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1. (1998) and set r = 2%, okun = 3, u∗ = 6%, and π∗ = 2%. Notably, the assumption that the real interest rate is 2% is criticizable given the low interest rate environment experienced over the past decade. However, as our data go back three decades, it is not unreasonable to assume that the average real interest rate has been 2% over this period. The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09. FF Futures Overnight Index Swaps n = 3 6 3 6 9 12 ρ ψTaylor t+n , rx (n) t+n 0.20 0.28 0.14 0.21 0.31 0.36 [0.00] [0.00] [0.04] [0.00] [0.00] [0.00] ρ
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2. (2009). See appendix IA.6 for details on sample sizes and data sources. Overnight Index Swaps n = 3 6 9 12 Australia-1.61-1.47 0.68 4.59 (-1.22) (-0.65) (0.18) (0.79) Canada 1.41 4.82 9.09 9.22 (1.37) (2.00) (2.13) (1.05) Euro area 2.49 5.48 10.67 13.94 (2.27) (2.04) (1.94) (2.06) United Kingdom 3.04 6.55 11.35 17.21 (1.48) (1.58) (1.71) (1.82) Japan 0.23 0.57 1.22 2.02 (0.58) (0.81) (0.91) (1.12) Switzerland 2.10 5.75 10.39 15.44 (1.01) (1.38) (1.68) (1.84) Table 8: Expectation Errors and Excess Returns on International OIS The table shows the correlations between excess returns on OIS and expectation errors internationally. Survey expectations are from Reuters Central Bank Polls. We consider excess returns on contracts with horizons 3, 6, 9, and 12 months and report p−values for the correlations being larger than zero.
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39. FF Futures Overnight Index Swaps n = 3 6 3 6 9 12 α(n) 4.08 8.90 2.17 4.99 8.43 13.07 (3.43) (2.94) (1.56) (1.80) (1.65) (1.57) β(n) 20.49 36.34 16.13 29.75 45.18 58.49 (5.33) (4.01) (3.59) (3.28) (2.86) (2.40) R2 0.12 0.13 0.09 0.11 0.13 0.13 IA – Table IA.8: Excess Return Predictability: Out-of-Sample The table reports the Campbell and Thompson (2008) R2 OoS statistic for predicting excess returns out-of-sample using either the stock market, nonfarm employment growth, the credit spread, or the Treasury yield spread as the predictor variable. The forecasts are formed as c rx (n) t+n = b α (n) t + b β (n) t xt, where xt contains the given predictor variable and the coefficients are estimated recursively based on an expanding window of observations, where the initial estimation window contains five years of data.
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40. FF Futures Overnight Index Swaps n = 3 6 3 6 9 12 Panel A: rx (n) t+n = α(n) + β(n) Disagreement (n) t + (n) t+n β(n) 0.37 0.64 0.21 0.45 0.81 1.03 (3.77) (3.12) (1.47) (1.77) (2.39) (2.65) R2 0.07 0.11 0.02 0.05 0.13 0.19 Panel B: EE (n) t+n = α(n) + β(n) Disagreement (n) t + (n) t+n β(n) 0.45 0.59 0.51 0.61 0.87 1.03 (3.08) (2.41) (2.85) (2.11) (2.26) (2.42) R2 0.07 0.08 0.07 0.07 0.13 0.17 Table 7: Mean Excess Returns on International OIS The table shows the mean excess returns on international OIS. We regress each series on a constant and report coefficient estimates in basis points. t-statistics use standard errors computed using a block bootstrap, with the block length determined according to Politis and White (2004) and Patton et al.

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51. IA – IA.6. Overview of International Data The table summarizes the sources of international data. OIS in all currency areas target overnight interest rates, while survey participants in Reuters Central Bank Polls report their expectations of the future monetary policy target (Australia, Canada, and Japan were introduced late into the survey, hence their exclusion). Data on OIS, overnight rates, and stock returns are from Bloomberg (in 2018:01, SARON replaced TOIS as the official overnight rate in Switzerland), while survey responses are retrieved from the Thomson Reuters database. Policy rates are from the Bank for International Settlements. While the series have different starting dates as denoted below, they all end 2021:09.
