. Diagnostics of Bayesian Model Averaging

Table 9. Summary of BMA Estimation (baseline model)
Mean No. Regressors
8.1261No. Models Visited
83,511,152
Corr. PMP
0.9999Shrinkage Stats
Av. = 0.995

Draws
2 · 108
Modelspace
8.6 · 109
No. Obs.
198

Burn-Ins
1 · 108
Visited
0.97%
Model Prior
Uniform/16.5

Time
11.88852 hours
Topmodels34%
g-Prior
UIP

Note: UIP = unit information prior, PMP = posterior model probability.

Figure 6. Model Size and Convergence (baseline model)

0.20

Posterior Model Size Distribution
Mean: 8.1261

Prior

0.00

0.10

Posterior

0

2

4

6

8 10

13

16

19

22

25

28

31

Model Size

Posterior Model Probabilities(Corr: 0.9999)

PMP (Exact)

0.000

0.006

PMP (MCMC)

0

1000

2000

3000

Index of Models

4000

5000

Vol. 9 No. 4

Transmission Lags of Monetary Policy

65

Table 10. Summary of BMA Estimation (hump-shaped
impulse responses)
Draws
2 · 108
Modelspace
4.3 · 109
No. Obs.
100

Mean No. Regressors
10.7143
No. Models Visited
104,093,439
Corr. PMP
0.9997
Shrinkage Stats
Av. = 0.9901

Burn-Ins
1 · 108
Visited
2.4%
Model Prior
Uniform/16

Time
12.15215 hours
Topmodels16%
g-Prior
UIP

Note: UIP = unit information prior, PMP = posterior model probability.

Figure 7. Model Size and Convergence (hump-shaped
impulse responses)
Posterior Model Size Distribution
Mean: 10.7143

Prior

0.00

0.10

Posterior

0

2

4

6

8

10

13

16

19

22

25

28

31

Model Size

Posterior Model Probabilities(Corr: 0.9997)

PMP (Exact)

0.000

0.003

PMP (MCMC)

0

1000

2000

3000

Index of Models

4000

5000

66

International Journal of Central Banking

December 2013

Table 11. Summary of BMA Estimation (time to−0.1 percent decrease in prices)Draws
2 · 108
Modelspace
8.6 · 109
No. Obs.
173

Mean No. Regressors
9.6899No. Models Visited
87,125,827
Corr. PMP
0.9999Shrinkage Stats
Av. = 0.9943

Burn-Ins
1 · 108
Visited1%Model Prior
Uniform/16.5

Time
12.0976 hours
Topmodels30%
g-Prior
UIP

Note: UIP = unit information prior, PMP = posterior model probability.

Figure 8. Model Size and Convergence (time to –0.1
percent decrease in prices)

0.20

Posterior Model Size Distribution
Mean: 9.6899

Prior

0.00

0.10

Posterior

0

2

4

6

8 10

13

16

19

22

25

28

31

Model Size

Posterior Model Probabilities(Corr: 0.9999)

PMP (Exact)

0.000

0.004

PMP (MCMC)

0

1000

2000

3000

Index of Models

4000

5000

Vol. 9 No. 4

Transmission Lags of Monetary Policy

67

Appendix 2. Results of Censored Regression

Table 12. Censored Regression, General Model(all variables are included)
Response Variable: Transmission Lag
Country Characteristics
GDP per Capita
GDP Growth
Inﬂation
Financial Dev.
Openness
CB Independence−9.792∗1.512−17.41∗∗22.17∗∗∗−11.16∗∗30.20∗∗(5.192)(1.346)(8.695)(6.084)(5.595)(12.27)

Data Characteristics
MonthlyNo. of Observations
Average Year−4.4024.287−0.168(6.920)(5.186)(0.367)

Speciﬁcation Characteristics
GDP Deﬂator
Single Regime
No. of Lags
Commodity Prices
Money
Foreign Variables
Time Trend
Seasonal
No. of Variables
Industrial Prod.
Output Gap
Other Measures

5.102
4.143
8.132∗−1.284
1.768
4.102
2.700
7.231∗
1.352−6.785∗−10.41−6.246(4.281)(3.497)(4.744)(2.861)(2.949)(3.400)(5.791)(4.057)(3.536)(3.904)(7.681)(5.017)

Estimation Characteristics
BVAR
FAVAR
SVAR
Sign Restrictions−1.14714.53∗∗−4.243−3.270(5.094)(6.525)(3.008)(5.163)(continued)

68International Journal of Central Banking

December 2013

Table 12. (Continued)
Response Variable: Transmission Lag
Publication Characteristics
Price Puzzle
Study Citations
ImpactCentral Banker
Policymaker
NativePublication Year
Constant
Observations

3.651−0.717−0.7425.313
9.024−1.9960.047562.32(2.537)(1.734)(0.699)(3.633)(6.137)(3.043)(0.453)(50.10)198

Notes: Standard errors in parentheses. Estimated by Tobit with the upper limit fortransmission lags equal to sixty months. ***, **, and * denote signiﬁcance at the 1
percent, 5 percent, and 10 percent levels, respectively.

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