We estimate perceptions about the Fed’s monetary policy rule from panel data on professional forecasts of interest rates and macroeconomic conditions. The perceived dependence of the federal funds rate on economic conditions is time-varying and cyclical: high during tightening episodes but low during easings. Forecasters update their perceptions about the policy rule in response to monetary policy actions, measured by high-frequency interest rate surprises, suggesting that forecasters have imperfect information about the rule. The perceived rule impacts asset prices crucial for monetary policy transmission, driving how interest rates respond to macroeconomic news and explaining term premia in long-term interest rates.
The extent of future climate change is a policy choice. Using an integrated climate-economy assessment model, we estimate climate policy curves (CPCs) that link the price of carbon dioxide (CO2) to subsequent global temperatures. The resulting downward sloping CPCs quantify the inverse relationship between carbon prices and future temperatures and illustrate how climate policy choices determine climate outcomes. Our analysis can account for a variety of climate policies—for example, carbon or fuel taxes, emissions trading programs, green subsidies, and energy-efficiency regulations—all of which can be summarized by means of an effective CO2 price. Importantly, we also examine CPC uncertainty, for example, by perturbing the model’s equilibrium climate sensitivity to trace out the temperature range associated with a given CO2 price. Finally, based on the latest Intergovernmental Panel on Climate Change (IPCC) integrated-assessment model scenarios, we estimate an implicit CPC, which provides a high-level IPCC summary of the climate policy actions required to achieve global climate targets.
Published Articles (Refereed Journals and Volumes)
The conditional skewness of Treasury yields is an important indicator of the risks to the macroeconomic outlook. Positive skewness signals upside risk to interest rates during periods of accommodative monetary policy and an upward-sloping yield curve, and vice versa. Skewness has substantial predictive power for future bond excess returns, high-frequency interest rate changes around FOMC announcements, and survey forecast errors for interest rates. The estimated expectational errors, or biases in beliefs, are quantitatively important for statistical bond risk premia. These findings are consistent with a heterogeneous-beliefs model where one of the agents is wrong about consumption growth.
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.
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.
High-frequency changes in interest rates around FOMC announcements are an important tool for identifying the effects of monetary policy on asset prices and the macroeconomy. However, some recent studies have questioned both the exogeneity and the relevance of these monetary policy surprises as instruments, especially for estimating the macroeconomic effects of monetary policy shocks. For example, monetary policy surprises are correlated with macroeconomic and financial data that is publicly available prior to the FOMC announcement. We address these concerns in two ways: First, we expand the set of monetary policy announcements to include speeches by the Fed Chair, which doubles the number and importance of announcements; Second, we explain the predictability of the monetary policy surprises in terms of the “Fed response to news” channel of Bauer and Swanson (2021) and account for it by orthogonalizing the surprises with respect to macroeconomic and financial data that pre-date the announcement. Our subsequent reassessment of the effects of monetary policy yields two key results: First, estimates of the high-frequency effects on asset prices are largely unchanged; Second, estimates of the effects on the macroeconomy are substantially larger and more significant than what previous studies using high-frequency data have typically found.
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.
The relative equity pricing of more climate-friendly (“green”) versus less climate- friendly (“brown”) companies is an open question in climate finance. Previous research comes to conflicting conclusions, documenting either a “carbon premium” with brown stocks yielding higher returns, or the opposite, with green stocks outperforming brown. This paper provides new international evidence on this issue for a range of methodologies. Using carbon dioxide (CO2) emissions as reported by companies to measure their greenness, we document that green stocks across the G7 have generally provided higher returns than brown stocks for much of the past decade. We also try to reconcile our findings with previous work, and we provide some results for early 2022 that show that brown stocks outperformed green ones during the energy crisis.
Uncertainty about future policy rates plays a crucial role for the transmission of monetary policy to financial markets. We demonstrate this using event studies of FOMC announcements and a new model-free uncertainty measure based on derivatives. Over the “FOMC uncertainty cycle” announcements systematically resolve uncertainty, which then gradually ramps up again over the subsequent two weeks. Changes in monetary policy uncertainty around FOMC announcements-often due to new forward guidance-have pronounced effects on asset prices that are distinct from the effects of conventional policy surprises. Furthermore, the level of uncertainty determines the magnitude of the financial market reaction to surprises about the path of policy rates.
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 terms 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 for interest rates. The model also provides new, more plausible estimates of the term premium and accurate out-of-sample yield forecasts.
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.
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
analysis in six published studies and find that the evidence against
the spanning hypothesis is much weaker than it originally
appeared. Our results pose a serious challenge to the prevailing
Most existing macro-finance term structure models (MTSMs) appear incompatible with regression evidence of unspanned macro risk. This “spanning puzzle” appears to invalidate those models in favor of new unspanned MTSMs. However, our empirical analysis supports the previous spanned models. Using simulations to investigate the spanning implications of MTSMs, we show that a canonical spanned model is consistent with the regression evidence; thus, we resolve the spanning puzzle. In addition, direct likelihood-ratio tests find that the knife-edge restrictions of unspanned models are rejected with high statistical significance, though these restrictions have only small effects on cross-sectional fit and estimated term premia.
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.
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.
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.
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.
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.
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 long-term interest rates. In contrast, bias-corrected term premia show pronounced countercyclical behavior, consistent with theoretical and empirical arguments about movements in risk premia.
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, both for maximally-flexible and over-identified specifications. Our estimates imply short rate expectations and term premia that are more plausible from a macro-finance perspective.
Models of endogenous growth have strong empirical predictions about the determinants of technological progress. This thesis details the implications of alternative R&D-based endogenous growth models, and then surveys the empirical literature that tests different aspects of this New Growth Theory. Numerous studies attempt to test the validity of endogenous growth models but come to very different conclusions, since varying hypotheses are considered. There are few rigorous and plausible empirical assessments of whether the determinants of technological progress conform to the predictions of the theory. I provide new evidence on the relevance of R&D intensity for economic growth, using dynamic panel data methods, thereby contributing to the empirical literature that finds support for R&D-based endogenous growth models.