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Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
Aaronson, Daniel; Brave, Scott A.; Karger, Ezra; Fogarty, Michael; Krane, Spencer D. (2021-03-04)
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau’s Monthly Retail Trade Survey (MRTS). The index’s weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate implications of policies implemented during the Covid-19 pandemic. To construct the index, we extract the co-movement in several weekly data series capturing credit & debit card transactions and revenues, mobility, and consumer sentiment as well as monthly retail and food services sales excluding automotive spending (ex. autos) from the ...
Working Paper Series , Paper WP-2021-05

Working Paper
Corporate Bond Market Distress
Boyarchenko, Nina; Crump, Richard K.; Kovner, Anna; Shachar, Or (2024-09)
We link bond market functioning to future economic activity through a new measure, the Corporate Bond Market Distress Index (CMDI). The CMDI coalesces metrics from primary and secondary markets in real time, offering a unified measure to capture access to debt capital markets. The index correctly identifies periods of distress and predicts future realizations of commonly used measures of market functioning, while the converse is not the case. We show that disruptions in access to corporate bond markets have an economically material, statistically significant impact on the real economy, even ...
Working Paper , Paper 24-09

Working Paper
Dynamic Factor Copula Models with Estimated Cluster Assignments
Oh, Dong Hwan; Patton, Andrew J. (2022-05-06)
This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model ...
Finance and Economics Discussion Series , Paper 2021-029r1

Working Paper
Measuring the Euro Area Output Gap
Barigozzi, Matteo; Lissona, Claudio; Luciani, Matteo (2024-12-20)
We measure the Euro Area (EA) output gap and potential output using a non-stationary dynamic factor model estimated on a large dataset of macroeconomic and financial variables. From 2012 to 2023, we estimate that the EA economy was tighter than the European Commission and the International Monetary Fund estimate, suggesting that the slow EA growth is the result of a potential output issue, not a business cycle issue. Moreover, we find that credit indicators are crucial for pinning down the output gap, as excluding them leads to estimating a lower output gap in periods of debt build-up and a ...
Finance and Economics Discussion Series , Paper 2024-099

Working Paper
A Global Trade Model for the Euro Area
Modugno, Michele; Osbat, Chiara; D'Agostino, Antonello (2015-02-27)
We propose a model for analyzing euro area trade based on the interaction between macroeconomic and trade variables. First, we show that macroeconomic variables are necessary to generate accurate short-term trade forecasts; this result can be explained by the high correlation between trade and macroeconomic variables, with the latter being released in a more timely manner. Second, the model tracks well the dynamics of trade variables conditional on the path of macroeconomic variables during the great recession; this result makes our model a reliable tool for scenario analysis. Third, we ...
Finance and Economics Discussion Series , Paper 2015-13

Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
Aaronson, Daniel; Brave, Scott A.; Karger, Ezra; Fogarty, Michael; Krane, Spencer D. (2021-05-27)
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, mobility, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel ...
Working Paper Series , Paper WP-2021-05

Working Paper
Metro Business Cycles
Gascon, Charles S.; Rapach, David E.; Arias, Maria A. (2014-11-11)
We construct monthly economic activity indices for the 50 largest U.S. metropolitan statistical areas (MSAs) beginning in 1990. Each index is derived from a dynamic factor model based on twelve underlying variables capturing various aspects of metro area economic activity. To accommodate mixed-frequency data and differences in data-publication lags, we estimate the dynamic factor model using a maximum- likelihood approach that allows for arbitrary patterns of missing data. Our indices highlight important similarities and differences in business cycles across MSAs. While a number of MSAs ...
Working Papers , Paper 2014-46

Working Paper
Constructing Applicants from Loan-Level Data: A Case Study of Mortgage Applications
Elzayn, Hadi; Freyaldenhoven, Simon; Shin, Minchul (2025-02-04)
We develop a clustering-based algorithm to detect loan applicants who submit multiple applications (“cross-applicants”) in a loan-level dataset without personal identifiers. A key innovation of our approach is a novel evaluation method that does not require labeled training data, allowing us to optimize the tuning parameters of our machine learning algorithm. By applying this methodology to Home Mortgage Disclosure Act (HMDA) data, we create a unique dataset that consolidates mortgage applications to the individual applicant level across the United States. Our preferred specification ...
Working Papers , Paper 25-05

Working Paper
Simultaneous Spatial Panel Data Models with Common Shocks
Lu, Lina (2017-08-09)
I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a quasi-maximum likelihood (QML) method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further ...
Supervisory Research and Analysis Working Papers , Paper RPA 17-3

Journal Article
The New York Fed Staff Underlying Inflation Gauge (UIG)
Amstad, Marlene; Potter, Simon M.; Rich, Robert W. (2017-23-02)
A measure of underlying inflation that uses all relevant information, is available in real time, and forecasts inflation better than traditional underlying inflation measures?such as core inflation measures?would greatly benefit monetary policymakers, market participants, and the public. This article presents the New York Fed Staff Underlying Inflation Gauge (UIG) for the consumer price index and the personal consumption expenditures deflator. Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, ...
Economic Policy Review , Issue 23-2 , Pages 1-32

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