Monetary Policy Shocks: Data or Methods?

1.1 Connection to the Literature

While there are numerous approaches to identifying exogenous variation in monetary policy, we focus on four commonly used high-frequency series and three that interchange data and methods. All seven series rely on asset price data that are either at an intraday or a daily frequency and are constructed either directly from raw data or from simple statistical procedures. The data used in construction consist of short-term futures and the full term structure of Treasury yields. Although there are complementary non-high frequency approaches and add-on techniques that further purge high-frequency series from contamination, we focus on four core series to highlight their differences and similarities as simply as possible. In a similar appeal to simplicity, we follow Bauer and Swanson (2022) and focus on shock series that summarize monetary policy in a single series rather than multiple dimensions. As Bauer and Swanson (2022) explain, a single series can often be interpreted as a weighted average of multiple dimensions that parsimoniously captures certain aspects of the dimensions.

Complementing our study of the implications of differences within high-frequency monetary policy shock series are those that compare across types of shock series. Rudebusch (1998) compares monetary shock series estimated as a VAR residual [Christiano et al. (1996, 2005)] to high-frequency shock series and finds that these series are quite different. Similarly, Ettmeier and Kriwoluzky (2019) compare the performance of narrative identification achieved by parsing FOMC policy documents for intended changes in the federal funds rate [Romer and Romer (1989), Romer and Romer (2004), Wieland and Yang (2020), Aruoba and Drechsel (2025)] to high-frequency shocks and find differences in inference. Finally, Ramey (2016) also documents differences within and across types of shocks. McKay and Wolf (2023) appeal to Sims’s (1998) argument that monetary policy shocks need not necessarily be correlated across different types of identification as they could be capturing different sources of exogenous variation in monetary policy. However, within high-frequency monetary policy shocks series, one should expect more similarity given that they are constructed from highly correlated asset prices.

Because high-frequency identification explicitly relies on monetary policy announcements, most researchers are limited to starting their sample in 1994 when the Federal Reserve’s Federal Open Market Committee (FOMC) began regularly announcing its monetary policy decisions (exceptions include Bauer and Swanson (2022) and Bu et al. (2021)). Other approaches typically extract longer monetary shock series because they are not constrained to explicitly announced FOMC decisions. However, judgment plays a larger role in determining the time and date of a monetary shock in the absence of an explicit announcement. Therefore, relying on explicitly announced decisions may lend to greater reproducibility, as it is straightforward for researchers to look up the date and time of the announcement and calculate a fixed time window around that announcement. See Appendix D for details on FOMC announcement dates and times.

Researchers have focused on refining high-frequency monetary shock series with add-on techniques because estimates of monetary transmission often have signs that are opposite of what theory predicts.^[1] By controlling for information mismatches between central banks and private agents, high-frequency monetary shock series and their associated monetary transmission estimates can be purged of this so-called “Fed information effect” [see Miranda-Agrippino and Ricco (2021), Bauer and Swanson (2023, 2022), Jarociński and Karadi (2020), Nunes et al. (2023), Zhu (2023), and others]. Because there are many solutions to control for potential endogeneity, we leave our monetary shock series in their simplest form without any additional refinements, permitting more straightforward and transparent comparisons across data and methods.

With today’s monetary policy featuring many tools in addition to the federal funds rate, researchers have often extracted multi-dimensional high-frequency monetary shock series [Acosta 2023; Gürkaynak et al. 2005; Jarociński 2024; Lewis 2025; Swanson 2021, 2023 and others]. We follow Bauer and Swanson (2022) and focus on single monetary shock series for easier comparisons, especially for exercises that interchange data and methods.^[2] Furthermore, a single series allows us to parsimoniously identify the joint effects of monetary policy tools and may combine different dimensions of monetary policy that are not necessarily independent, like those identified by Jarociński (2024).

Although we focus on high-frequency monetary shock series for the United States, the data and methods described in this paper can be extended to other settings. Altavilla et al. (2019), Cieslak and Schrimpf (2019), Andrade and Ferroni (2021), Bu et al. (2021), and others construct shock series for Europe while Braun et al. (2025) and Cieslak and Schrimpf (2019) construct shock series for the United Kingdom. Like us, these researchers start from raw data to highlight the choices faced by researchers and how these choices affect estimates of monetary transmission.

2.1 Kuttner (2001), MP1

Kuttner (2001) was one of the first to rely on the federal funds futures market to disentangle anticipated from unanticipated changes to the federal funds rate. For FOMC announcement in month s, Kuttner (2001) uses the scaled change in the current month federal funds futures (j = 1) unless the monetary policy announcement is in the final seven days of the month, then he uses the unscaled next month federal funds futures (j = 2). Label this instrument as MP1 and in month s we have,

D ^s is the number of days in month s and d ^s is the day of the FOMC announcement. See Appendix A.2 for more details.

2.2 Nakamura & Steinsson (2018), NS

Nakamura and Steinsson (2018) use the first principal component of the financial instruments employed by Gürkaynak et al. (2005) {MP1, MP2, ED2, ED3, ED4} to identify exogenous variation in monetary policy. To exploit information beyond the immediate horizon, Gürkaynak et al. (2005) build off Kuttner’s (2001) work by creating composite measures of changes in interest rate futures that span the first year of the term structure. They include Kuttner’s current-month surprise MP1 along with the surprise for the next FOMC meeting MP2 (defined below) and Eurodollar futures {ED2, ED3, ED4} which we update with SOFR futures {SF3, SF4, SF5} starting in January 2022 as recommended by Acosta et al. (2024). Barakchian and Crowe (2013) document that the federal funds futures market is highly liquid for contracts expiring in the next three months and less liquid for contracts expiring in several years. Because federal funds rate futures are more liquid at shorter horizons, only horizons up to three months ahead are typically used in the construction of monetary shock series. Appendix Figure (A.11) shows that in practice trading volume can be low during ELB episodes, but is roughly triple its 2014 levels at present.

As before, t indexes 20 min after an FOMC announcement and t − Δt 10 min before, resulting in a 30-min time window.^[4] Specifically, the five Gürkaynak et al. (2005)/Nakamura and Steinsson (2018) futures – along with their SOFR futures counterparts – are:

Where MP1 is as before and captures the unexpected change in the federal funds futures contracts expiring at the end of the month s of an FOMC announcement. MP2 captures the unexpected change in federal funds futures that expire at the end of the month s′ which is the month of the next scheduled FOMC meeting. For example, let s =March 2014 then s′ = April 2014. See Appendix A.2 for more details.^[5]

Whether or not monetary policy has multiple dimensions or can be summarized by a single series is debated. Gürkaynak et al. (2005) extract and rotate two factors – the target and path – from the instrument set {MP1, MP2, ED2, ED3, ED4}. These factors correspond to the level and slope of the yield curve for one-year-ahead interest rates and explain 80 and 15  % of the variation, respectively. Gürkaynak et al.’s (2005) multiple factors with suitable rotations can identify the independent effects of each monetary policy tool which may be useful for researchers studying the effects of forward guidance, or other policy tools, separate from that of the federal funds rate [see Gürkaynak et al. (2005), Jarociński (2024), Swanson (2021, 2023) and Acosta (2023) for additional multi-dimensional examples].

