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Working Paper
Identification Through Sparsity in Factor Models
Freyaldenhoven, Simon (2020-06-22)
Factor models are generally subject to a rotational indeterminacy, meaning that individual factors are only identified up to a rotation. In the presence of local factors, which only affect a subset of the outcomes, we show that the implied sparsity of the loading matrix can be used to solve this rotational indeterminacy. We further prove that a rotation criterion based on the 1-norm of the loading matrix can be used to achieve identification even under approximate sparsity in the loading matrix. This enables us to consistently estimate individual factors, and to interpret them as structural ...
Working Papers , Paper 20-25

Working Paper
Assessing Macroeconomic Tail Risks in a Data-Rich Environment
Cook, Thomas R.; Doh, Taeyoung (2019-11-20)
We use a large set of economic and financial indicators to assess tail risks of the three macroeconomic variables: real GDP, unemployment, and inflation. When applied to U.S. data, we find evidence that a dense model using principal components (PC) as predictors might be misspecified by imposing the “common slope” assumption on the set of predictors across multiple quantiles. The common slope assumption ignores the heterogeneous informativeness of individual predictors on different quantiles. However, the parsimony of the PC-based approach improves the accuracy of out-of-sample forecasts ...
Research Working Paper , Paper RWP 19-12

Working Paper
Forecasting with Sufficient Dimension Reductions
Barbarino, Alessandro; Bura, Efstathia (2015-09-14)
Factor models have been successfully employed in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. When the objective is to forecast a target variable y with a large set of predictors x, the construction of the summary of the xs should be driven by how informative on y it is. Most existing methods first reduce the predictors and then forecast y in independent phases of the modeling process. In this paper we present an alternative and potentially more attractive alternative: summarizing x as it relates to y, so ...
Finance and Economics Discussion Series , Paper 2015-74

Journal Article
Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression
Groen, Jan J. J.; Nattinger, Michael (2020-10-01)
Official Chinese GDP growth rates have been remarkably smooth over the past decade, in contrast with alternative Chinese economic data. To better identify Chinese business cycles, we construct a sparse partial least squares (PLS) factor from a wide array of Chinese higher-frequency data, targeted toward variables that are highly correlated with important aspects of the Chinese economy. Our resulting alternative growth indicator clearly identifies Chinese business cycle fluctuations and it performs well both in out-of-sample testing for China as well as when applied to other economies. Using ...
Economic Policy Review , Volume 26 , Issue 4 , Pages 39-68

Working Paper
Improving the Accuracy of Economic Measurement with Multiple Data Sources: The Case of Payroll Employment Data
Cajner, Tomaz; Crane, Leland D.; Decker, Ryan A.; Hamins-Puertolas, Adrian; Kurz, Christopher J. (2019-09-05)
This paper combines information from two sources of U.S. private payroll employment to increase the accuracy of real-time measurement of the labor market. The sources are the Current Employment Statistics (CES) from BLS and microdata from the payroll processing firm ADP. We briefly describe the ADP-derived data series, compare it to the BLS data, and describe an exercise that benchmarks the data series to an employment census. The CES and the ADP employment data are each derived from roughly equal-sized samples. We argue that combining CES and ADP data series reduces the measurement error ...
Finance and Economics Discussion Series , Paper 2019-065

Working Paper
Big data analytics: a new perspective
Chudik, Alexander; Kapetanios, George; Pesaran, M. Hashem (2016-02-29)
Model specification and selection are recurring themes in econometric analysis. Both topics become considerably more complicated in the case of large-dimensional data sets where the set of specification possibilities can become quite large. In the context of linear regression models, penalised regression has become the de facto benchmark technique used to trade off parsimony and fit when the number of possible covariates is large, often much larger than the number of available observations. However, issues such as the choice of a penalty function and tuning parameters associated with the use ...
Globalization Institute Working Papers , Paper 268

Working Paper
Big Data versus a Survey
Whitaker, Stephan D. (2015-01-07)
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
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
Financial variables and macroeconomic forecast errors
Barnes, Michelle L.; Olivei, Giovanni P. (2017-10-31)
A large set of financial variables has only limited power to predict a latent factor common to the year-ahead forecast errors for real Gross Domestic Product (GDP) growth, the unemployment rate, and Consumer Price Index (CPI) inflation for three sets of professional forecasters: the Federal Reserve?s Greenbook, the Survey of Professional Forecasters (SPF), and the Blue Chip Consensus Forecasts. Even when a financial variable appears to be fairly robust across sample periods in explaining the latent factor, from an economic standpoint its contribution appears modest. Still, several financial ...
Working Papers , Paper 17-17

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

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