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Working Paper
Using Payroll Processor Microdata to Measure Aggregate Labor Market Activity
We show that high-frequency private payroll microdata can help forecast labor market conditions. Payroll employment is perhaps the most reliable real-time indicator of the business cycle and is therefore closely followed by policymakers, academia, and financial markets. Government statistical agencies have long served as the primary suppliers of information on the labor market and will continue to do so for the foreseeable future. That said, sources of ?big data? are becoming increasingly available through collaborations with private businesses engaged in commercial activities that record ...
Working Paper
Forecasting with Sufficient Dimension Reductions
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 ...
Working Paper
Common Factors, Trends, and Cycles in Large Datasets
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 ...
Working Paper
The U.S. Syndicated Loan Market : Matching Data
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 ...
Working Paper
Common and Idiosyncratic Inflation
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 ...
Working Paper
Tracking Labor Market Developments during the COVID-19 Pandemic: A Preliminary Assessment
Many traditional official statistics are not suitable for measuring high-frequency developments that evolve over the course of weeks, not months. In this paper, we track the labor market effects of the COVID-19 pandemic with weekly payroll employment series based on microdata from ADP. These data are available essentially in real-time, and allow us to track both aggregate and industry effects. Cumulative losses in paid employment through April 4 are currently estimated at 18 million; just during the two weeks between March 14 and March 28 the U.S. economy lost about 13 million paid jobs. ...
Working Paper
A Unified Framework for Dimension Reduction in Forecasting
Factor models are widely used in summarizing large datasets with few underlying latent factors and in building time series forecasting models for economic variables. In these models, the reduction of the predictors and the modeling and forecasting of the response y are carried out in two separate and independent phases. We introduce a potentially more attractive alternative, Sufficient Dimension Reduction (SDR), that summarizes x as it relates to y, so that all the information in the conditional distribution of y|x is preserved. We study the relationship between SDR and popular estimation ...
Working Paper
Technological Innovation in Mortgage Underwriting and the Growth in Credit: 1985-2015
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 assessment 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 Paper
Assessing International Commonality in Macroeconomic Uncertainty and Its Effects
This paper uses a large vector autoregression (VAR) to measure international macroeconomic uncertainty and its effects on major economies, using two datasets, one with GDP growth rates for 19 industrialized countries and the other with a larger set of macroeconomic indicators for the U.S., euro area, and U.K. Using basic factor model diagnostics, we first provide evidence of significant commonality in international macroeconomic volatility, with one common factor accounting for strong comovement across economies and variables. We then turn to measuring uncertainty and its effects with a large ...
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 ...