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52. In Panel A, we run univariate regressions using the excess returns on the S&P500 as the predictor variable. The estimated coefficients denote the basis point change in expectation errors following a 1% (100 bps) increase or decrease in the stock market. In Panel B, we run a horse race between the stock market and nonfarm employment growth. The coefficient γ(n) shows the basis point change in expectation errors following a 1% change in employment growth. Panels C and D use the corporate bond spread and the Treasury yield spread as controls, respectively, where γ(n) measures the basis point change in expectation errors following a 1% change in either of these two variables. We report t-statistics based on standard errors computed using a block bootstrap, where the block length is determined according to Politis and White (2004) and Patton et al. (2009). The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09.
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53. ψTaylor t+n , EE (n) t+n 0.23 0.32 0.19 0.26 0.35 0.40 [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] IA – Table IA.7: Excess Returns in Recessions The table reports the coefficient estimates from regressions of excess returns on a constant and a recession dummy, rx (n) t+n = α(n) +β(n)NBERt +ε (n) t+n, where NBERt is the National Bureau of Economic Research (NBER) recession indicator which takes the value one whenever the economy is in recession and zero otherwise. We report t-statistics based on standard errors computed using a block bootstrap, where the block length is determined according to Politis and White (2004) and Patton et al. (2009).
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77. Square brackets present Clark and West (2007) p−values for tests of equal predictive accuracy between these forecasts and the EH benchmark.
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78. Sundaresan, S., Z. Wang, and W. Yang (2016). Dynamics of the expectation and risk premium in the OIS term structure. Indiana University Working paper.
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81. Tables and Figures Table 1: Decomposing Excess Returns on FF Futures and OIS Panel A shows the mean excess returns on FF futures and OIS, as well as expectation errors and survey-implied term premia. We regress each series on a constant and report coefficient estimates in basis points. t-statistics use standard errors computed using a block bootstrap, with the block length determined according to Politis and White (2004) and Patton et al. (2009). In Panel B, we perform a simple variance decomposition to test how much excess return variation is attributed to expectation errors and term premia, respectively. We compute the contribution of expectation errors as cov(rx (n) t+n, EE (n) t+n)/var(rx (n) t+n), where rx (n) t+n are excess returns and EE (n) t+n are the expectation errors over the same horizon. We compute the contribution of term premia as cov(rx (n) t+n, TP (n) t )/var(rx (n) t+n).
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82. The coefficient γ(n) shows the basis point change in excess returns following a 1% change in nonfarm employment. In Panels C and D, we use the corporate bond spread and the Treasury yield spread as control variables instead of nonfarm employment, respectively. Here, γ(n) measures the basis point change in excess returns following a 1% change in either of these two variables. We report t-statistics with standard errors computed using a block bootstrap, where the block length is determined according to Politis and White (2004) and Patton et al. (2009). The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09.
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83. The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09.
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84. The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09.
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85. The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09.
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86. The sample for FF futures is 1990:11 to 2021:09 and the sample for OIS is 2001:12 to 2021:09.
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87. These findings actively demonstrate that investors update their expectations upwards in acIA – cordance with FIRE, but face significant information rigidities when revising their expectations of the short rate downwards. These results corroborate recent work on short rate expectation formation, e.g. Bordalo et al. (2020) who argue that market participants “underreact to news” when forecasting the short rate. We contribute to this body of literature by showing that this underreaction is highly asymmetric: when faced with positive news, market participants do in fact adjust their expectations in accordance with FIRE. When faced with negative news, however, market participants are not pessimistic enough and underestimate by how much the Fed will cut interest rates.
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88. Thornton, D. L. (2006). When did the FOMC begin targeting the Federal Funds rate? What the verbatim transcripts tell us. Journal of Money, Credit and Banking, 2039–2071.
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89. Tuckman, B. and A. Serrat (2011). Fixed income securities: Tools for today’s markets (3rd ed.). Hoboken, NJ: John Wiley & Sons.
