Activity shortfalls are more costly than strong activity. I consider optimal monetary policy under discretion with an asymmetric (activity shortfalls) loss function. The model satisfies the natural rate hypothesis. The asymmetric loss function and resulting optimal monetary policy exacerbates shortfalls in activity. The additional frequency of activity shortfalls arises from the adjustment of expectations implied by the natural rate hypothesis. The shortfalls asymmetry leads to an inflationary bias, similar to results in the time-consistency literature. Mandating a central bank objective with greater symmetry than the social loss function improves outcomes. Greater symmetry lowers the magnitude of activity shortfalls. Greater symmetry also reduces inflation bias. The model also implies that an optimal monetary policy does not accommodate fluctuations from aggregate demand shocks, as is standard in such models. As a result, the analysis implies that monetary accommodation of strength in economic activity likely requires justifications other than asymmetric costs of shortfalls.

I assess monetary policy strategies to foster price stability and labor market strength. The assessment incorporates a range of challenges, including uncertainty regarding the equilibrium real interest rate, mismeasurement of economic potential, and balancing the costs and benefits associated with employment shortfalls and labor market strength. I find that the ELB remains a significant constraint, hindering achievement of the inflation objective and worsening employment shortfalls. Symmetric policy reaction functions mitigate the most adverse effects of employment shortfalls by contributing to economic stability. Make-up strategies address ELB risks. These strategies call for policy to accommodate some period of inflation above its long-run objective following an ELB episode. I also consider an asymmetric shortfalls approach to policy. This approach provides accommodation in response to weak activity while foregoing tightening in response to strong activity. While the approach can, in principle, address ELB risks by raising inflation, it performs poorly. The shortfalls approach exacerbates economic volatility, worsens employment shortfalls, and creates excess inflationary pressures. Mismeasurement is not sufficient to limit the importance of strong responses to measured slack. Overall, monetary policy can promote price stability and labor market strength by focusing on economic stability, with a strategy targeted to address ELB risks.

Since the initial launch of inflation targeting in the early 1990s in New Zealand and a few other countries, inflation targeting has become the predominant monetary policy strategy in large advanced and emerging market economies. Inflation targeting has been remarkably successful in anchoring inflation, likely owing to core elements of the framework across central banks. Its reaction process, which adjusts the monetary policy stance to ensure the return of inflation to target, allows it to flexibly incorporate a wide range of factors while limiting the discretionary biases that can contribute to excessive inflation. The emphasis on communications about the inflation outlook promotes transparency and accountability. As a result, inflation targeting central banks have, on balance, managed well the large shocks associated with the Global Financial Crisis and COVID. Even so, there are numerous challenges discussed in this paper that are associated with calibration and communications of forward guidance, quantitative easing/tightening, and financial stability.

Since the 1990s, monetary policy research has highlighted the properties of policy rules that stabilize inflation and economic activity, the role of inflation targeting in anchoring expectations, and the constraints posed by the effective lower bound (ELB). This paper combines these themes by examining whether explicitly responding to long-run inflation expectations improves policy effectiveness. Using both a small model for intuition and a large-scale policy model for quantitative evaluation, the analysis shows that the proposed approach reinforces inflation anchoring, reduces volatility from slow-moving inflationary forces, and mitigates ELB risks. The findings suggest that policy rules incorporating long-run inflation expectations enhance stability and complement makeup strategies by addressing ELB risks through different channels. Given that central banks already emphasize inflation expectations in their communications, this strategy aligns naturally with existing policy discussions.

How will climate change affect risks to economic activity? Research on climate impacts has tended to focus on effects on the average level of economic growth. I examine whether climate change may make severe contractions in economic activity more likely using quantile regressions linking growth to temperature. The effects of temperature on downside risks to economic growth are large and robust across specifications. These results suggest the growth at risk from climate change is large—climate change may make economic contractions more likely and severe and thereby significantly impact economic and financial stability and welfare.

