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Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad (2023-01-18)
This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses the issue of whether weighted or unweighted observations should be used at the variable selection stage in the presence of parameter instability, particularly when the number of potential covariates is large. Amongst the extant variable selection approaches, we focus on the One Covariate at a time ...
Globalization Institute Working Papers , Paper 394

Working Paper
Financial Conditions and Economic Activity: Insights from Machine Learning
Kiley, Michael T. (2020-11-16)
Machine learning (ML) techniques are used to construct a financial conditions index (FCI). The components of the ML-FCI are selected based on their ability to predict the unemployment rate one-year ahead. Three lessons for macroeconomics and variable selection/dimension reduction with large datasets emerge. First, variable transformations can drive results, emphasizing the need for transparency in selection of transformations and robustness to a range of reasonable choices. Second, there is strong evidence of nonlinearity in the relationship between financial variables and economic ...
Finance and Economics Discussion Series , Paper 2020-095

Working Paper
Common Factors, Trends, and Cycles in Large Datasets
Barigozzi, Matteo; Luciani, Matteo (2017-11-13)
This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic properties of the estimator are discussed. Then, we show how to separate trends and cycles in the factors by mean of eigenanalysis of the estimated non-stationary factors. Finally, we employ our methodology on a panel of US quarterly macroeconomic indicators to estimate aggregate real output, or Gross ...
Finance and Economics Discussion Series , Paper 2017-111

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
Technological innovation in mortgage underwriting and the growth in credit, 1985–2015
Foote, Christopher L.; Loewenstein, Lara; Willen, Paul S. (2019-11-01)
The application of information technology to finance, or ?fintech,? is expected to revolutionize many aspects of borrowing and lending in the future, but technology has been reshaping consumer and mortgage lending for many years. During the 1990s, computerization allowed mortgage lenders to reduce loan-processing times and largely replace human-based assessments of credit risk with default predictions generated by sophisticated empirical models. Debt-to-income ratios at origination add little to the predictive power of these models, so the new automated underwriting systems allowed higher ...
Working Papers , Paper 19-11

Working Paper
The U.S. Syndicated Loan Market : Matching Data
Sicilian, Martin; Shaton, Maya; Hayes, William; Friedrichs, Melanie; Gupta, Kamran; Mislang, Nathan; Lee, Seung Jung; Cohen, Gregory J.; Marsh, W. Blake (2018-12-07)
We introduce a new software package for determining linkages between datasets without common identifiers. We apply these methods to three datasets commonly used in academic research on syndicated lending: Refinitiv LPC DealScan, the Shared National Credit Database, and S&P Global Market Intelligence Compustat. We benchmark the results of our match using results from the literature and previously matched files that are publicly available. We find that the company level matching is enhanced by careful cleaning of the data and considering hierarchical relationships. For loan level matching, a ...
Finance and Economics Discussion Series , Paper 2018-085

Working Paper
Common and Idiosyncratic Inflation
Luciani, Matteo (2020-03-05)
We use a dynamic factor model to disentangle changes in prices due to economy-wide (common) shocks, from changes in prices due to idiosyncratic shocks. Using 146 disaggregated individual price series from the U.S. PCE price index, we find that most of the fluctuations in core PCE prices observed since 2010 have been idiosyncratic in nature. Moreover, we find that common core inflation responds to economic slack, while the idiosyncratic component does not. That said, even after filtering out idiosyncratic factors, the estimated Phillips curve is extremely flat post-1995. Therefore, our ...
Finance and Economics Discussion Series , Paper 2020-024

Working Paper
Artificial Intelligence Methods for Evaluating Global Trade Flows
Monken, Anderson; Gopinath, Munisamy; Batarseh, Feras A. (2020-08-20)
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
Identifying Financial Crises Using Machine Learning on Textual Data
Chen, Mary; DeHaven, Matthew; Kitschelt, Isabel; Lee, Seung Jung; Sicilian, Martin (2023-03-31)
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

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