By contrast, Nakamura and Steinsson (2018) use a single factor from the same instrument set {MP1, MP2, ED2, ED3, ED4} which can parsimoniously capture the joint effects of different policy tools, which is more advantageous when estimating monetary transmission in more complicated frameworks. We follow the approach of Nakamura and Steinsson (2018), which is also used by Bauer and Swanson (2022), and focus on the single series to simplify the comparison of shocks and interchanging exercises.

2.3 Gertler & Karadi (2015), FF4

Gertler and Karadi (2015) find that the three-month-ahead federal funds futures, FF4, perform strongly as an external instrument in VAR analysis over the January 1991 to June 2012 period. As before, t index the 20 min after an FOMC announcement and t − Δt 10 min before, resulting in a 30-min time window.

For example, if the month s of an FOMC meeting is March 2014, FF4 is the expected federal funds rate at the end of June 2014. In contrast to the construction of the MP1 and NS shock series, Gertler and Karadi (2015) do not scale the FF4 shock series by D ^s /(D ^s − d ^s ). Because they use the FF4 shock series as an external instrument in a monthly VAR, they instead use a moving average representation.^[6] We report the unscaled version of the FF4 shock series due to the scaled version inducing predictability and serial correlation as shown by Ramey (2016) and Miranda-Agrippino and Ricco (2021). See Appendix A.2 for more details.

2.4 Bu et al. (2021), BRW

Bu et al. (2021) note the following two shortcomings of the previously discussed high-frequency monetary shock series constructed from futures with maturities of one year or less. First, obtaining futures data at an intraday frequency can be difficult and second, shorter maturities may be less suited to capturing policies deployed at the ELB designed to affect longer maturity assets.

By constructing monetary shock series from changes in daily zero-coupon Treasury yields that span the full one- to 30-year term structure, Bu et al. (2021) overcome these challenges.^[7] While intraday data ensure the crispest separation of monetary news from its non-monetary counterpart, daily data like that originally used by Kuttner (2001) may only be problematic on all but a few FOMC meetings as explained by Gürkaynak et al. (2005) and confirmed by An et al. (2025). However, because Nakamura and Steinsson (2018) find that changes in long-term interest rates can be confounded by background noise when used at daily frequency, Bu et al. (2021) use a Rigobon (2003) heteroskedasticity-based estimator in shock construction to avoid overstating statistical precision. An et al.’s (2025) comparison of intraday versus daily data in the construction of the NS shock series shows that the series are highly correlated and often lead to similar estimates of monetary policy transmission. Finally, we note that daily data have the advantage of uniform time windows throughout the sample. Appendix A.3 shows that intraday time windows bracketing FOMC announcements can often be larger than 30 min when there is a shortage of suitable trades.

Not only is the BRW shock series constructed from different underlying data, it also relies on a different method than the three shock series previously discussed. The Fama and MacBeth (1973) two-step regression extracts unobserved monetary policy shocks Δi _s from the common component of the change in zero-coupon yields Δ R s j . The first step estimates the average responsiveness of each maturity j = 1, …, 30 on days of FOMC announcements s. The second step obtains repeated cross-sectional estimates for each FOMC announcement by regressing the daily change in maturities one to 30 onto the first step’s average responsiveness coefficients for maturities one to 30. The resulting second step coefficients are the BRW monetary shock series. Note the slight change in notation in that regression coefficients will have maturity j as a subscript instead of a superscript.

1. Estimate responsiveness of zero-coupon yields Δ R s j with maturities j = 1, …, J to policy indicator Δi _s for monetary policy announcement s via time-series regressions. For maturities j = 1, …30 years there will be 30 regressions.

   Δ R s 1 = α 1 + β 1 Δ i s + ϵ s 1 ⋮ Δ R s 30 = α 30 + β 30 Δ i s + ϵ s 30

   This implementation assumes Δi _s is one-to-one with a particular tenor of interest rate. Bu et al. (2021) choose the two-year constant maturity Treasury yield Δ R s 2 . For each maturity j = 1, …J, the above expression can be written as:

   (8) Δ R s j = θ j + β j Δ R s 2 + ϵ s j − β j ϵ s 2 ︸ ξ s j

   The endogeneity arising from corr Δ R s j , ξ s j > 0 stems from β j ϵ s 2 being part of ξ s j and can be reconciled with IV or the heteroskedasticity-based estimator of Rigobon (2003). The time-series regressions instrument Δi _s with (−1) × Δi _s−7, the negative change in the chosen policy indicator seven days before FOMC announcement s. Using (−1) × Δi _s−7 as an instrument should cancel out the β j ϵ s 2 that would exist in any given day without monetary policy news.

2. Recover monetary policy shock Δ i ̂ s from s = 1, …, T repeated cross-sectional regressions of Δ R s j on the responsiveness index β ̂ j for each FOMC announcement s estimated in step 1.

   (9) Δ R s j = α j + Δ i s β ̂ j + v s j , s = 1 , … , T   FOMC announcements

3. Re-scale the shock series by the assumed normalization in step 1, i.e. the daily change in the two-year constant maturity Δ R s 2 Treasury yield in the original formulation.

Compared to other methods of incorporating information from policy actions that target long-term interest rates, the single series of Bu et al. (2021) has the advantage of parsimony and flexibility in assumptions. On the other hand, Swanson (2023) defines multiple independent dimensions of monetary policy and only allows for the effect of large-scale asset purchase shocks during certain periods. Jarociński (2024) and Lewis (2025) define multiple dimensions via additional information from financial markets.

2.5 Comparing Shocks

Figures 1 and 2 show the time series and distributions, respectively, from January 1995 to September 2024 for each of the four shock series studied – MP1, NS, FF4, BRW – as well as three shock series that interchange the data and methods of the NS and BRW shocks. Table 1 compares the correlation coefficients and the signs of the shock series over the full sample and at the ELB.

