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Regressions of private-sector macroeconomic forecast revisions on monetary policy surprises often produce coefficients with signs opposite to standard macroeconomic models. The “Fed information effect” argues these puzzling results are due to monetary policy surprises revealing Fed private information. We show they are also consistent with a “Fed response to news” channel, where both the Fed and professional forecasters respond to incoming economic news. We present new evidence challenging the Fed information effect and supporting the Fed response to news channel, including: regressions that control for economic news, our own survey of professional forecasters, and financial market responses to FOMC announcements. (JEL D82, E23, E27, E43, E44, E52, E58)

Monetary policy affects financial markets and the broader economy in part by changing the risk appetite of investors. This article provides new evidence for this so-called risk-taking channel of monetary policy by revisiting and extending event-study analysis of Federal Open Market Committee announcements. We document significant effects of unexpected monetary policy changes on risk indicators drawn from equity, fixed-income, credit, and foreign exchange markets. We develop a new index of risk appetite based on the common component of these indicators. Surprise monetary easing leads to strong and persistent increases in our index, and vice versa for tightening surprises, consistent with the view that monetary policy affects asset prices in large part through its effects on risk appetite. We discuss the implications of the risk-taking channel for monetary policy transmission, optimal monetary policy, and financial stability.

Engineers provide essential services to society, solving pressing challenges through technological inventiveness. Students new to engineering often cite the lure of creative problem solving as attracting them to the discipline. However, traditional engineering curricula typically focus on a narrow application of fundamentals for solving closed-ended problems. Too often, engineering programs do not encourage inventive expression in problem solving. Not surprisingly, the attrition rate for engineering programs is unusually high. Recently, engineering education has shifted its focus to new, more engaging practices that incorporate hands-on methods, boosting prospects for students to engage in creative problem solving. Because service learning provides opportunities for applied work, incorporating it into engineering education programs in can engage students positively and lower attrition rates. Moreover, since engineers are fundamentally involved with social improvement, then engaging students in activities that expand their understanding of the potential impact their skills may impart to a community is not only prudent but best practices. This paper explores two case studies of community-based service learning engineering projects, highlighting community partnerships, analyses and decision-making that helped drive designs and outcomes. It explores how both the communities and students benefitted, focusing notably on the influence these activities had on student understanding of their work, academic and/or professional direction and social consciousness. These are analyzed via longitudinal reporting of students incorporating lessons learned several years post-project. The service learning projects took place in marginalized communities in Denver and Costa Rica. In the Denver project, engineering students designed, built and installed low cost solar heaters into an area with poor housing stock. In Costa Rica, students built a solar water heater for a local school. Keywords: applied learning, engineering education, experiential learning, service-learning.

Macro-finance theory implies that trend inflation and the equilibrium real interest rate are fundamental determinants of the yield curve. However, empirical models of the term structure of interest rates generally assume that these fundamentals are constant. We show that accounting for time variation in these underlying long-run trends is crucial for understanding the dynamics of Treasury yields and predicting excess bond returns. We introduce a new arbitrage-free model that captures the key role that long-run trends play in determining interest rates. The model also provides new, more plausible estimates of the term premium and accurate out-of-sample yield forecasts. (JEL E31, E43, E47)

High-frequency changes in interest rates around FOMC announcements are a standard method of measuring monetary policy shocks. However, some recent studies have documented puzzling effects of these shocks on private-sector forecasts of GDP, unemployment, or inflation that are opposite in sign to what standard macroeconomic models would predict. This evidence has been viewed as supportive of a “Fed information effect” channel of monetary policy, whereby an FOMC tightening (easing) communicates that the economy is stronger (weaker) than the public had expected. We show that these empirical results are also consistent with a “Fed response to news” channel, in which incoming, publicly available economic news causes both the Fed to change monetary policy and the private sector to revise its forecasts. We provide substantial new evidence that distinguishes between these two channels and strongly favors the latter; for example, (i) regressions that include the previously omitted public macroeconomic news, (ii) high-frequency stock market responses to Fed announcements, and (iii) a new survey that we conduct of individual Blue Chip forecasters all indicate that the Fed and private sector are simply responding to the same public news, and that there is little if any role for a “Fed information effect”.

