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Working Paper
Forecasting U.S. Economic Activity with a Small Information Set
Cooper, Daniel H.; Olivei, Giovanni P.; Rhodenhiser, Hannah (2025-06-01)
We provide a parsimonious setup for forecasting U.S. GDP growth and the unemployment rate based on a few fundamental drivers. This setup yields forecasts that are reasonably accurate compared with private-sector and Federal Reserve forecasts over the 1984–2019 and post COVID-19 pandemic periods. This result is achieved by jointly estimating the processes for GDP growth and the unemployment rate, with the constraint that GDP and unemployment follow Okun’s law in first differences. This setup can be easily extended to replace the variables in the information set with factors that might ...
Working Papers , Paper 25-4

Working Paper
The Effect of Component Disaggregation on Measures of the Median and Trimmed-Mean CPI
Garciga, Christian; Verbrugge, Randal J.; Zaman, Saeed (2024-01-04)
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 ...
Working Papers , Paper 24-02

Working Paper
Total Recall? Evaluating the Macroeconomic Knowledge of Large Language Models
Crane, Leland D.; Karra, Akhil; Soto, Paul E. (2025-06-25)
We evaluate the ability of large language models (LLMs) to estimate historical macroeconomic variables and data release dates. We find that LLMs have precise knowledge of some recent statistics, but performance degrades as we go farther back in history. We highlight two particularly important kinds of recall errors: mixing together first print data with subsequent revisions (i.e., smoothing across vintages) and mixing data for past and future reference periods (i.e., smoothing within vintages). We also find that LLMs can often recall individual data release dates accurately, but aggregating ...
Finance and Economics Discussion Series , Paper 2025-044

Working Paper
Disentangling rent index differences: data, methods, and scope
Adams, Brian; Loewenstein, Lara; Montag, Hugh; Verbrugge, Randal (2022-12-19)
Working Papers , Paper 22-38

Working Paper
Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions
Clark, Todd E.; Carriero, Andrea; Marcellino, Massimiliano (2020-09-22)
A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and ...
Working Papers , Paper 20-02R

Working Paper
Indirect Consumer Inflation Expectations: Theory and Evidence
Hajdini, Ina; Knotek, Edward S.; Leer, John; Pedemonte, Mathieu; Rich, Robert W.; Schoenle, Raphael (2022-11-22)
Based on indirect utility theory, we introduce a novel methodology of measuring inflation expectations indirectly. This methodology starts at the individual level, asking consumers about the change in income required to buy the same amounts of goods and services one year ahead. Analytically, our methodology possesses smaller ex-post aggregate inflation forecast errors relative to forecasts based on conventional survey questions. We ask this question in a large-scale, high-frequency survey of consumers in the US and 14 countries, and we show that indirect consumer inflation expectations ...
Working Papers , Paper 22-35

Working Paper
Finite-Order VAR Representation of Linear Rational Expectations Models: With Some Lessons for Monetary Policy
Martinez-Garcia, Enrique (2016-09-01)
This paper considers the characterization via finite-order VARs of the solution of a large class of linear rational expectations (LRE) models. I propose a unified approach that uses a companion Sylvester equation to check the existence and uniqueness of a solution to the canonical (first-order) LRE model in finite-order VAR form and a quadratic matrix equation to characterize it decoupling the backward- and forward-looking aspects of the model. I also investigate the fundamentalness of the shocks recovered. Solving LRE models by this procedure is straightforward to implement, general in its ...
Globalization Institute Working Papers , Paper 285

Report
Revisiting useful approaches to data-rich macroeconomic forecasting
Kapetanios, George; Groen, Jan J. J. (2008)
This paper analyzes the properties of a number of data-rich methods that are widely used in macroeconomic forecasting, in particular principal components (PC) and Bayesian regressions, as well as a lesser-known alternative, partial least squares (PLS) regression. In the latter method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the covariance between a target variable and these common components is maximized. Existing studies have focused on modelling the target variable as a function of a finite set of unobserved common factors that ...
Staff Reports , Paper 327

Report
Online Estimation of DSGE Models
Herbst, Edward; Schorfheide, Frank; Cai, Michael; Matlin, Ethan; Del Negro, Marco; Sarfati, Reca (2019-08-01)
This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, explore the benefits of an SMC variant we call generalized tempering for ?online? estimation, and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts of DSGE models with and without financial frictions and document the benefits of conditioning DSGE model forecasts on nowcasts ...
Staff Reports , Paper 893

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