Figure 1:

Time series of monetary shock series, January 1995 to September 2024. MP1 is the 30-min change around an FOMC announcement in the current month’s federal funds future if the FOMC announcement is in the first 23 days of the month with an adjustment or the next month’s federal funds future if the FOMC announcement is within the last seven days of the month. FF4 is the change in the three-month-ahead federal funds futures within 30 min of an FOMC announcement. NS is the first principal component of the instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} which is the 30-min change in these futures around an FOMC announcement. BRW is a Fama-MacBeth regression of the daily change in one- to 30-year zero-coupon Treasury yields. NS data/BRW method is a Fama-MacBeth regression of the NS data. NS plus long-term rates/BRW method is a Fama-MacBeth regression of the NS data augmented with 2-, 5-, 10-, and 30-year Treasury yields. BRW data/NS method is the first principal component of the BRW data.

Figure 2:

Distributions of monetary shock series, January 1995 to September 2024. MP1 is the 30-min change around an FOMC announcement in the current month’s federal funds future if the FOMC announcement is in the first 23 days of the month with an adjustment or the next month’s federal funds future if the FOMC announcement is within the last seven days of the month. FF4 is the change in the three-month-ahead federal funds futures within 30 min of an FOMC announcement. NS is the first principal component of the instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} which is the 30-min change in these futures around an FOMC announcement. BRW is a Fama-MacBeth regression of the daily change in one- to 30-year zero-coupon Treasury yields. NS data/BRW method is a Fama-MacBeth regression of the NS data. NS plus long-term rates/BRW method is a Fama-MacBeth regression of the NS data augmented with 2-, 5-, 10-, and 30-year Treasury yields. BRW data/NS method is the first principal component of the BRW data. The ELB is defined as December 16, 2008 to December 16, 2015 and March 15, 2020 to March 16, 2022.

Panels (1a) and (2a) show the MP1 shock series, which stands out for having some of the largest negative shocks in the sample and being close to zero throughout both ELB periods. When the federal funds rate is at the ELB – as it was from December 16, 2008 to December 16, 2015 and again from March 15, 2020 to March 16, 2022 – the FOMC has used date- or threshold-based forward guidance to communicate expected liftoff [see Carlstrom and Jacobson (2013) for an overview]. An expected liftoff date far into the future or macroeconomic indicators far from their policy thresholds has resulted in market participants perceiving a change in the federal funds rate as unlikely at the upcoming meeting. As a result, there may be either little trading in federal funds futures contracts expiring in the current or next month, or no monetary news that surprises markets. For example, panel 3 of Table 1 shows that about 40 % of ELB observations are 0 for the MP1 shock series. Moreover, the magnitudes of these shocks at the ELB are at a maximum of about five basis points.

With estimates of the MP1 shock series close to zero in the ELB periods, estimates of monetary transmission may be quite small or imprecise, leading researchers to conclude that there is no effect of monetary policy on the economy, as shown in Appendix Figures (B.23). These findings could be potentially problematic because other evidence such as the work by Swanson and Williams (2014) finds that monetary policy can have an effect at the ELB through communication that targets longer horizons as opposed to the very short horizons used to calculate the MP1 shock series.

In light of these shortcomings, the MP1 shock series has the advantage of reducing bias from either the so-called “Fed information effect” or Fed forward guidance, as noted by Paul (2020). Because either of these features could be operating in the opposite direction of direct changes in the federal funds rate, researchers concerned about contamination may find the MP1 shock series appealing.

With a 0.77 correlation coefficient, the NS shock series is similar to the MP1 shock series, as shown in panels (1b) and (2b). Like the MP1 shock series, the NS shock series is tightly distributed around zero throughout the ELB periods. In contrast to the MP1 shock series where most ELB observations were 0 or negative, 78 % of the NS shock series observations are positive at the ELB, as shown in panel 2 of Table 1. Given that the Federal Reserve has deployed record monetary stimulus at the ELB, what is the interpretation of the 78 % positive observations of the NS shock series? One could interpret unanticipated contractionary monetary news as markets expecting a larger stimulus than what was announced or implemented. Vissing-Jorgensen and Krishnamurthy (2011) note that the LSAP program known as “QE II” announced on November 3, 2010 was about $150 billion less than market expectations. However, it is unlikely that markets expected a larger stimulus in nearly four out of five announcements.

Panel 1 of Table 1 shows that with correlation coefficients of about 0.8 and 0.9, the FF4 shock series is tightly correlated with the MP1 and NS shock series, respectively. This tight correlation is not surprising given that they are all calculated over the same high-frequency intervals and rely on futures with maturities of year or less. Moreover, sometimes these series even use the same underlying data, which is especially true for MP1 in the NS instrument set. Panels (1c) and (2c) show that the distribution of the FF4 shock series narrows at the ELB like the MP1 and NS shock series, it is more centered at zero than the latter. In fact, panel 3 of Table 1 shows that 45 % of the FF4 shock observations are zero at the ELB and another 27 % are positive, which is much less than the 78 % positive observations of the NS shock. Even though the FF4 shock series avoids suggesting more contractionary monetary news than expected in a time of record stimulus, the preponderance of zero observations at the ELB suggests that FF4 may still struggle to capture monetary policy actions at the ELB.

In contrast to the other high-frequency monetary shock series previously described, panels (1d) and (2d) show that the distribution of the BRW shock series is similar across ELB and non-ELB periods. Furthermore, panel 3 of Table 1 shows that shock estimates are never zero throughout the ELB period and 64 % are negative and hence expansionary in periods of record monetary stimulus. The largest negative observations occurred when the Federal Reserve extended or announced LSAP programs in March 2009 and March 2020, respectively. We explain how both data and methods account for these findings.

Because forward guidance and LSAPs affect maturities longer than the one-year horizon of the NS instrument set, shocks series like that of BRW which are constructed from data with maturities up to 30 years should intuitively better capture the effects of these policies. However, including data on longer maturities may be insufficient as these data may be unresponsive to monetary policy on average. Figure 3a shows the responsiveness coefficients from the first step of the Fama-MacBeth regression given by equation (8). On average, Treasuries with relatively shorter maturities are more responsive to changes in the two-year Treasury on FOMC announcement days. In fact, Treasuries with maturities beyond 15 years do not have a statistically significant average responsiveness. For this reason, simply including longer-term rates in the NS instrument set via principal component analysis does not materially change the final series as shown in Appendix A.4. In fact, augmenting the NS instrument set with 2-, 5-, 10- and 30-year intraday Treasury yields results in a shock series that has a 0.92 correlation coefficient with the original NS series. We note that this correlation coefficient is higher than quite a few of the other series studied in this paper.