This paper investigates the role of monetary policy uncertainty for the transmission of FOMC actions to financial markets using a novel model-free measure of uncertainty based on derivative prices. We document a systematic pattern in monetary policy uncertainty over the course of the FOMC meeting cycle: On FOMC announcement days uncertainty tends to decline substantially, indicating the resolution of policy uncertainty. This decline is then reversed over the first two weeks of the intermeeting FOMC cycle. Both the level and the changes in uncertainty play an important role for the transmission of monetary policy to financial markets. First, changes in uncertainty have substantial effects on a variety of asset prices that are distinct from the effects of the conventional policy surprise measure. For example, the Fed's forward guidance announcements affected asset prices not only by adjusting the expected policy path but also by changing market-perceived uncertainty about this path. Second, at high levels of uncertainty a monetary policy surprise has only modest effects on assets, whereas with low uncertainty the impact is significantly more pronounced.

Conditional yield skewness is an important summary statistic of the state of the economy. It exhibits pronounced variation over the business cycle and with the stance of monetary policy, and a tight relationship with the slope of the yield curve. Most importantly, variation in yield skewness has substantial forecasting power for future bond excess returns, high-frequency interest rate changes around FOMC announcements, and consensus survey forecast errors for the ten-year Treasury yield. The COVID pandemic did not disrupt these relations: historically high skewness correctly anticipated the run-up in long-term Treasury yields starting in late 2020. The connection between skewness, survey forecast errors, excess returns, and departures of yields from normality is consistent with a theoretical framework where one of the agents has biased beliefs.

The social discount rate is a crucial element required for valuing future damages from climate change. A consensus has emerged that discount rates should be declining with horizon, i.e., that the term structure of discount rates should have a negative slope. However, much controversy remains about the appropriate the overall level of discount rates.We contribute to this debate from a macro-finance perspective, based on the insight that the equilibrium real interest rate, commonly known as r*, is the crucial determinant of the level of discount rates. First, we show theoretically how r* anchors the term structure of discount rates, using the modern macro-finance theory of the term structure of interest rates to provide a new perspective on classic results about social discount rates. Second, we show empirically that new macro-finance estimates of r* have fallen substantially over the past quarter century---consistent with a broader literature that documents such a secular decline. Bayesian estimation of a state-space model for Treasury yields, inflation and the real interest rate allows us to quantify both the decline in r* and the resulting downward shift of the term structure of social discount rates. Third, we document that this decline in r* and the social discount rate boosts the social cost of carbon and has quantitatively important implications for assessing the economic consequences of climate change. In essence, we demonstrate that the lower new normal for interest rates implies a higher new normal for the present value of climate change damages.

High-frequency changes in interest rates around FOMC announcements are a standard method of measuring monetary policy shocks. However, some recent studies have documented puzzling effects of these shocks on private-sector forecasts of inflation, unemployment, or real GDP, which have the opposite sign from what standard macroeconomic models would predict. This evidence has been viewed as supportive of a “Fed information effect” channel of monetary policy, whereby an FOMC tightening (easing) communicates that the economy is stronger (weaker) than the public had expected. We show that these empirical results are also consistent with a “Fed response to news” channel, in which incoming, publicly available economic news causes both the Fed to change monetary policy and the private sector to revise its forecasts. We provide substantial new evidence that distinguishes between these two channels and strongly favors the latter; for example, (i) high-frequency stock market responses to Fed announcements, (ii) a new survey that we conduct of individual Blue Chip forecasters, and (iii) regressions that include the previously omitted public macroeconomic data releases all indicate that the Fed and Blue Chip forecasters are simply responding to the same public news, and that there is little if any role for a "Fed information effect".

Social discount rates (SDRs) are crucial for evaluating the costs of climate change. We show that the fundamental anchor for market-based SDRs is the equilibrium or steady-state real interest rate. Empirical interest rate models that allow for shifts in this equilibrium real rate find that it has declined notably since the 1990s, and this decline implies that the entire term structure of SDRs has shifted lower as well. Accounting for this new normal of persistently lower interest rates substantially boosts estimates of the social cost of carbon and supports a climate policy with stronger carbon mitigation strategies.

