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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
Quantifying Risks to Sovereign Market Access: Methods and Challenges
Erce, Aitor; Zigraiova, Diana; Jiang, Xu (2020-02-10)
In this paper we use data from the euro area to study episodes when sovereigns lose market access. We construct a detailed dataset with potential indicators of market access tensions, and evaluate their ability to forecast episodes when market access is lost, using various econometric approaches. We find that factors associated with high market access tensions are not limited to financial markets, but also encompass developments in global demand, macroeconomic conditions and the fiscal stance. Using the top-performing indicators, we construct a number of market tension indices and use them as ...
Globalization Institute Working Papers , Paper 377

Newsletter
Looking down the road with ALEX: Forecasting U.S. GDP
Brave, Scott A.; Butters, R. Andrew; Fogarty, Michael (2020-10-23)
In this article, we examine the recovery from the recession that began with the onset of the Covid-19 pandemic in the U.S. To do so, we present and discuss for the first time the results from a mixed-frequency Bayesian vector autoregressive model called ALEX. This model uses 107 monthly and quarterly indicators of economic activity to forecast the near-term path of U.S. real gross domestic product (GDP).
Chicago Fed Letter , Issue 447 , Pages 5

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
The perils of working with Big Data and a SMALL framework you can use to avoid them
Fogarty, Michael; Butters, R. Andrew; Brave, Scott A. (2020-03-02)
The use of “Big Data” to explain fluctuations in the broader economy or guide the business decisions of a firm is now so commonplace that in some instances it has even begun to rival more traditional government statistics and business analytics. Big data sources can very often provide advantages when compared to these more traditional data sources, but with these advantages also comes the potential for pitfalls. We lay out a framework called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL ...
Working Paper Series , Paper WP-2020-35

Working Paper
Forecasting Low Frequency Macroeconomic Events with High Frequency Data
Galvão, Ana B.; Owyang, Michael T. (2020-09)
High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their confidence bands) for these low-frequency events. The new approach is compared with single-frequency alternatives using loss functions adequate to rare event forecasting. We provide evidence that: (i) weekly-sampled spread improves over monthly-sampled to predict NBER recessions, (ii) the predictive ...
Working Papers , Paper 2020-028

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

Working Paper
At-Risk Transformation for U.S. Recession Prediction
Billakanti, Rahul; Shin, Minchul (2025-10-30)
We propose a simple binarization of predictors—an “at-risk” transformation—as an alternative to the standard practice of using continuous, standardized variables in recession forecasting models. By converting predictors into indicators of unusually weak states, we demonstrate their ability to capture the discrete nature of rare events such as U.S. recessions. Using a large panel of monthly U.S. macroeconomic and financial data, we show that binarized predictors consistently improve out-of-sample forecasting performance—often making linear models competitive with flexible machine ...
Working Papers , Paper 25-34

Report
A 14-Variable Mixed-Frequency VAR Model
Beauchemin, Kenneth (2013-12-19)
This paper describes recent modifications to the mixed-frequency model vector autoregression (MF-VAR) constructed by Schorfheide and Song (2012). The changes to the model are restricted solely to the set of variables included in the model; all other aspects of the model remain unchanged. Forecast evaluations are conducted to gauge the accuracy of the revised model to standard benchmarks and the original model.
Staff Report , Paper 493

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