Comparing Apples to Apples: “Synthetic Real‑Time” Estimates of R‑Star

Estimates of the natural rate of interest, commonly called “r-star,” garner a great deal of attention among economists, central bankers, and financial market participants. The natural interest rate is the real (inflation-adjusted) interest rate expected to prevail when supply and demand in the economy are in balance and inflation is stable. The natural rate cannot be measured directly but must be inferred from other data. When assessing estimates of r-star, it is important to distinguish between real-time estimates and retrospective estimates. Real-time estimates answer the question: “What is the value of r-star based on the information available at the time?” Meanwhile, retrospective estimates answer the question: “What was r-star at some point in the past, based on the information available today?” Although the latter question may be of historical interest, the former question is typically more relevant in practice, whether in financial markets or central banks. Thus, given their different nature, comparing real-time and retrospective estimates is like comparing apples to oranges. In this Liberty Street Economics post, we address this issue by creating new “synthetic real-time” estimates of r-star in the U.S. for the Laubach-Williams (2003) and Holston-Laubach-Williams (2017) models, using vintage datasets. These estimates enable apples-to-apples comparisons of the behavior of real-time r-star estimates over the past quarter century.
Supply and Demand Drivers of Global Inflation Trends

Our previous post identified strong global components in the slow-moving and persistent dynamics of headline consumer price index (CPI) inflation in the U.S. and abroad. We labeled these global components as the Global Inflation Trend (GIT), the Core Goods Global Inflation Trend (CG-GIT) and the Food & Energy Global Inflation Trend (FE-GIT). In this post we offer a narrative of the drivers of these global inflation trends in terms of shocks that induce a trade-off for monetary policy, versus those that do not. We show that most of the surge in the persistent component of inflation across countries is accounted for by global supply shocks—that is, shocks that induce a trade-off for central banks between their objectives of output and inflation stabilization. Global demand shocks have become more prevalent since 2022. However, had central banks tried to fully offset the inflationary pressures due to sustained demand, this would have resulted in a much more severe global economic contraction.
Global Trends in U.S. Inflation Dynamics

A key feature of the post-pandemic inflation surge was the strong correlation among inflation rates across sectors in the United States. This phenomenon, however, was not confined to the U.S. economy, as similar inflationary pressures have emerged in other advanced economies. As generalized as the inflation surge was, so was its decline from the mid-2022 peak. This post explores the common features of inflation patterns in the U.S. and abroad using an extension of the Multivariate Core Trend (MCT) Inflation model, our underlying inflation tracker for the U.S. The Global MCT model purges transitory noise from international sectoral inflation data and quantifies the covariation of their persistent components—in the form of global inflation trends—along both country and sectoral dimensions. We find that global trends play a dominant role in determining the slow-moving and persistent dynamics of headline consumer price index (CPI) inflation in the U.S. and abroad, both over the pre-pandemic and post pandemic samples.
The R&D Puzzle in U.S. Manufacturing Productivity Growth

In a previous post, we provided evidence for a broad-based slowdown in productivity growth across industries and firms in the U.S. manufacturing sector starting in 2010. Since firms’ investment in research and development (R&D) for new technologies constitutes a central driver of productivity growth, in this post we ask if the observed slowdown in productivity may be due to a decline in R&D. We find that “R&D intensity” has been increasing at both the firm and industry level, even as productivity growth declines. This points to a decline in the effectiveness of R&D in generating productivity growth in U.S. manufacturing.
A New Indicator of Labor Market Tightness for Predicting Wage Inflation

A key question in economic policy is how labor market tightness affects wage inflation and ultimately prices. In this post, we highlight the importance of two measures of tightness in determining wage growth: the quits rate, and vacancies per searcher (V/S)—where searchers include both employed and non-employed job seekers. Amongst a broad set of indicators, we find that these two measures are independently the most strongly correlated with wage inflation. We construct a new index, called the Heise-Pearce-Weber (HPW) Tightness Index, which is a composite of quits and vacancies per searcher, and show that it performs best of all in explaining U.S. wage growth, including over the COVID pandemic and recovery.
Can Professional Forecasters Predict Uncertain Times?

Economic surveys are very popular these days and for a good reason. They tell us how the folks being surveyed—professional forecasters, households, firm managers—feel about the economy. So, for instance, the New York Fed’s Survey of Consumer Expectations (SCE) website displays an inflation uncertainty measure that tells us households are more uncertain about inflation than they were pre-COVID, but a bit less than they were a few months ago. The Philadelphia Fed’s Survey of Professional Forecasters (SPF) tells us that forecasters believed last May that there was a lower risk of negative 2024 real GDP growth than there was last February. The question addressed in this post is: Does this information actually have any predictive content? Specifically, I will focus on the SPF and ask: When professional forecasters indicate that their uncertainty about future output or inflation is higher, does that mean that output or inflation is actually becoming more uncertain, in the sense that the SPF will have a harder time predicting these variables?
Are Professional Forecasters Overconfident?

The post-COVID years have not been kind to professional forecasters, whether from the private sector or policy institutions: their forecast errors for both output growth and inflation have increased dramatically relative to pre-COVID (see Figure 1 in this paper). In this two-post series we ask: First, are forecasters aware of their own fallibility? That is, when they provide measures of the uncertainty around their forecasts, are such measures on average in line with the size of the prediction errors they make? Second, can forecasters predict uncertain times? That is, does their own assessment of uncertainty change on par with changes in their forecasting ability? As we will see, the answer to both questions sheds light of whether forecasters are rational. And the answer to both questions is “no” for horizons longer than one year but is perhaps surprisingly “yes” for shorter-run forecasts.
The Mysterious Slowdown in U.S. Manufacturing Productivity

Throughout the twentieth century, steady technological and organizational innovations, along with the accumulation of productive capital, increased labor productivity at a steady rate of around 2 percent per year. However, the past two decades have witnessed a slowdown in labor productivity, measured as value added per hour worked or sectoral output per hour worked. This slowdown has been particularly stark in the manufacturing sector, which historically has been a leading sector in driving the productivity of the aggregate U.S. economy. What makes this slowdown particularly puzzling is the fact that manufacturing accounts for the majority of U.S. research and development (R&D) expenditure. Despite several recent studies (see, for example, Syverson [2016]), much remains to be uncovered about the nature of this slowdown. This post illustrates a key facet of the mystery: the productivity slowdown appears to be pervasive across industries and across firms of various sizes.
Has Market Concentration in U.S. Manufacturing Increased?

The increasing dominance of large firms in the United States has raised concerns about pricing power in the product market. The worry is that large firms, facing fewer competitors, could increase their markups over marginal costs without fear of losing market share. In a recently published paper, we show that although sales of domestic firms have become more concentrated in the manufacturing sector, this development has been accompanied by the entry and growth of foreign firms. Import competition has lowered U.S. producers’ share of the U.S. market and put smaller, less efficient domestic firms out of business. Overall, market concentration in manufacturing was stable in recent decades, though import penetration has greatly altered the makeup of the U.S. manufacturing sector.
What Happens to U.S. Activity and Inflation if China’s Property Sector Leads to a Crisis?

A previous post explored the potential implications for U.S. growth and inflation of a manufacturing-led boom in China. This post considers spillovers to the U.S. from a downside scenario, one in which China’s ongoing property sector slump takes another leg down and precipitates an economic hard landing and financial crisis.