RDP 2023-04: Can We Use High-frequency Yield Data to Better Understand the Effects of Monetary Policy and Its Communication? Yes and No! 2. Related Literature

Our paper relates to the large literature that tries to identify monetary policy ‘shocks’ – movements unrelated to economic conditions (see Ramey (2016) for detailed reviews of the literature). One of the most common approaches to identifying shocks and their effects is to use SVARs along with some identifying restrictions about the timing, magnitude or direction of the effects that shocks have on other economic variables. Numerous papers have applied these approaches to Australian data (e.g. Berkelmens 2005; Beechey and Österholm 2008; Lawson and Rees 2008; Jääskelä and Jennings 2010; Jääskelä and Smith 2011; Read 2022). A common finding in the Australian literature is that while increases in interest rates tend to be contractionary for the economy, they also tend to be associated with increases in inflation: the so-called ‘price puzzle’.

More recently, papers have tried to identify monetary policy shocks using other types of data. For example, Romer and Romer (2004) estimate how the Federal Reserve sets interest rates based on current and expected future conditions, and then use this to identify movements that appear unrelated to these conditions. Bishop and Tulip (2017) and Beckers (2020) both apply this approach to the Australian data. The latter, by also incorporating indicators of financial conditions into the reaction function, is able to overturn the price puzzle. In a somewhat similar vein, some papers have tried to account for this additional information by increasing the information set included in the SVAR (Gambetti 2021). For example, Hartigan and Morley (2020) are able to overturn the price puzzle by including factors extracted from a large macro dataset into the VAR.

Our paper relates more directly to a more recent stream of the literature that has used high-frequency changes in interest rates around policy or other central bank announcements. Using these ‘surprises’ as monetary policy shocks relies on three assumptions:

1. during the short window under examination the announcement is the only economic news, so any change can be fully attributed to the announcement.
2. the market has priced in all information about future economic conditions, and so the surprise reflects only information about monetary policy that is unrelated to economic outcomes.
3. the market understands how the central bank will react to the available information.

One key advantage of the high-frequency approach is that it allows us to incorporate information from across the yield curve. This opens up the possibility of understanding not only the transmission of monetary policy through changes in the current policy rate, but also how policy and its communication can affect the economy by changing expectations about future rates, as well as people's views about risks and therefore the premia they require to invest.

Early papers in this literature focus on a single short-term interest rate to identify shocks to current rates – Action shocks (Kuttner 2001; Gürkaynak, Sack and Swanson 2005b). Gürkaynak, Sack and Swanson (2005a) extend this analysis while focusing on shocks to both current and future expected rates – Action and Path shocks – by looking at high-frequency changes in interest rates with maturities up to one year.

More recent papers on identification using high-frequency policy surprises point out that the second assumption noted above is likely to be violated in many cases, and that the surprises are likely to combine true policy shocks with additional information about the state of the economy – an information shock. In particular, an unexpected increase in the policy rate could indicate that the central bank expects economic conditions to improve, reflecting additional information held by the central bank and not available to the market. This reintroduces the standard endogeneity issue that shocks are meant to avoid. Jarockinski and Karadi (2020) and Miranda-Agrippino and Ricco (2021) show that ignoring these information shocks leads to biased estimates of the effects of monetary policy. He (2021) explores information shocks in the Australian context, finding some evidence that speeches contain such information, but little evidence for other communication.

An alternative explanation of the information shocks is the ‘Fed response to news channel’ proposed by Bauer and Swanson (2021, 2022). They show that their empirical results are consistent with incoming, publicly available economic news causing the Fed to change monetary policy, but the private sector systematically underestimating the Fed's response. This leads to a violation of the third assumption. They provide substantial new evidence that distinguishes between news and the response to news channels. The evidence strongly favours the response to news channel. They also outline a model where market participants learn about the Fed's reaction function, which can explain systematic misunderstanding of the Fed's response.

Another recent debate in the literature revolves around the role of term premia and financial conditions in the transmission of monetary policy shocks. Using high-frequency changes in interest rates around announcements for the United States, Gertler and Karadi (2015) find that monetary policy shocks transmit through the economy largely by affecting term premia, with expected future interest rates left almost unaffected. To this end, they highlight the importance of including financial variables in models to capture the transmission of monetary policy through the credit markets.^[1] Consistent with this, Caldara and Herbst (2019) find that US monetary policy has a strong and systematic response to financial conditions, underlining the importance of including financial variables such as credit spreads in the VAR or when constructing shock measures using the Romer and Romer (2004) approach.

Gertler and Karadi's (2015) finding that monetary policy transmits to the economy almost exclusively through changes in term premia presents a challenge to theoretical models used for monetary policy analysis. This, alongside the earlier noted debate around information shocks, motivated KMS to revisit the relevance of Action, Path and Premia shocks using a more structural approach.

Specifically, KMS apply an ATSM to high-frequency data on changes in the yield curve around Fed policy announcements. This allows them to decompose these changes into changes in expected rates and term premia. Using additional restrictions in a second step, they further decompose these surprises into three different facets of monetary policy and its communication: changing current short-term rates – Actions shocks; changes in the expected path of rates – Path shocks; and changes in term premia or uncertainty – Premia shocks. They then study the macroeconomic effects of the different facets of monetary policy using a local projections model. Overall, they find that Action shocks affect the economy like textbook monetary policy shocks. Path shocks appear to capture signalling about future economic conditions, while Premia shocks are hard to interpret, but have some hallmarks of uncertainty shocks.

Our paper also relates to the literature on ATSMs. In particular, several papers have highlighted that ATSMs tend to produce unreasonably stable estimates of expected rates in the far future. This reflects small-sample biases in these models. Some papers have suggested using statistical approaches to address this bias (e.g. Malik and Meldrum 2016), as used in KMS. In contrast, other papers have argued for the incorporation of surveys to provide additional information on the expected path of short-term interest rates (e.g. Kim and Orphanides 2012; Guimarães 2016). As we show in Appendices A and B, the choice of approach can affect the identified shocks in the KMS framework and therefore alter the interpretation of the shocks.

Footnote

Doko Tchatoka and Haque (2021) point out that the real effects of US monetary policy shocks identified using Gertler and Karadi's (2015) approach are sensitive to the sample period. They find that once the Volcker disinflation period is left out and one focuses on the post-mid-1980s period, policy shocks have no significant effects on output, despite large movements in credit costs. [1]