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Jel Classification:C55 

Working Paper
Internal Migration in the United States: A Comparative Assessment of the Utility of the Consumer Credit Panel

This paper demonstrates that credit bureau data, such as the Federal Reserve Bank of New York Consumer Credit Panel/Equifax (CCP), can be used to study internal migration in the United States. It is comparable to, and in some ways superior to, the standard data used to study migration, including the American Community Survey (ACS), the Current Population Survey (CPS), and the Internal Revenue Service (IRS) county-to-county migration data. CCP-based estimates of migration intensity, connectivity, and spatial focusing are similar to estimates derived from the ACS, CPS, and IRS data. The CCP can ...
Working Papers (Old Series) , Paper 1804

Working Paper
Identifying Financial Crises Using Machine Learning on Textual Data

We use machine learning techniques on textual data to identify financial crises. The onset of a crisis and its duration have implications for real economic activity, and as such can be valuable inputs into macroprudential, monetary, and fiscal policy. The academic literature and the policy realm rely mostly on expert judgment to determine crises, often with a lag. Consequently, crisis durations and the buildup phases of vulnerabilities are usually determined only with the benefit of hindsight. Although we can identify and forecast a portion of crises worldwide to various degrees with ...
International Finance Discussion Papers , Paper 1374

Working Paper
Artificial Intelligence Methods for Evaluating Global Trade Flows

International trade policies remain in the spotlight given the recent rethink on the benefits of globalization by major economies. Since trade critically affects employment, production, prices and wages, understanding and predicting future patterns of trade is a high-priority for decision making within and across countries. While traditional economic models aim to be reliable predictors, we consider the possibility that Artificial Intelligence (AI) techniques allow for better predictions and associations to inform policy decisions. Moreover, we outline contextual AI methods to decipher trade ...
International Finance Discussion Papers , Paper 1296

Working Paper
Integrating Prediction and Attribution to Classify News

Recent modeling developments have created tradeoffs between attribution-based models, models that rely on causal relationships, and “pure prediction models†such as neural networks. While forecasters have historically favored one technology or the other based on comfort or loyalty to a particular paradigm, in domains with many observations and predictors such as textual analysis, the tradeoffs between attribution and prediction have become too large to ignore. We document these tradeoffs in the context of relabeling 27 million Thomson Reuters news articles published between 1996 ...
Finance and Economics Discussion Series , Paper 2022-042

Dynamic effects of credit shocks in a data-rich environment

We examine the dynamic effects of credit shocks using a large data set of U.S. economic and financial indicators in a structural factor model. An identified credit shock resulting in an unanticipated increase in credit spreads causes a large and persistent downturn in indicators of real economic activity, labor market conditions, expectations of future economic conditions, a gradual decline in aggregate price indices, and a decrease in short- and longer-term riskless interest rates. Our identification procedure, which imposes restrictions on the response of a small number of economic ...
Staff Reports , Paper 615

Economic predictions with big data: the illusion of sparsity

We compare sparse and dense representations of predictive models in macroeconomics, microeconomics, and finance. To deal with a large number of possible predictors, we specify a prior that allows for both variable selection and shrinkage. The posterior distribution does not typically concentrate on a single sparse or dense model, but on a wide set of models. A clearer pattern of sparsity can only emerge when models of very low dimension are strongly favored a priori.
Staff Reports , Paper 847

Working Paper
Measuring Uncertainty and Its Effects in the COVID-19 Era

We measure the effects of the COVID-19 outbreak on uncertainty, and we assess the consequences of the uncertainty for key economic variables. We use a large, heteroskedastic vector autoregression (VAR) in which the error volatilities share two common factors, interpreted as macro and financial uncertainty. Macro and financial uncertainty are allowed to contemporaneously affect the macroeconomy and financial conditions, with changes in the common component of the volatilities providing contemporaneous identifying information on uncertainty. The model includes additional latent volatility ...
Working Papers , Paper 20-32R

Working Paper
Big Data versus a Survey

Economists are shifting attention and resources from work on survey data to work on ?big data.? This analysis is an empirical exploration of the trade-offs this transition requires. Parallel models are estimated using the Federal Reserve Bank of New York Consumer Credit Panel/Equifax and the Survey of Consumer Finances. After adjustments to account for different variable definitions and sampled populations, it is possible to arrive at similar models of total household debt. However, the estimates are sensitive to the adjustments. Little similarity is observed in parallel models of nonmortgage ...
Working Papers (Old Series) , Paper 1440

Working Paper
Forecasting Consumption Spending Using Credit Bureau Data

This paper considers whether the inclusion of information contained in consumer credit reports might improve the predictive accuracy of forecasting models for consumption spending. To investigate the usefulness of aggregate consumer credit information in forecasting consumption spending, this paper sets up a baseline forecasting model. Based on this model, a simulated real-time, out-of-sample exercise is conducted to forecast one-quarter ahead consumption spending. The exercise is run again after the addition of credit bureau variables to the model. Finally, a comparison is made to test ...
Working Papers , Paper 20-22

Working Paper
The perils of working with Big Data and a SMALL framework you can use to avoid them

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


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