IntroductionKeeping Canadians safe requires a robust public health (PH) system. This is especially true when there is a PH emergency, like the COVID-19 pandemic. Social media, like Twitter and Facebook, is an important information channel because most people use the internet for their health information. The PH sector can use social media during emergency events for (1) PH messaging, (2) monitoring misinformation, and (3) responding to questions and concerns raised by the public. In this study, we ask: what is the Canadian PH risk communication response to the COVID-19 pandemic in the context of social media?Methods and analysisWe will conduct a case study using content and sentiment analysis to examine how provinces and provincial PH leaders, and the Public Health Agency of Canada and national public heath leaders, engage with the public using social media during the first wave of the pandemic (1 January–3 September 2020). We will focus specifically on Twitter and Facebook. We will compare findings to a gold standard during the emergency with respect to message content.Ethics and disseminationWestern University’s research ethics boards confirmed that this study does not require research ethics board review as we are using social media data in the public domain. Using our study findings, we will work with PH stakeholders to collaboratively develop Canadian social media emergency response guideline recommendations for PH and other health system organisations. Findings will also be disseminated through peer-reviewed journal articles and conference presentations.

Previous research has established that the Federal Reserve's large scale asset purchases (LSAPs) significantly influenced international bond yields. We use dynamic term structure models to uncover to what extent signaling and portfolio balance channels caused these declines. For the U.S. and Canada, the evidence supports the view that LSAPs had substantial signaling effects. For Australian and German yields, signaling effects were present but likely more moderate, and portfolio balance effects appear to have played a relatively larger role than in the U.S. and Canada. Portfolio balance effects were small for Japanese yields and signaling effects basically nonexistent. These findings about LSAP channels are consistent with predictions based on interest rate dynamics during normal times: Signaling effects tend to be large for countries with strong yield responses to conventional U.S. monetary policy surprises, and portfolio balance effects are consistent with the degree of substitutability across international bonds, as measured by the covariance between foreign and U.S. bond returns.

This paper provides new evidence on the importance of inflation expectations for variation in nominal interest rates, based on both market-based and survey-based measures of inflation expectations. Using the information in TIPS breakeven rates and inflation swap rates, I document that movements in inflation compensation are important for explaining variation in long-term nominal interest rates, both unconditionally as well as conditionally on macroeconomic data surprises. Daily changes in inflation compensation and changes in long-term nominal rates generally display a close statistical relationship. The sensitivity of inflation compensation to macroeconomic data surprises is substantial, and it explains a sizable share of the macro response of nominal rates. The paper also documents that survey expectations of inflation exhibit significant comovement with variation in nominal interest rates, as well as significant responses to macroeconomic news.

Term premia implied by maximum likelihood estimates of affine term structure models are misleading because of small-sample bias. We show that accounting for this bias alters the conclusions about the trend, cycle, and macroeconomic determinants of the term premia estimated in Wright (2011). His term premium estimates are essentially acyclical, and often just parallel the secular trend in longterm interest rates. In contrast, bias-corrected term premia show pronounced countercyclical behavior, consistent with theoretical and empirical arguments about movements in risk premia. (JEL E31, E43, E52, G12, H63) In an important recent paper, Wright ( 2011) presents a new international interest rate dataset and estimates term premia in far-ahead forward rates using an affine Gaussian dynamic term structure model ( DTSM) as well as a survey-based method. He then relates these empirical term premia to macroeconomic conditions, notably inflation uncertainty. In particular, he shows that the marked secular decline in estimated forward term premia across countries is correlated with decreases in measures of uncertainty around forecasts of future inflation. In this comment, we demonstrate that his model-based term premium estimates are distorted by smallsample bias in the estimated model parameters. Accounting for this bias changes the behavior of the resulting term premium estimates in terms of their trend and cyclical behavior. In an affine Gaussian DTSM, bond yields are determined by a small number of risk factors, and the dynamic evolution of these risk factors follows a Gaussian vector autoregression ( VAR ). If the risk factors in such a model are observable, and if the model is “maximally flexible,” i.e., if it has no parameter restrictions beyond those required for identification, we know from Joslin, Singleton, and Zhu ( 2011) that the maximum likelihood estimates of the VAR parameters can be found by

The affine dynamic term structure model (DTSM) is the canonical empirical finance representation of the yield curve. However, the possibility that DTSM estimates may be distorted by small-sample bias has been largely ignored. We show that conventional estimates of DTSM coefficients are indeed severely biased, and this bias results in misleading estimates of expected future short-term interest rates and of long-maturity term premia. We provide a variety of bias-corrected estimates of affine DTSMs, for both maximally flexible and overidentified specifications. Our estimates imply interest rate expectations and term premia that are more plausible from a macrofinance perspective. This article has supplementary material online.

