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Journal Article
Challenges in identifying interbank loans
Copeland, Adam; Armantier, Olivier (2015-21-01)
Although interbank lending markets play a key role in the financial system, the lack of disaggregated data often makes the analysis of these markets difficult. To address this problem, recent academic papers focusing on unsecured loans of central bank reserves have employed an algorithm in an effort to identify individual transactions that are federal funds loans. The accuracy of the algorithm, however, is not known. The authors of this study conduct a formal test with U.S. data and find that the rate of false positives produced by one of these algorithms is on average 81 percent; the rate of ...
Economic Policy Review , Issue 21-1 , Pages 1-17

Journal Article
An overview of the Survey of Consumer Expectations
Zafar, Basit; Van der Klaauw, Wilbert; Armantier, Olivier; Topa, Giorgio (2017-23-02)
The authors present an overview of the New York Fed?s Survey of Consumer Expectations, a monthly online survey of a rotating panel of household heads. The survey collects timely information on respondents? expectations and decisions on a broad variety of topics, including inflation, household finance, the labor market, and the housing market. It has three main goals: (1) measuring consumer expectations at a high frequency, (2) understanding how these expectations are formed, and (3) investigating the link between expectations and behavior. The authors discuss the origins of the survey, the ...
Economic Policy Review , Issue 23-2 , Pages 51-72

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
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. (2020-12-22)
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
Assessing the macroeconomic impact of bank intermediation shocks: a structural approach
Chen, Kaiji; Zha, Tao (2015-08-01)
We take a structural approach to assessing the empirical importance of shocks to the supply of bank-intermediated credit in affecting macroeconomic fluctuations. First, we develop a theoretical model to show how credit supply shocks can be transmitted into disruptions in the production economy. Second, we use the unique micro-banking data to identify and support the model's key mechanism. Third, we find that the output effect of credit supply shocks is not only economically and statistically significant but also consistent with the vector autogression evidence. Our mode estimation indicates ...
FRB Atlanta Working Paper , Paper 2015-8

Working Paper
An Assessment of the National Establishment Time Series (NETS) Database
Crane, Leland D.; Barnatchez, Keith; Decker, Ryan A. (2017-11-03)
The National Establishment Time Series (NETS) is a private sector source of U.S. business microdata. Researchers have used state-specific NETS extracts for many years, but relatively little is known about the accuracy and representativeness of the nationwide NETS sample. We explore the properties of NETS as compared to official U.S. data on business activity: The Census Bureau's County Business Patterns (CBP) and Nonemployer Statistics (NES) and the Bureau of Labor Statistics' Quarterly Census of Employment and Wages (QCEW). We find that the NETS universe does not cover the entirety of the ...
Finance and Economics Discussion Series , Paper 2017-110

Working Paper
Employer Reallocation During the COVID-19 Pandemic: Validation and Application of a Do-It-Yourself CPS
Bick, Alexander; Blandin, Adam (2022-05-03)
Economists have recently begun using independent online surveys to collect national labor market data. Questions remain over the quality of such data. This paper provides an approach to address these concerns. Our case study is the Real-Time Population Survey (RPS), a novel online survey of the US built around the Current Population Survey (CPS). The RPS replicates core components of the CPS, ensuring comparable measures that allow us to weight and rigorously validate our results using a high-quality benchmark. At the same time, special questions in the RPS yield novel information regarding ...
Working Papers , Paper 2022-012

Working Paper
Bill of Lading Data in International Trade Research with an Application to the COVID-19 Pandemic
Flaaen, Aaron B.; Haberkorn, Flora; Lewis, Logan T.; Monken, Anderson; Pierce, Justin R.; Rhodes, Rosemary; Yi, Madeleine (2021-10-15)
We evaluate high-frequency bill of lading data for its suitability in international trade research. These data offer many advantages over both other publicly accessible official trade data and confidential datasets, but they also have clear drawbacks. We provide a comprehensive overview for potential researchers to understand these strengths and weaknesses as these data become more widely available. Drawing on the strengths of the data, we analyze three aspects of trade during the COVID-19 pandemic. First, we show how the high-frequency data capture features of the within-month collapse ...
Finance and Economics Discussion Series , Paper 2021-066

Working Paper
Improved Estimation of Poisson Rate Distributions through a Multi-Mode Survey Design
Hitczenko, Marcin (2021-02-03)
Researchers interested in studying the frequency of events or behaviors among a population must rely on count data provided by sampled individuals. Often, this involves a decision between live event counting, such as a behavioral diary, and recalled aggregate counts. Diaries are generally more accurate, but their greater cost and respondent burden generally yield less data. The choice of survey mode, therefore, involves a potential tradeoff between bias and variance of estimators. I use a case study comparing inferences about payment instrument use based on different survey designs to ...
FRB Atlanta Working Paper , Paper 2021-10

Working Paper
Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
Abowd, John M.; Abramowitz, Joelle Hillary; Levenstein, Margaret Catherine; McCue, Kristin; Patki, Dhiren; Raghunathan, Trivellore; Rodgers, Ann Michelle; Shapiro, Matthew D.; Wasi, Nada; Zinsser, Dawn (2021-10-01)
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. ...
Working Papers , Paper 22-11

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