Treasury yields have fallen since the 1980s. Standard decompositions of Treasury yields into expected short-term interest rates and term premiums suggest term premiums account for much of the decline. In an alternative real-time decomposition, term premiums have fluctuated in a stable range, while long-run expected short-term interest rates have fallen. For example, a real-time decomposition of the 10-yr. Treasury yield shows term premiums essentially equal in late 2013 and 2023, while the long-run value of expected short-term interest rates is estimated to have fallen in a manner similar to the FOMC’s Summary of Economic Projections and estimates from research on long-run neutral interest rates. These results suggest standard decompositions may overstate the role of term premiums in fluctuations of the yield curve.

Inflation was low and stable in the United States during the first two decades of the 21st century and broke out of its stable range in 2021. Experience in the early 21st century differed from that of the second half of the 20th century, when inflation showed persistent movements including the "Great Inflation" of the 1970s. This analysis examines the extent to which the experience from 2000-2019 should lead a Bayesian decisionmaker to update their assessment of inflation dynamics. Given a prior for inflation dynamics consistent with 1960-1999 data, a Bayesian decisionmaker would not update their view of inflation persistence in light of 2000-2019 data unless they placed very low weight on their prior information. In other words, 21st century data contains very little information to dissuade a Bayesian decisionmaker of the view that inflation fluctuations are persistent, or "unanchored". The intuition for, and implications of, this finding are discussed.

The world economy has experienced the largest financial crisis in generations, a global pandemic, and a resurgence in inflation during the first quarter of the 21st century, yielding important insights for central banking.Price stability has important benefits and is the responsibility of a central bank.Achieving price stability in a complex and uncertain environment involves a credible commitment to a nominal anchor with a strong response to inflation and pre-emptive leaning against an overheating economy.Associated challenges imply that central bank communication and transparency are key elements of monetary policy strategies and tactics.Crises have emphasized the role of central banks in promoting financial stability, as financial stability is key to achieving price and economic stability, but this role increases risks to independence.Goals for central banks other than price and economic stability, complemented by financial stability, can make it more difficult for them to stabilize both inflation and economic activity.

This paper examines whether the measurement of trend inflation can be improved by using wage data in a dynamic factor model of disaggregated prices and wages for the United States. The model features time-varying coefficients and stochastic volatility. An estimate of trend inflation is a time-varying distributed lag of prices and wages, where the weight on a series depends on its time-varying volatility, persistence, and comovement with other series. The results show that wages inform estimates of trend inflation. The weight on wages was highest around 1980, drifted down through the 2000s, and returned to its 1980s value by 2022. In addition, inflation in the 2020s appears to have unmoored moderately from the 2 percent range that prevailed for decades, as the role of the persistent component of inflation increased in recent year. However, accounting for wages lowers the model's view of the increase in the volatility of trend inflation.

Inflation in 2021 reached the highest level seen since the early 1980s. High inflation has raised questions regarding the speed with which inflation may return to the 2-percent range consistent with the Federal Reserve's inflation objective.

Examining a parsimonious, yet comprehensive, set of recession signals yields three lessons. First, signals from financial markets, leading indicators of activity, and gauges of the macroeconomic environment are each useful at different horizons, with leading indicators and financial signals informative at short horizons and the state of the business cycle at medium horizons. Second, approaches emphasizing the yield curve overstate the recession signal from the term spread if other factors are not considered; given correlations among indicators, these differences are often small, but were large in 2022. Finally, simulations of a reduced-form vector autoregression of unemployment and financial conditions, which captures the time-series properties of the series well, suggest the patterns are consistent with a typical hump-shape characterization of business cycle dynamics; this synthesis tightens the connections of the recession prediction literature with the business-cycle literature.

Fluctuations in upside risks to unemployment over the medium term are examined using quantile regressions. U.S. experience reveals an elevated risk of large increases in unemployment when inflation or credit growth is high and when the unemployment rate is low. Inflation was a significant contributor to unemployment risk in the 1970s and early 1980s, and fluctuations in credit have contributed importantly to unemployment risk since the 1980s. Fluctuations in upside risk to unemployment are larger than fluctuations in the median outlook or downside risk to unemployment. Accounting for inflation and the state of the business cycle is important for understanding the role of financial conditions in shaping unemployment risk. The analysis suggests that fluctuations in near-term risks to unemployment decreased after 1984 because inflation stabilized, but fluctuations in medium-term risks increased owing to the large swings in credit in recent decades.