Restrictions on the risk-pricing in dynamic term structure models (DTSMs) can unleash the power of no-arbitrage by creating a tighter link between cross-sectional and time-series variation of interest rates. This paper presents a new econometric framework for estimation of affine Gaussian DTSMs under restrictions on risk prices, which addresses the issues of a large model space and of model uncertainty using a Bayesian approach. A simulation study demonstrates the good performance of the proposed method, both for model choice and for inference about the objects of interest. I obtain novel results for the U.S. Treasury yield curve. The data strongly favor tight restrictions on risk pricing: only level risk is priced, and only changes in the slope affect term premia. Incorporating the restrictions into an otherwise standard model substantially alters its conclusions. Interest rate persistence is significantly higher than in a maximally-flexible model, hence expectations of future short rates are more variable, and the role for term premia is somewhat diminished. Hence, restrictions on risk prices help resolve the puzzle of implausibly stable short-rate expectations which has plagued this literature. Restricted models attribute a larger share of the secular decline in long-term interest rates over the last twenty years to the expectations component, consistent with survey evidence on expectations of future interest rates and inflation.

A consensus has recently emerged that variables beyond the level, slope, and curvature of the yield curve can help predict bond returns. This paper shows that the statistical tests underlying this evidence are subject to serious small-sample distortions. We propose more robust tests, including a novel bootstrap procedure specifically designed to test the "spanning hypothesis." We revisit the evidence in five published studies, find most rejections of the spanning hypothesis to be spurious, and conclude that the current consensus is wrong. Only the level and the slope of the yield curve are robust predictors of bond returns.

Long-term U.S. government bond yields have trended down for more than two decades, but identifying the source of this decline is difficult. A new methodology suggests that reductions in long-run expectations of inflation and inflation-adjusted interest rates have played a significant role in the secular decline in yields. In contrast, standard statistical finance methods appear to overemphasize the effects of lower risk premiums and reduced uncertainty about future inflation.

We show that conventional dynamic term structure models (DTSMs) estimated on recent U.S. data severely violate the zero lower bound (ZLB) on nominal interest rates and deliver poor forecasts of future short rates. In contrast, shadow-rate DTSMs account for the ZLB by construction, capture the resulting distributional asymmetry of future short rates, and achieve good forecast performance. These models provide more accurate estimates of the most likely path for future monetary policy --- including the timing of policy liftoff from the ZLB and the pace of subsequent policy tightening. We also demonstrate the benefits of including macroeconomic factors in a shadow-rate DTSM when yields are constrained near the ZLB.

The Federal Reserve has indicated that it may raise the federal funds rate from its current value near zero in 2015. This forward policy guidance is broadly consistent with expectations from business surveys on the most likely timing for the funds rate liftoff. It also appears in line with estimates of policy liftoff from forward interest rates derived from Treasury yields. However, in interpreting forward rates, it is important to account for the zero lower bound on interest rates.

Previous research has established that the Federal Reserve large scale asset purchases (LSAPs) significantly influenced international bond yields. This paper analyzes the channels through which these effects occurred. We use dynamic term structure models to decompose international yield changes into changes in term premia and expected short rates. The conclusions for most countries are model dependent. Models that impose a unit root tend to imply large signaling effects for Australia, Canada, Germany and the United States. Models that do not restrict persistence imply negligible signaling effects for any country. Our preferred bias-corrected model implies large signaling effects for Canada and the United States. The idea that LSAP announcements signal information about Canadian rates is intuitively attractive because conventional US monetary policy shocks strongly predict Canadian rates.

Market expectations about the Federal Reserve’s policy rate involve both the future path of that rate and the uncertainty surrounding that path. Fed policy actions have historically been preceded by high levels of uncertainty, which decline after the policy is made public. Recently, measures of near-term interest rate uncertainty have fallen to historical lows, due partly to a Fed policy rate near zero. Unconventional monetary policies have substantially lowered both expectations and uncertainty about the future path of the Fed’s policy rate.

Past rounds of large-scale asset purchases by the Federal Reserve have lowered yields not only on the targeted securities, but also on various private borrowing rates. In particular, yields on corporate bonds and primary mortgage rates decreased in response to Fed asset purchase announcements. Notably, however, the link between rates on mortgage-backed securities and actual mortgage rates has weakened in the wake of the financial crisis.