Figure 3:

Construction of the Bu et al. (2021) shock series. Panels (a) and (b) show estimates { β ̂ j } j = 1 30 from equation (8), Δ R s j = θ j + β j Δ R s 2 + ξ s j for maturities j = 1, …, 30 years are obtained by regressing daily changes in zero-coupon Treasury yields from maturities j = 1, …, 30 on the daily change in the constant maturity two-year Treasury. Estimates are obtained via OLS with robust standard errors. Because response variables are zero-coupon and the independent variable is constant maturity, the coefficient β ̂ 2 for the two-year will be close to one, but not exactly. The effective lower bound of the federal funds rate (ELB) is defined as December 16, 2008 to December 16, 2015 and March 15, 2020 to March 16, 2022. Panels (c) and (d) show the second step of the BRW Fama-MacBeth regression in equation (9), Δ R s j = α j + Δ i s β ̂ j + v s j where s =March 16, 2016 and August 9, 2011, respectively. The x-axis in panels (c) and (d) is { β ̂ j } j = 1 30 , the coefficient estimates from the first-step in equation (8) for one- to 30-year maturities plotted in panels (a) and (b). β ̂ j close to 1 are short-term yields and β ̂ j close 0 are long-term yields. The y-axis in panels (c) and (d) is the daily change in zero-coupon Treasury yields Δ R s j j = 1 30 for maturities j = 1, …, 30 years. The estimated linear fit Δ i ̂ s is the monetary shock. The sample is from January 1994 to September 2024.

If long-term data alone cannot account for the differences in monetary shock series, what is the role of methods? In contrast to principal component analysis, Fama-MacBeth regression allows for the weights on underlying instruments to be time varying so that long-term rates may matter more than short-term for certain announcements and the opposite for others. By relying on the differential responsiveness of short- and long-term Treasuries, the Fama-MacBeth regression can therefore exploit information from long-term rates despite their overall average low responsiveness shown in panel (3a). The second step of the method projects the change in Treasuries for each maturity on the day of an FOMC announcement onto their average responsiveness shown in Figure 3a. If the changes across all maturities on FOMC announcement day s were equal such that Δ R s j = Δ R s j + 1 for all maturities j + 1, then the estimated monetary shock Δ i ̂ s from equation (9) would be equal to zero. On the other hand, if the change in all maturities equaled their average responsiveness, i.e., Δ R s j = β ̂ j for all maturities j, then Δ i ̂ s = 1 . A more practical example than either of these two extremes would be one like that shown in panel (3c) where the change in long-term rates is close to zero, but short-term rates fall. The BRW Fama-MacBeth method would then estimate a negative shock Δ i ̂ s < 0 even though long rates are little changed.

Furthermore, identifying monetary shocks via the differential responsiveness of short- and long-term rates can account for why the BRW shock series is so different than the other shock series at the ELB. Although short-term rates may be relatively unchanged due to forward guidance communicating no expected change in the federal funds rate, medium- to long-term rates may still be adjusting, especially since forward guidance and LSAPs may be targeting these rates. In fact, panel (3b) shows that medium-term rates became relatively more responsive at the ELB changing more than one-for-one relative to a change in the two-year rate. Panel (3d) confirms these differential changes by showing a particular FOMC announcement where long-term rates drop by more than short-term rates and medium-term rates are the most responsive. With roughly a −0.03 percentage point change, the one-year Treasury is the least responsive on this particular FOMC announcement during an ELB episode.

Appendices A.5 and A.6 show that the NS and BRW shock series are tightly correlated with versions constructed from real-time estimates and different normalizations, respectively. Additionally, Appendix A.7 shows versions of the BRW shock series constructed from daily changes in Treasury futures and forwards and discusses the issues therein.

2.6 Interchanging Data and Methods

To confirm that both data and methods drive the differences in the BRW shock series relative to the other shocks series studied, we follow Bu et al. (2021) and interchange data and methods of the NS and BRW shock series. Overall, we find that differences become more pronounced with the largest correlation coefficient at 0.62 and the lowest at −0.40   as shown in Table 1.

Using the NS data – intraday changes in five interest rate futures with maturities of a year or less – in the BRW Fama-MacBeth method produces shocks that have only a correlation coefficient of only about 0.2 with either of the original series, as shown in Table 1. This series has the lowest correlation coefficient in the entire table, −0.40 with the MP1 shock series.

Panels (1f) and (2f) show that the NS data with BRW method results in distributions that are similar in the ELB and non-ELB periods. By contrast, shock series based on raw data or principal component analysis have distributions that narrow at the ELB. In support of these findings, Figure 4 shows the average responsiveness of the five NS interest rate futures to the two-year constant maturity Treasury yield on FOMC days is not significantly different in ELB episodes relative to non-ELB episodes. We do note that the responsiveness of the instruments with the shortest maturities – MP1 and MP2 – is relatively lower in ELB episodes.

Figure 4:

Average responsiveness of the NS data and long-term rates to two-year Treasuries on FOMC days. Estimates β ̂ j from equation (8), but with the updated NS instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} plus long-term rates including 2-, 5-, 10-, and 30-year Treasury yields regressed on the daily change in the constant maturity two-year Treasury. Estimates are obtained via OLS with robust standard errors. The effective lower bound of the federal funds rate (ELB) is defined as defined as December 16, 2008 to December 16, 2015 and March 15, 2020 to March 16, 2022. The sample is from January 1995 to September 2024. Regressing the daily change of the constant maturity two-year Treasury yield on the intraday change in the zero-coupon two-year Treasury yield results in a coefficient that does not equal to one because of the different time windows.

Furthermore, like the original BRW shock series, the version with NS data is mostly symmetric around zero at the ELB as shown in panel (2f). Although the underlying short-term data may be left-censored, because the Fama-MacBeth regression calculates the differential variation across underlying instruments, it may not always prescribe a positive shock if all rates rise. In fact, Table 1 shows that only 52 % of observations are positive at the ELB compared with 78 % using the original principal component analysis. Finally, Figure 4 supports the findings of Swanson and Williams (2014) that monetary policy can still have effects at the ELB.

Although augmenting the NS instrument set with longer-term rates – 2-, 5-, 10-, and 30-day intraday changes in Treasuries – has little effect on the resulting shock series constructed from the NS method as shown in Appendix Figure (A.14), we now ask the question of whether this augmentation affects the BRW method. Panels 2 and 3 of Table 1 show that the series often has the same sign as other shock series studied and that the share of negative observations at the ELB is quite close to that of the original BRW series. The improved fit relative to its counterpart with only the short-term rates can likely be attributed to the increased responsiveness of long-term rates at the ELB as shown in Figure 4.

Panels (1g) and (2g) show that using the BRW data – changes in one- to 30-year zero-coupon Treasury yields – with the NS principal component analysis results in shock series that are not as small in magnitude as the other interchanged shock series shown in panels (1f) and (2f). Like the NS shock series, these interchanged shock series also have a positive mass – 68 % of observations – during the ELB periods. Because the principal component analysis is a linear combination of the underlying instruments, it will prescribe positive shock if all rates rise in response to the monetary policy announcement. Like the previously discussed interchanged shock, the shock series constructed from the BRW data with NS method has a relatively low correlation coefficient of at most 0.24 with the commonly used shocks we study.