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90. When Taylor rule deviations are positive, short rates are below the level implied by the Taylor rule and vice versa. The series are plotted with National Bureau of Economic Research (NBER) recession periods in gray shading. Both series are standardized to have mean zero and unit variance and the sample is 2001:12 to 2021:09. 2005 2010 2015 2020 -2 0 2 4 6 8 10 2005 2010 2015 2020 -2 0 2 4 6 2005 2010 2015 2020 -2 0 2 4 2005 2010 2015 2020 -2 0 2 4
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91. Woodford, M. (2001). Imperfect common knowledge and the effects of monetary policy. National Bureau of Economic Research.

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11. Macroeconomic Drivers of Bond and Equity Risks. (2018). Viceira, Luis ; Pflueger, Carolin ; Campbell, John.
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12. Time-varying Response of Treasury Yields to Monetary Policy Shocks: Evidence from the Tunisian Bond Market. (2018). Marfatia, Hardik ; Juko, Sonja ; Mbarek, Lassaad .
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13. Macroeconomic determinants of the term structure: Long-run and short-run dynamics. (2018). Doshi, Hitesh ; Liu, Rui ; Jacobs, Kris.
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14. Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero. (2017). Pruitt, Seth ; Kim, Jinill.
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15. Federal Reserve Credibility and the Term Structure of Interest Rates. (2017). Lakdawala, Aeimit ; Wu, Shu.
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16. Term-structure modelling at the zero lower bound: Implications for estimating the forward term premium. (2017). Chung, Tsz-Kin ; Li, Ka-Fai ; Hui, Cho-Hoi.
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17. Federal Reserve credibility and the term structure of interest rates. (2017). Lakdawala, Aeimit ; Wu, Shu.
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18. Pricing of bonds and equity when the zero lower bound is relevant. (2017). Kick, Heinrich .
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19. Pricing corporate bonds with interest rates following double square-root process. (2016). Lo, Chi-Fai ; Hui, Cho-Hoi.
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20. Influence de la politique monétaire sur le taux long Quelques évidences empiriques, cas du Maroc. (2016). el Faiz, Zakaria ; Ziani, Manal .
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21. Pricing Corporate Bonds With Interest Rates Following Double Square-root Process. (2016). Hui, Cho-Hoi ; Lo, Chi-Fai.
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22. The term structure of interest rates in an estimated New Keynesian policy model. (2016). Buncic, Daniel ; Lentner, Philipp .
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23. Evaluating the robustness of UK term structure decompositions using linear regression methods. (2016). Meldrum, Andrew ; Malik, Sheheryar.
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24. The evolution of U.S. monetary policy: 2000–2007. (2016). Ireland, Peter ; Belongia, Michael.
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25. Pricing sovereign credit risk of an emerging market. (2016). Serwa, Dobromił ; Camba-Mendez, Gonzalo ; Marszal, Anna ; Kostrzewa, Konrad .
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26. Prediction of Term Structure with Potentially Misspecified Macro-Finance Models near the Zero Lower Bound. (2015). Iiboshi, Hirokuni ; Chung, Tsz-Kin.
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27. Always and Everywhere Inflation? Treasuries Variance Decomposition and the Impact of Monetary Policy. (2015). Zekaite, Zivile ; Nolan, Charles ; Kontonikas, Alexandros.
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28. Resolving the spanning puzzle in macro-finance term structure models. (2015). Rudebusch, Glenn ; Bauer, Michael.
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29. A regime-switching Nelson–Siegel term structure model of the macroeconomy. (2015). Zhu, Xiaoneng ; RAHMAN, Shahidur .
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30. Monetary policys time-varying impact on the US bond markets: Role of financial stress and risks. (2015). Marfatia, Hardik.
    In: The North American Journal of Economics and Finance.
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31. Losing track of the asset markets: the case of housing and stock. (2015). Leung, Charles ; Chen, Nan-Kuang ; Charles Ka Yui Leung, ; Chang, Kuang-Liang.
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32. Resolving the Spanning Puzzle in Macro-Finance Term Structure Models. (2015). Rudebusch, Glenn ; Bauer, Michael ; GlennD. Rudebusch, .
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33. Long-run priors for term structure models. (2015). Roberts-Sklar, Matt ; Meldrum, Andrew.