Recessions impose sizable hardship, with large increases in the unemployment rate and related dislocations. In addition, recessions can lead to large shifts in financial markets. As a result, economists and financial market professionals have considered prediction models to assess the probability of a recession.

Consumer price inflation in the United States, as measured by the Consumer Price Index, jumped to just above 7 percent in the twelve months ending in December 2021. Inflation in 2021 reached the highest level seen since the early 1980s. The jump in inflation outside of the range experienced over several decades has raised questions regarding the speed with which, or the degree to which, inflation may return to the 2-percent range consistent with the Federal Reserve's inflation objective.

Research has suggested that a rapid pace of nonfinancial borrowing reliably precedes financial crises, placing the pace of debt growth at the center of frameworks for the deployment of macroprudential policies. I reconsider the role of asset-prices and current account deficits as leading indicators of financial crises. Run-ups in equity and house prices and a widening of the current account deficit have substantially larger (and more statistically-significant) effects than debt growth on the probability of a financial crisis in standard crisis-prediction models. The analysis highlights the value of graphs of predicted crisis probabilities in an assessment of predictors.

The equilibrium real interest rate (r*) is the short-term real interest rate that, in the long run, is consistent with aggregate production at potential and stable inflation. Estimation of r* faces considerable econometric and empirical challenges. On the econometric front, classical inference confronts the "pile-up" problem. Empirically, the co-movement of output, inflation, unemployment, and real interest rates is too weak to yield precise estimates of r*. These challenges are addressed by applying Bayesian methods and examining the role of several "demand shifters", including asset prices, fiscal policy, and credit conditions. We find that the data provide relatively little information on the r* data-generating process, as the posterior distribution of this process lies very close to its prior. This result contrasts sharply with those for the trend growth or natural rate of unemployment processes. Second, credit spreads are very important for the estimated links between output and interest rates and hence for estimates of r*. Estimates of r* that account for this range of considerations are more stable than other estimates, with r* at the end of 2014 equal to approximately 1-1/4 percent.

Real interest rates have been persistently below historical norms over the past decade, leading economists and policy makers to view the equilibrium real interest rate as likely to be low for some time. Various definitions and approaches to estimating the equilibrium real interest rate are examined, including approaches based on the term structure of interest rates and small macroeconomic models. The individual country approaches common in the literature are extended to allow for global trend and cyclical factors. The analysis finds that global factors dominate the downward trend in the equilibrium interest rate across 13 advanced economies. A corollary of this finding is that the U.S. equilibrium rate can be informed by global developments and is recently lower than estimated in U.S.-only studies. The analysis also highlights how the common global trend confounds empirical assessments of the determinants of movements in the equilibrium rate and the need to better integrate term-structure and macroeconomic approaches.

As an alternative, two recession scenarios are presented in which interest rates change from October 2019 levels by the same amount as seen, on average, around the 1990 and 2001 recessions.

Machine learning (ML) techniques are used to construct a financial conditions index (FCI). The components of the ML‐FCI are selected based on their ability to predict the unemployment rate one‐year ahead. Three lessons for macroeconomics and variable selection/dimension reduction with large datasets emerge. First, variable transformations can drive results, emphasizing the need for transparency in selection of transformations and robustness to a range of reasonable choices. Second, there is strong evidence of nonlinearity in the relationship between financial variables and economic activity—tight financial conditions are associated with sharp deteriorations in economic activity and accommodative conditions are associated with only modest improvements in activity. Finally, the ML‐FCI places sizable weight on equity prices and term spreads, in contrast to other measures. These lessons yield an ML‐FCI showing tightening in financial conditions before the early 1990s and early 2000s recessions, in contrast to the National Financial Conditions Index (NFCI).