2.7 Predictability

Because commonly used monetary shock series have been shown to be predictable and hence not entirely exogenous, we include in our discussion of shock construction predictability tests standard in the literature. Karnaukh and Vokata (2022), Sastry (2021), and Bauer and Swanson (2023) have shown that the NS shocks series is predictable by observables in the form of economic news, and Bu et al. (2021) confirm that the BRW shocks series is not. We find that shocks constructed from federal funds futures are the most likely to be predictable by economic news.

Standard tests assess if economic news predicts monetary shocks ε t i . Let T index months and t higher frequencies. Following the literature, ϵ t i is aggregated to a monthly frequency by summation, ϵ T i = ∑ t ϵ t i and all news indicators pre-date the FOMC announcement in a given month.^[8]

Figure 5 shows the predictability coefficients β ̂ estimated from equation (10) for various measures of economic news along with their 95 % confidence intervals. A monetary shock series is predicted by pre-existing economic news and is therefore not entirely exogenous to underlying economic conditions when confidence intervals do not encompass zero.^[9] Figure 5 shows that the NS shock series appears to suffer the most from predictability. The underlying instrument set can account for this finding, particularly the eurodollar/SOFR futures that are only used in the construction of shock series based on the NS data. Karnaukh and Vokata (2022) find that eurodollar futures tend to be more predictable than short-term fed funds futures, which could help explain why the MP1 and FF4 shock series are less likely to be predictable than the NS shock series. In fact, only those shock series constructed from short-term futures – MP1, FF4, NS, and the NS data interchanged shock series – have any statistically significant predictability coefficients.

Figure 5:

Predictability coefficients with 95 % confidence intervals. Estimate of β ̂ in equation (10) ε t i = α + β n e w s T k + e T are obtained via OLS with robust standard errors that are similar when bootstrapped. The sample is from January 1995 to September 2024 and excludes the second quarter of 2020. See Appendix figure (A.18) for results over additional subsamples and with different controls. For the specification using the Blue Chip GDP revisions, we follow Bauer and Swanson (2023) and exclude observations where the FOMC announcement is in the first three business days of the month from 1995 to December 2000 and the first two business days thereafter to ensure that the Blue Chip Survey was completed prior to the FOMC announcement. Blue Chip GDP revisions are the monthly revision of one-quarter-ahead GDP growth forecasts. The specification using nonfarm payrolls ensures that the FOMC meeting is after the FOMC release which is typically the first Friday of every month. Nonfarm payrolls are the monthly change in the nonfarm payrolls release. The ADS Index is the Aruoba et al. (2009) business conditions index. The BKK index is the Brave et al. (2019) Big Data index. MP1 is the 30-min change around an FOMC announcement in the current month’s federal funds future if the FOMC announcement is in the first 23 days of the month with an adjustment or the next month’s federal funds future if the FOMC announcement is within the last seven days of the month. FF4 is the change in the three-month-ahead federal funds futures within 30 min of an FOMC announcement. NS is the first principal component of the instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} which is the 30-min change in these futures around an FOMC announcement. BRW is a Fama-MacBeth regression of the daily change in one- to 30-year zero-coupon Treasury yields. NS data/BRW method is a Fama-MacBeth regression of the NS data. NS plus long-term rates/BRW method is a Fama-MacBeth regression of the NS data augmented with 2-, 5-, 10-, and 30-year Treasury yields. BRW data/NS method is the first principal component of the BRW data.

Finally, Figure 5 confirms that shock series constructed from long-term interest rates – the BRW shock series and its interchanged counterpart constructed from the NS principal component method – are unpredictable according to several standard tests in the literature. Because the interchanged shock series with the BRW data is also unpredictable, including long-term rates in shock construction may help ensure that high-frequency series are indeed controlling for all available pre-existing information. However, the shock series constructed only from fed funds futures – the MP1 and FF4 shock series – are only marginally predictable suggesting that predictability is most strongly associated with series constructed from the NS data. See Appendix Figure (A.18) for additional results over different sub-samples.

3.1 Forecast Revision Specification

The monetary transmission specification of Campbell et al. (2012) and Nakamura and Steinsson (2018) estimate the response of monthly Blue Chip GDP forecast revisions to high-frequency monetary shocks. Let T index months and t higher frequencies. Following the literature, ϵ t i is aggregated to a monthly frequency by summation, ϵ T i = ∑ t ϵ t i .

Equation (11) can be estimated via OLS because the dependent and independent variables are not simultaneously determined. If the change in actual GDP were used instead of the change in expected GDP, this would not be the case. Because quarterly GDP statistics are the accumulation of economic output over three months, it is impossible to disentangle the output produced before an FOMC announcement – and hence pre-determined at the time of the announcement – from the output produced after the announcement. By contrast, a monthly series of GDP forecast revisions side-steps simultaneous determination by subtracting forecasts made before the announcement from those made after. The resulting forecast revision brackets the FOMC announcement and can estimate the effect of policy on perceptions about economic activity. In fact, researchers exclude monetary shock observations in the first several business days of the month to ensure that the Blue Chip survey was completed prior to an FOMC announcement, as detailed in Appendix B.

The coefficient β ̂ from equation (11) estimates monetary transmission and is shown in Figure 6 along with 95 % confidence intervals for the seven high-frequency shocks studied in this paper. Although New Keynesian theory predicts that the response of GDP to a contractionary monetary shock should be significant and negative, Figure 6 shows that this is not the case for all shock series. Estimates of monetary transmission are positive and significant for the standard shocks based on short-term interest rate futures – the MP1, FF4, and NS shock series. Campbell et al. (2012) and Nakamura and Steinsson (2018) account for these opposite-signed responses as an information mismatch between the central bank and private agents. They argue that this so-called “Fed information” effect can upwardly bias estimates and account for opposite-signed responses that arise from the more informed central bank using announcements to signal information to private agents about the underlying economy. Bauer and Swanson (2023) find a similar upward bias and argue it arises from the central bank and private agents responding to economic news.