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34. Applying a Macro-Finance Yield Curve to UK Quantitative Easing. (2014). Waters, Alex ; Chadha, Jagjit.
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35. Macroeconomic Drivers of Bond and Equity Risks. (2014). Viceira, Luis ; Pflueger, Carolin ; Campbell, John.
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36. Monetary Policy Switching in the Euro Area and Multiple Equilibria: An Empirical Investigation. (2014). Dufrénot, Gilles ; Khayat, Anwar ; Dufrenot, Gilles.
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37. Monetary policy regimes: Implications for the yield curve and bond pricing. (2014). De Giorgi, Enrico ; Audrino, Francesco ; Filipova, Kameliya .
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    RePEc:eee:jfinec:v:113:y:2014:i:3:p:427-454.

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38. Stress testing interest rate risk exposure. (2014). Abdymomunov, Azamat ; Gerlach, Jeffrey .
    In: Journal of Banking & Finance.
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39. Applying a macro-finance yield curve to UK quantitative Easing. (2014). Waters, Alex ; Chadha, Jagjit.
    In: Journal of Banking & Finance.
    RePEc:eee:jbfina:v:39:y:2014:i:c:p:68-86.

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40. Monetary Policy Switching in the Euro Area and Multiple Equilibria: An Empirical Investigation. (2014). Dufrénot, Gilles ; Khayat, Anwar ; Dufrenot, Gilles.
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41. Dynamic term structure models: The best way to enforce the zero lower bound. (2014). Meldrum, Andrew ; Andreasen, Martin M..
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42. Adaptive Dynamic Nelson-Siegel Term Structure Model with Applications. (2013). Niu, Linlin ; Chen, Ying.
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43. No-Arbitrage Taylor Rules with Switching Regimes. (2013). Li, Tao ; Yu, Cindy .
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44. Estimating Monetary Policy Rules When Nominal Interest Rates Are Stuck at Zero. (2013). Pruitt, Seth ; Kim, Jinill.
    In: CAMA Working Papers.
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45. Inflation ambiguity and the term structure of U.S. Government bonds. (2013). Ulrich, Maxim .
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46. Endogenous monetary policy shifts and the term structure: Evidence from Japanese government bond yields. (2013). Koeda, Junko.
    In: Journal of the Japanese and International Economies.
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47. Why Gaussian macro-finance term structure models are (nearly) unconstrained factor-VARs. (2013). Singleton, Kenneth ; Joslin, Scott .
    In: Journal of Financial Economics.
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48. Monetary policy regimes and the term structure of interest rates. (2013). Chernov, Mikhail ; Bikbov, Ruslan .
    In: Journal of Econometrics.
    RePEc:eee:econom:v:174:y:2013:i:1:p:27-43.

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49. Endogenous Monetary Policy Shifts and the Term Structure: Evidence from Japanese Government Bond Yields. (2013). Koeda, Junko.
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50. Macroeconomic Regimes. (2012). Moreno, Antonio ; Inghelbrecht, Koen ; Bekaert, Geert ; Cho, Seong Hoon ; Baele, Lieven .
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51. Estimating term structure changes using principal component analysis in Indian sovereign bond market. (2012). Nath, Golaka .
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52. Evolving macroeconomic perceptions and the term structure of interest rates. (2012). Wei, Min ; Orphanides, Athanasios.
    In: Journal of Economic Dynamics and Control.
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53. The yield curve and the macro-economy across time and frequencies. (2012). Martins, Manuel ; Aguiar-Conraria, Luís ; Martins, Manuel M. F., ; Soares, Maria Joana.
    In: Journal of Economic Dynamics and Control.
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54. Equity Yields. (2012). koijen, ralph ; Vrugt, Evert B. ; Hueskes, Wouter H. ; Ralph S. J. Koijen, ; van Binsbegen, Jules H..
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55. An International Dynamic Term Structure Model with Economic Restrictions and Unspanned Risks. (2012). Diez de los Rios, Antonio ; Bauer, Gregory.
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56. Challenges in macro-finance modeling. (2009). Kim, Don H..
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57. Monetary Policy Regimes and the Term Structure of Interest Rates. (2008). Chernov, Mikhail ; Bikbov, Ruslan .
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