Post-COVID Inflation Dynamics: Higher for Longer (with Randal Verbrugge)
Journal of Forecasting (2023) forthcoming, [journal version] [working paper] [latest version: paper and online appendix] 

Improving Inflation Forecasts Using Robust Measures (with Randal Verbrugge)
International Journal of Forecasting (2023) forthcoming, [journal version] [working paper]

The Hard Road to a Soft Landing: Evidence from a (Modestly) Nonlinear Structural Model (with Randal Verbrugge)
Energy Economics (2023) 123:106733 [journal version] [working paper]

Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach (with Edward Knotek II)
International Journal of Forecasting (2021) forthcoming [journal version] [working paper]

Asymmetric Responses of Consumer Spending to Energy Prices: A Threshold VAR Approach (with Edward Knotek II)
Energy Economics (2021) 95:105127 (lead article) [journal version] [working paper]

Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy (with Ellis W. Tallman)
International Journal of Forecasting (2020) 36(2): 373–398 [journal version] [working paper]

Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting (with Edward Knotek II)
International Journal of Forecasting (2019) 35(4): 1708-1724 [journal version] [working paper]

The Usefulness of the Median CPI in Bayesian VARs Used for Macroeconomic Forecasting and Policy (with Brent Meyer)
Empirical Economics (2019) 57(2): 603–630 [journal version] [working paper]

Nowcasting US Headline and Core Inflation (with Edward Knotek II)
Journal of Money, Credit, and Banking (2017) 49(5): 931–968 [journal version] [working paper]

Forecasting Inflation: Phillips Curve Effects on Services Price Measures (with Ellis W. Tallman)
International Journal of Forecasting (2017) 33(2): 442–457 [journal version] [working paper]

Evidence of Forward-Looking Loan–Loss Provisioning with Credit Market Information (with L. Balasubramanyan & James B. Thomson)
Journal of Financial Services Research (2017) 52(3): 191–223 [journal version] [working paper]

Credit Market Information Feedback (with Lakshmi Balasubramanyan, Ben Craig, and James B. Thomson)
Atlantic Economic Journal (2016) 44(3): 405–407 [journal version] [working paper]

Nowcasting Inflation  (2024)  (with Edward Knotek II)  [Chapter prepared for Handbook of Inflation]
        Federal Reserve Bank of Cleveland, Working Paper No. 24-06 [working paper]

Abstract

This chapter summarizes the mixed-frequency methods commonly used for nowcasting inflation. It discusses the importance of key high-frequency data in producing timely and accurate inflation nowcasts. In the US, consensus surveys of professional forecasters have historically provided an accurate benchmark for inflation nowcasts because they incorporate professional judgment to capture idiosyncratic factors driving inflation. Using real-time data, we show that a relatively parsimonious mixed-frequency model produces superior point and density nowcasting accuracy for headline inflation and competitive nowcasting accuracy for core inflation compared with surveys of professional forecasters over a long sample spanning 1999–2022 and over a short sample focusing on the period since the start of the pandemic. 

The Effect of Component Disaggregation on Measures of the Median and Trimmed-Mean CPI  (2024)  (with Christian Garciga and Randal Verbrugge)  [Under review]
        Federal Reserve Bank of Cleveland, Working Paper No. 24-02 [working paper]

Abstract

For decades, the Federal Reserve Bank of Cleveland (FRBC) has produced median and trimmed-mean consumer price index (CPI) measures. These have proven useful in various contexts, such as forecasting and understanding post-COVID inflation dynamics. Revisions to the FRBC methodology have historically involved increasing the level of disaggregation in the CPI components, which has improved accuracy. Thus, it may seem logical that further disaggregation would continue to enhance its accuracy. However, we theoretically demonstrate that this may not necessarily be the case. We then explore the empirical impact of further disaggregation along two dimensions: shelter and non-shelter components. We find that significantly increasing the disaggregation in the shelter indexes, when combined with only a slight increase in non-shelter disaggregation, improves the ability of the median and trimmed-mean CPI to track the medium-term trend in CPI inflation and marginally increases predictive power over future movements in CPI inflation. Finally, we examine the practical implications of our preferred degree of disaggregation. Our preferred measure of the median CPI suggests that trend inflation was lower pre-pandemic, while both our preferred median and trimmed-mean measures suggest a faster acceleration in trend inflation in 2021. We also find that higher disaggregation marginally weakens the Phillips curve relationship between median CPI inflation and the unemployment gap, though it remains statistically significant. 

Forecasting Core Inflation and Its Goods, Housing, and Supercore Components  (2023)  (with Todd Clark and Matthew Gordon)
        Federal Reserve Bank of Cleveland, Working Paper No. 23-34 [working paper] [Under review]

Abstract

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 projection puts core inflation at 2.6 percent in 2026, well below its 2023 level but still elevated relative to the Federal Reserve's 2 percent objective. The probability that core inflation will return to 2 percent or less is much higher when conditioning on goods or non-housing services inflation slowing to pre-pandemic levels than when conditioning on these components remaining above the same thresholds.  Scenario analysis indicates that slower wage growth will likely be associated with reduced inflation in all three components, especially goods and non-housing services, helping to return core inflation to near the 2 percent target by 2026.

The Distributional Predictive Content of Measures of Inflation Expectations  (2023)  (with James Mitchell)
        Federal Reserve Bank of Cleveland, Working Paper No. 23-31 [working paper] [Under review]

Abstract

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 the upper tail. The predictive ability of measures of inflation expectations is greatest when combined. We show that it is helpful to let the combination weights on different agents’ expectations of inflation vary by quantile when assessing inflationary pressures probabilistically.

A Unified Framework to Estimate Macroeconomic Stars (2021)
Federal Reserve Bank of Cleveland, Working Paper No. 21-23r
        [working paper] [latest version: paper and online appendix] [download stars' estimates] [Under review]

Abstract
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, the 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 and stars to improve the stars’ econometric estimation. Our approach permits time variation in the relationships between various components, including time variation in error variances. To tractably estimate the large multivariate model, we use a recently developed precision sampler that relies on Bayesian methods. The by-products of this approach are the time-varying estimates of the wage and price Phillips curves, and the pass-through between prices and wages, both of which provide new insights into these empirical relationships’ instability in US data. Furthermore, our estimates of the stars are among the most precise. Lastly, we document the competitive real-time forecasting properties of the model and, separately, the usefulness of stars’ estimates as steady-state values in external models.

A Medium Scale Forecasting Model for Monetary Policy (2011) (with Kenneth Beauchemin)
Federal Reserve Bank of Cleveland, Working Paper No. 11-28 [working paper] [Permanent Working Paper]

Abstract
This paper presents a 16-variable Bayesian VAR forecasting model of the U.S. economy for use in a monetary policy setting. The variables that comprise the model are selected not only for their effectiveness in forecasting the primary variables of interest, but also for their relevance to the monetary policy process. In particular, the variables largely coincide with those of an augmented New-Keynesian DSGE model. We provide out-of sample forecast evaluations and illustrate the computation and use of predictive densities and fan charts. Although the reduced form model is the focus of the paper, we also provide an example of structural analysis to illustrate the macroeconomic response of a monetary policy shock.

PhD Thesis submitted to the Department of Economics, University of Strathclyde, UK. (2021)
Essays in Forecasting and Empirical Macroeconomics  [link]