Figure 6:

Forecast revision coefficients and 95 % confidence intervals. β ̂ in eq. (11) Blue Chip GDP Revisions T = α + β ε T i + e T is estimated via OLS. Robust standard errors are similar when bootstrapped. The full sample is from January 1995 to September 2024. Following Bauer and Swanson (2023), we exclude observations where the FOMC announcement is in the first three business days of the month from 1995 to December 2000 and the first two business days thereafter to ensure that the Blue Chip Survey was completed prior to the FOMC announcement. See Appendix Figures (B.23) for results over additional subsamples and with different controls. MP1 is the 30-min change around an FOMC announcement in the current month’s federal funds future if the FOMC announcement is in the first 23 days of the month with an adjustment or the next month’s federal funds future if the FOMC announcement is within the last seven days of the month. FF4 is the change in the three-month-ahead federal funds futures within 30 min of an FOMC announcement. NS is the first principal component of the instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} which is the 30-min change in these futures around an FOMC announcement. BRW is a Fama-MacBeth regression of the daily change in one- to 30-year zero-coupon Treasury yields. NS data/BRW method is a Fama-MacBeth regression of the NS data. NS plus long-term rates/BRW method is a Fama-MacBeth regression of the NS data augmented with 2-, 5-, 10-, and 30-year Treasury yields. BRW data/NS method is the first principal component of the BRW data.

The BRW and the interchanged shock series do not produce statistically significant responses suggesting that the opposite-signed response is only found in a subset of shock series. Bu et al. (2021) attribute the lack of opposite-signed response in their shock series to a combination of longer-term rates and methods. As BRW explain, if there is a differential effect of the Fed Information effect on short- and long-term rates – as shown by Hansen et al. (2019) –, adding long-term rates to the construction of monetary shock series can offset the information effect found in short-term rates. However, any information effect in short-term rates is unlikely to be offset in principal component analysis because the procedure extracts linear combinations of the underlying instruments and hence will preserve any information effect in the underlying data. Although Miranda-Agrippino and Rey (2020) and Stavrakeva and Tang (2024) find evidence of information effects in factors constructed from longer-term rates, as long as there is a differential responsiveness of short- and long-term rates, a Fama-MacBeth regression will not necessarily inherit an information effect detected in a subset of rates.

Even though the BRW shock series is the only series of the four main shock series that does not result in a positive and significant opposite-signed response, we note that the MP1 shock series of Kuttner (2001) is only on the margin of insignificance. For researchers concerned that an opposite-signed response in a shock series indicates contamination from central bank information signaling, we argue that the MP1 shock series may be an alternative to the NS shock series. Although additional procedures to the NS shock series can purge these information effects, MP1 offers the advantage of simple construction from raw data.^[10] Appendix figure (B.23) confirms these findings across samples and controls.

3.2 Daily Local Projections

Although the information mismatch between central banks and private agents can account for opposite-signed responses of monetary transmission estimates, Jacobson et al. (2025) present the information mismatch between economic modelers and private agents as a complementary explanation. When response variables are observed at a lower frequency than explanatory variables – as is the case with most macroeconomic response variables – temporal aggregation bias can affect transmission estimates. Jacobson et al. (2025) show that time aggregated data can lead to earlier arriving response coefficients being magnified relative to their later arriving counterparts. When using the daily inflation series from the Billion Prices Project [Cavallo and Rigobon (2016)] as a response variable instead of the monthly official CPI, Jacobson et al. (2025) find that initial positive response coefficients are indeed magnified relative to later arriving negative coefficients. After all, the opposite-signed positive response is quite temporary, if detected at all, when the data frequencies of explanatory and response variables are better matched. Additionally, a specification with matched data frequencies does not require researchers to discard FOMC announcements that occur early in the month as is necessary for identification when data from the Blue Chip survey are used as a response variable.

After showing that the daily inflation series decently approximates the official CPI, Jacobson et al. (2025) compute local projections and find conventionally-signed responses with only a short-lived initial adverse response. Their local projection for day t + h is the following,

Where π _t+h is daily inflation at day t + h calculated as the 30-day percentage change, z _t is the vector of controls which are the 30 lags of daily inflation, and ε t i is one of the seven monetary shock series studied in this paper. Estimates are obtained from the Canova and Ferroni (2022) toolbox using instrumental variables with robust heteroskedasticity and autocorrelation consistent (HAC) standard errors reported at 90 % error bands.

Figure 7 shows the estimated impulse response functions β ̂ ( h ) of the daily inflation index to a one-standard deviation contractionary monetary shock for each of the seven high-frequency series constructed in this paper. The impulse responses to all four constructed monetary shock series estimate statistically significant conventionally-signed responses. If there is an opposite-signed response, it is short-lived and ambiguous. In the daily local projections specification with matched frequencies of explanatory and response variables, neither data nor methods seem to imply much difference in the sign of the estimates. This finding contrasts with that of the forecast revision specification in Section 3.1, where both data and methods drive differences in estimates of monetary transmission.

Figure 7:

Impulse response functions of local projections to a 1 percentage point monetary shock, x-axis is days and y-axis is percentage points. Estimates of β ̂ ( h ) in equation (12) π t + h = α ( h ) + β ( h ) ε t i + Γ ( h ) z t + e t ( h ) , e t ( h ) ∼ N ( 0 , σ ( h ) ) are obtained via the Canova and Ferroni (2022) toolbox with robust heteroskedasticity and autocorrelation consistent (HAC) standard errors reported at 90 % error bands. The daily inflation series π _t is the 30-day percentage change of the Billion Prices Project daily price index which is publicly available from July 2008 to August 2015 via Cavallo and Rigobon (2016). All monetary shock series shown – except those constructed from BRW data – are calculated over the January 1995 to August 2015 sub-sample instead of the full 1995 to 2024 sample. Shock series constructed from BRW data are calculated starting in January 1994. MP1 is the 30-min change around an FOMC announcement in the current month’s federal funds future if the FOMC announcement is in the first 23 days of the month with an adjustment or the next month’s federal funds future if the FOMC announcement is within the last seven days of the month. FF4 is the change in the three-month-ahead federal funds futures within 30 min of an FOMC announcement. NS is the first principal component of the instrument set {MP1, MP2, ED2, ED3, ED4} which is the 30-min change in these futures around an FOMC announcement. BRW is a Fama-MacBeth regression of the daily change in one- to 30-year zero-coupon Treasury yields. NS data/BRW method is a Fama-MacBeth regression of the NS data. NS plus long-term rates/BRW method is a Fama MacBeth regression of the NS data augmented with 2-, 5-, 10-, and 30-year Treasury yields. BRW data/NS method is the first principal component of the BRW data.

Panels (7a) and (7b) repeat the main exercise of Jacobson et al. (2025) and show that the responses of daily inflation to the NS and BRW shock series are both conventionally-signed. Even though the transmission estimates of the forecast revision specification in Section 3.1 are positive and significant for the NS shock series, the local projection estimates are negative – and hence conventionally-signed – for a majority of the 60-day response horizon shown. The only positive – and hence opposite-signed response – is short-lived lasting about 10 days before turning negative. About 30 days after the initial impulse, the response is negative and significant. In fact, the estimated response coefficients with a negative sign are the only estimates that are statistically significant. When using the BRW shock series, the impulse responses are unambiguously negative about 60 days after a contractionary monetary shock. Unlike the NS shocks series, there are almost no estimated opposite-signed impulse response coefficients from the BRW shock series. The positive responses of the BRW shocks series are consistent with those of the forecast revision specification in Section 3.1.