Restrictions on the risk-pricing in dynamic term structure models (DTSMs) tighten the link between cross-sectional and time-series variation of interest rates, and make absence of arbitrage useful for inference about expectations. This paper presents a new econometric framework for estimation of affine Gaussian DTSMs under restrictions on risk prices, which addresses the issues of a large model space and of model uncertainty using a Bayesian approach. A simulation study demonstrates the good performance of the proposed method. Data for U.S. Treasury yields calls for tight restrictions on risk pricing: only level risk is priced, and only changes in the slope affect term premia. Incorporating the restrictions changes the model-implied short-rate expectations and term premia. Interest rate persistence is higher than in a maximally-flexible model, hence expectations of future short rates are more variable--restrictions on risk prices help resolve the puzzle of implausibly stable short-rate expectations in this literature. Consistent with survey evidence and conventional macro wisdom, restricted models attribute a large share of the secular decline in long-term interest rates to expectations of future nominal short rates.

This paper provides new estimates of the impact of monetary policy actions and macroeconomic news on the term structure of nominal interest rates. The key novelty is to parsimoniously capture the impact of news on all interest rates using a simple no-arbitrage model. The different types of news are analyzed in a common framework by recognizing their heterogeneity, which allows for a systematic comparison of their effects. This approach leads to novel empirical findings: First, monetary policy causes a substantial amount of volatility in both short-term and long-term interest rates. Second, macroeconomic data surprises have small and mostly insignificant effects on the long end of the term structure. Third, the term-structure response to macroeconomic news is consistent with considerable interest-rate smoothing by the Federal Reserve. Fourth, monetary policy surprises are multidimensional while macroeconomic surprises are one-dimensional.

How do monetary policy expectations and term premia respond to news? This paper provides new answers to this question by means of a dynamic term structure model (DTSM) in which risk prices are restricted. This leads to more precise and more reliable estimates of expectations and term premium components. I provide a new econometric framework for DTSM estimation that allows the researcher to select plausible constraints from a large set of restrictions, to correctly quantify statistical uncertainty, and to incorporate model uncertainty in the inference about risk pricing. The main empirical result is that under the restrictions favored by the data the expectations component, and not the term premium, accounts for the majority of high-frequency movements of long-term interest rates and for essentially all of their procyclical response to macroeconomic news. At both high and low frequencies, term premia are more stable than implied by a DTSM with unconstrained risk prices. The apparent disconnect between long-term rates and policy rates that has puzzled macroeconomists for some time is resolved by appropriately restricting the risk adjustment in models for bond pricing.

Federal Reserve announcements of future purchases of longer-term bonds may affect asset prices by changing market expectations of the future supply of targeted securities. Such announcements may also affect asset prices by signaling that the stance of conventional monetary policy is likely to remain loose for longer than previously anticipated. Research suggests that these signaling effects were a major contributor to the cumulative declines in Treasury security yields following the eight Fed announcements in 2008 and 2009 about its first round of large-scale asset purchases.

To understand the effects of news on bond markets, it is instructive to look beyond individual maturities and consider the entire term structure of interest rates. For example, unexpected changes in monthly nonfarm payroll employment numbers cause large movements at short and medium maturities, but do not affect long-term interest rates. Inflation news affects the long end of the term structure. Monetary policy actions vary in their effects on interest rates, but cause volatility at all maturities, including distant forward rates.

Affine dynamic term structure models (DTSMs) are the standard finance representation of the yield curve. However, the literature on DTSMs has ignored the coefficient bias that plagues estimated autoregressive models of persistent time series. We introduce new simulation-based methods for reducing or even eliminating small-sample bias in empirical affine Gaussian DTSMs. With these methods, we show that conventional estimates of DTSM coefficients are severely biased, which results in misleading estimates of expected future short-term interest rates and long-maturity term premia. Our unbiased DTSM estimates imply risk-neutral rates and term premia that are more plausible from a macro-finance perspective.

Previous research has emphasized the portfolio balance effects of Federal Reserve bond purchases, in which a reduced bond supply lowers term premia. In contrast, we find that such purchases have important signaling effects that lower expected future short-term interest rates. Our evidence comes from a model-free analysis and from dynamic term structure models that decompose declines in yields following Federal Reserve announcements into changes in risk premia and expected short rates. To overcome problems in measuring term premia, we consider bias-corrected model estimation and restricted risk price estimation. In comparison with other studies, our estimates of signaling effects are larger in magnitude and statistical significance.