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Working Paper
The Distributional Predictive Content of Measures of Inflation Expectations
This paper examines the predictive relationship between the distribution of realized inflation in the US and measures of inflation expectations from households, firms, financial markets, and professional forecasters. To allow for nonlinearities in the predictive relationship we use quantile regression methods. We find that the ability of households to predict future inflation, relative to that of professionals, firms, and the market, increases with inflation. While professional forecasters are more accurate in the middle of the inflation density, households’ expectations are more useful in ...
Journal Article
Cyclical versus Acyclical Inflation: A Deeper Dive
This Commentary builds on recent research separating the components of overall inflation into cyclical and acyclical categories, but it does so at a finer level of disaggregation than previous analyses to understand recent inflation developments in the two categories. The inflation rate among cyclically sensitive subcomponents, which comprise roughly 40 percent of overall core PCE inflation, has generally continued to firm in recent years in line with a strengthening labor market and has returned to near pre-Great Recession levels. By contrast, the inflation rate among the acyclical ...
Working Paper
A Unified Framework to Estimate Macroeconomic Stars
We develop a flexible semi-structural time-series model to estimate jointly several macroeconomic "stars" -- i.e., unobserved long-run equilibrium levels of output (and growth rate of output), the unemployment rate, the real rate of interest, productivity growth, price inflation, and wage inflation. The ingredients of the model are in part motivated by economic theory and in part by the empirical features necessitated by the changing economic environment. Following the recent literature on inflation and interest rate modeling, we explicitly model the links between long-run survey expectations ...
Journal Article
Buy a home or rent? A better way to choose
Knowing whether buying a home is a better financial move for a family than renting requires a consideration of costs and options that people often neglect to factor in. One aspect of the calculation that is almost always overlooked is uncertainty--the fact that no matter how good one's estimates of the future are, the future can turn out differently than projected. Incorporating uncertainty into the rent-or-buy calculation gives potential homebuyers information that can improve their decisions. While incorporating uncertainty is complicated, it's made easier with the Cleveland Fed's online ...
Working Paper
It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting
In this paper we investigate the forecasting performance of the median CPI in a variety of Bayesian VARs (BVARs) that are often used for monetary policy. Until now, the use of trimmed-mean price statistics in forecasting inflation has often been relegated to simple univariate or ?Philips-Curve? approaches, thus limiting their usefulness in applications that require consistent forecasts of multiple macro variables. We find that inclusion of an extreme trimmed-mean measure?the median CPI?significantly improves the forecasts of both headline and core CPI. across our wide-ranging set of BVARs. ...
Working Paper
Forecasting Core Inflation and Its Goods, Housing, and Supercore Components
This paper examines the forecasting efficacy and implications of the recently popular breakdown of core inflation into three components: goods excluding food and energy, services excluding energy and housing, and housing. A comprehensive historical evaluation of the accuracy of point and density forecasts from a range of models and approaches shows that a BVAR with stochastic volatility in aggregate core inflation, its three components, and wage growth is an effective tool for forecasting inflation's components as well as aggregate core inflation. Looking ahead, the model's baseline ...
Journal Article
Using an Improved Taylor Rule to Predict When Policy Changes Will Occur
A standard Taylor rule, which expresses the federal funds rate as a function of inflation, the unemployment gap, and the past federal funds rate, tracks the federal funds rate well over time. We improve the fit by adding employment growth. Then we evaluate the effectiveness of that rule in a new way?by how accurately it predicts whether the FOMC moves the fed funds rate at its next meeting. It does pretty well, predicting nearly 70 percent of the time correctly.
Journal Article
Forecasting implications of the recent decline in inflation
Should the unanticipated slowing of inflation that has occurred since early 2012 raise doubts about the reliability of inflation forecasts? We answer this question by conducting a few exercises with a common macroeconomic forecasting model. Our results indicate that even though inflation turned out to be much lower than forecasted, it still fell well within a normal range of uncertainty, and most of the deviation from the original forecast was a response to other economic developments.
Journal Article
Forecasting inflation? Target the middle
The Median CPI is well-known as an accurate predictor of future infl ation. But it?s just one of many possible trimmed-mean inflation measures. Recent research compares these types of measures to see which tracks future inflation best. Not only does the Median CPI outperform other trims in predicting CPI inflation, it also does a better job of predicting PCE inflation, the FOMC?s preferred measure, than the core PCE.
Working Paper
Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach
We develop a flexible modeling framework to produce density nowcasts for US inflation at a trading-day frequency. Our framework: (1) combines individual density nowcasts from three classes of parsimonious mixed-frequency models; (2) adopts a novel flexible treatment in the use of the aggregation function; and (3) permits dynamic model averaging via the use of weights that are updated based on learning from past performance. Together these features provide density nowcasts that can accommodate non-Gaussian properties. We document the competitive properties of the nowcasts generated from our ...