Turning to the interchanged shock series shown in panels (7e) and (7f), the series constructed from the BRW method with NS data – either the original or the version augmented with long-term rates – are the only series among the seven studied in this paper that have a significant opposite-signed response. Given that the series constructed from short-term rates is tightly correlated with its counterpart constructed from short- and long-term rates, as shown in Panel 1 of Table 1, the significance of the opposite-signed response is maintained even after adding long-term rates to the instrument set. These shock series are some of the smallest in magnitude so any large swing in inflation will be ascribed to a relatively tiny impulse and result in a statistically significant response coefficient. For this reason, the shock series constructed from the NS data with the BRW method likely estimates a larger initial positive impulse response than the NS shock series constructed from its standard principal component analysis method shown in panel (7a).

Because shock series constructed from short-term rates shown in panels (7a), (7c)-(7e) all detect an initial positive impulse response, it may be tempting to ascribe opposite-signed responses to short-term rates. However, the point estimates of the impulse responses of the interchanged shock series constructed from the BRW data with the NS method shown in panel (7g) are similar to those constructed with the standard NS data and method shown in panel (7a). Methods must therefore also play a role in the similarity of coefficients observed in panels (7a), (7c)-(7d), and (7g). In the case of the interchanged shock series with the BRW data and the NS method shown in panel (7g), the error bands are wider resulting in no statistically significant response from zero. Because the distribution of the interchanged series is larger than that of the NS series over the 2008 to 2015 period, it is likely that the additional variation leads to less precise estimates.

Overall, differences in estimates of monetary transmission from local projections with matched frequencies of explanatory and response variables matter less than in the forecast revision specification with mismatched frequencies. Both data and methods account for this finding as the point estimates are quite similar for 1) the NS shock series relying on short-term futures and principal component analysis, 2) the MP1 and FF4 shock series relying on short-term futures, and 3) the long-term BRW data in the principal component analysis.

3.3 Vector Autoregression

In addition to specifications relying on forecast revisions or local projections, the effect of monetary policy on the macroeconomy is frequently estimated via a structural vector autoregression framework at a monthly frequency by using high-frequency monetary policy shock series as external instruments. Relative to the previous two specifications studied, the VAR specification has disadvantages and advantages. Unlike the daily local projection specification, VAR specifications typically have mismatched data frequencies in the form of high-frequency shocks and low-frequency response variables. On the other hand, the VAR specification has the advantage of allowing for feedback between multiple macroeconomic variables as co-movements of variables like inflation and output are well documented but absent from the specifications in Sections 3.1 and 3.2.

We use the VAR specification of Bauer and Swanson (2022) which is a variant of Gertler and Karadi (2015). The external instrument imposes a second moment restriction to identify shocks, more specifically it replaces one column of the rotation matrix with predicted values from a regression of a reduced form VAR innovation on the external instrument.^[11] We focus on the VAR with external instruments as it is the dominant specification in empirical macroeconomics as noted by Miranda-Agrippino and Ricco (2023). Bauer and Swanson (2022) provide additional comparisons of the impulse responses of the NS shock and their orthogonalized variant in several VAR specifications including one where the shock series is used an internal instrument or in local projections.

Identification via an external instrument hinges on a high-frequency monetary policy shock series ε t i satisfying relevance and exogeneity conditions to be an adequate external instrument for ε t m p the unobservable true monetary shocks.

Where ɛ _t is any serially uncorrelated structural shocks driving the economy and ε m p is a subset of these shocks unrelated to monetary policy.

Since the true value of ε t m p is unobserved, instrument validity must ultimately be justified logically. All high-frequency monetary shock series studied in this paper should satisfy the relevance condition as they capture monetary news conveyed by FOMC announcements by construction. The exclusion condition should also be satisfied because the tight windows around FOMC announcements should prevent non-monetary news from moving markets. Section 2.7 calls into question the exclusion condition by showing that several commonly used monetary shock series – those of MP1, FF4, and NS – may be contaminated by observables and hence predictable. However, other shock series like those of BRW and MP1 have been shown to be unpredictable, suggesting there are alternatives for concerned researchers. Otherwise, we point researchers interested in relying on the FF4 or NS shocks to the orthogonalization procedure of Bauer and Swanson (2022). Similarly, researchers concerned about an information effect contaminating the exogeneity of the FF4 or NS shock series should explore the add-on procedures of Miranda-Agrippino and Ricco (2021), Jarociński and Karadi (2020), Nunes et al. (2023), and Zhu (2023) that isolate pure shocks from their information counterparts. However, we note that the MP1 and BRW shock series show no or marginal evidence of predictability or adversely-signed responses in the previous sections and may allow researchers to side-step these additional procedures.

The specification for a VAR with external instruments is given as:

Where Y _T is a vector containing four monthly economic variables from January 1973 to December 2019: the log of the consumer price index (CPI), the log of industrial production (IP), the Gilchrist and Zakrajšek (2012) excess bond premium, and the two-year zero-coupon Treasury yield at a monthly frequency. Appendix D details the sources of these series, which we match the exact vintage used by Bauer and Swanson (2022) (https://www.michaeldbauer.com/files/FOMC_Bauer_Swanson.xlsx). We also note that the two-year Treasury yield is the daily change observed at the end of the month as in the previously mentioned excel spreadsheet used by Bauer and Swanson (2022). The excess bond premium controls for financial factors and the two-year Treasury is a measure of the stance of monetary policy. Although the original Gertler and Karadi (2015) specification uses the one-year Treasury, the two-year has the advantage of being less constrained at the ELB and is used by Bauer and Swanson (2022), our main comparison.

Next, B(L) is the matrix polynomial in the lag operator. Although Bauer and Swanson (2022) follow Gertler and Karadi (2015) and Ramey (2016) in using 12 lags, we shorten to 8 lags due to the sample size of our high-frequency shocks. Bauer and Swanson (2022) construct a version of the NS shock series from 1988 to 2019 while we begin our sample in 1995 which is as close to the 1994 introduction of explicit policy statements as our intraday data allows without resorting to sources we cannot replicate from intraday data on actual trades. Appendix C confirms that reducing the number of lags does not substantially change the qualitative estimates of monetary policy transmission, but does result in slightly different point estimates. As noted by Ramey (2016), these types of specifications are highly sensitive to the underlying data sample and may therefore differ from the original Gertler and Karadi (2015) estimates which are from 1991 to 2012.

Finally, ϵ t i is the instrument for s 1 Y T 2 y estimated via two-stage least squares and u ̃ T is the residual. The sample of the external instrument ε t i does not have to be the same as that of the economic data. In fact, the sample used for our seven shock series is from January 1995 to December 2019 and the sample for the economic data is from January 1973 to February 2020. Furthermore, following the literature ϵ t i is aggregated to a monthly frequency by summation, ϵ T i = ∑ t ϵ t i . We do not make any further adjustments for the timing of shocks within the month as Ramey (2016) and Miranda-Agrippino and Ricco (2021) find that the adjustments proposed by Gertler and Karadi (2015) can induce serial correlation.^[12] Appendix C confirms that we obtain similar results using our constructed monetary policy shocks to those constructed by Bauer and Swanson (2022) using the data available from the author’s website (https://www.michaeldbauer.com/files/FOMC_Bauer_Swanson.xlsx).

Figure 8 plots impulse responses from equation (13) obtained via the Canova and Ferroni (2022) toolbox with 68 % error bands and 20,000 draws. For the four main shock series studied in this paper, the impulse responses to a 25 basis point monetary shock are qualitatively in line with macroeconomic theory and similar in sign and shape. The response of the two-year Treasury shown in the bottom row of each panel rises on impact and is normalized so that its initial response is 25 basis points. After initially rising, the two-year Treasury decreases and returns to zero about 10 months after the initial impulse. The excess bond premium displayed in the third row rises on impact to about 0.2–0.4 percentage point in all series shown and then declines towards zero.

Figure 8:

Impulse responses to a 25 basis point monetary shock, x-axis is months and y-axis is percentage points. Impulse responses are estimates from equation (13) Y T = α + B ( L ) Y T − 1 + s 1 Y T 2 Y + u ̃ T obtained via the Canova and Ferroni (2022) Bayesian VAR toolbox with 68 % error bands, 20,000 draws, and 8 lags. The sample of monetary shock series is from January 1995 to December 2019 while the sample of economic data is from January 1973 to February 2020. MP1 is the 30-min change around an FOMC announcement in the current month’s federal funds future if the FOMC announcement is in the first 23 days of the month with an adjustment or the next month’s federal funds future if the FOMC announcement is within the last seven days of the month. FF4 is the change in the three-month-ahead federal funds futures within 30 min of an FOMC announcement. NS is the first principal component of the instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} which is the 30-min change in these futures around an FOMC announcement. BRW is a Fama-MacBeth regression of the daily change in one- to 30-year zero-coupon Treasury yields. IP is the industrial production index, CPI is the consumer price index, excess bond premium is from Gilchrist and Zakrajšek (2012), and the two-year Treasury is the end of the month daily change in the zero-coupon yield. All sources of series are detailed in Appendix D. Appendix Table 3 displays the first-stage F-statistics.

The impulse responses of both industrial production and CPI shown in rows one and two of Figure 8, respectively, to a contractionary monetary shock are significantly negative – as standard New Keynesian theory predicts. We find no evidence of opposite-signed responses in CPI or industrial production as was found in the forecast revision specification in Section 3.1 or elsewhere in the literature. Differences in point estimates among the four shock series are only in magnitude rather than in sign. The responses of industrial production shown in the first row are the largest for the MP1 and FF4 shock series shown in panels (8a) and (8b), respectively. The responses of CPI shown in the second row have similar interpretation to the responses for industrial production. All CPI responses are statistically significant and negative with those of the MP1 and FF4 shocks in panels (8a) and (8b), respectively, being the largest in magnitude. However, the response of CPI to the BRW shock series in panel (8d) has an initial response that is largest in magnitude the CPI.

The differences between the NS and BRW shock series are relatively minor with the exception of the response of the excess bond premium, which can be explained by BRW’s shock construction including the longer-end of the yield curve. Inference with respect to other variables (CPI, industrial production and two-year Treasury) would not be substantially different between the BRW and NS series. Conversely, the MP1 and FF4 shock series show much larger responses of the CPI.

Both the similarity of impulse responses and the conventionally-signed estimates in Figure 8 could suggest that differences in monetary shock series are negligible when estimating monetary transmission in a VAR with external instruments. However, Figure 9 shows that the impulse responses of the interchanged shock series are quite different than their counterparts shown in Figure 8 as the interchanged shock series often estimate opposite-signed responses. Panel (9b) shows that the impulse responses to the shock series constructed from the NS instrument set augmented with long-term rates in the BRW method is somewhat of an exception as some of the point estimates are conventionally-signed like those of the main shock series shown in Figure 8. However, given the opposite-signed response to industrial production we still caution that interchanging data and methods can drastically change the inference. Together, these findings suggest that even though differences in monetary shock series can affect VAR estimates, the effects of these differences range from quantitatively small when comparing the four main shock series studied to qualitatively large when examining combinations of data and methods. An econometrician must proceed cautiously as inference is not robust to all modern constructions of monetary policy shock series. Appendix Table 3 displays the first-stage F-statistics.

Figure 9:

Impulse response to a 25 basis point monetary shock, x-axis is months and y-axis is percentage points. Impulse responses are estimates from equation (13) Y T = α + B ( L ) Y T − 1 + s 1 Y T 2 Y + u ̃ T obtained via the Canova and Ferroni (2022) Bayesian VAR toolbox with 68 % error bands, 20,000 draws, and 8 lags. The sample of monetary shock series is from January 1995 to December 2019 while the sample of economic data is from January 1973 to February 2020. NS data/BRW method is a Fama-MacBeth regression of the NS data, the instrument set {MP1, MP2, ED2/SF3, ED3/SF4, ED4/SF5} which is the 30-min change in these futures around an FOMC announcement. NS plus long-term rates/BRW method is a Fama MacBeth regression of the NS data augmented with 2-, 5-, 10-, and 30-year Treasury yields. BRW data/NS method is the first principal component of the BRW data, the daily change in one- to 30-year zero-coupon Treasury yields. IP is the industrial production index, CPI is the consumer price index, excess bond premium is from Gilchrist and Zakrajšek (2012), and the two-year Treasury is the end of the month daily change in the zero-coupon yield. All sources of series are detailed in Appendix D. Appendix Table 3 displays the first-stage F-statistics.

Although Section 2 documents differences in several commonly used monetary policy shocks, we find that the effect of these differences on estimates of monetary transmission can be small depending on the specification used. Specifications like the daily local projections and VAR with external instruments in Sections 3.2 And 3.3, respectively, are more similar in terms of signs and magnitudes of estimates than the forecast revision specification in Section 3.1.