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Working Paper
Betting the House
Taylor, Alan M.; Schularick, Moritz; Jordà, Òscar (2014-12)
Is there a link between loose monetary conditions, credit growth, house price booms, and financial instability? This paper analyzes the role of interest rates and credit in driving house price booms and busts with data spanning 140 years of modern economic history in the advanced economies. We exploit the implications of the macroeconomic policy trilemma to identify exogenous variation in monetary conditions: countries with fixed exchange regimes often see fluctuations in short-term interest rates unrelated to home economic conditions. We use novel instrumental variable local projection ...
Working Paper Series , Paper 2014-28

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
International Stock Comovements with Endogenous Clusters
Owyang, Michael T.; Coroneo, Laura; Jackson, Laura E. (2020-03-27)
We examine international stock return comovements of country-industry portfolios. Our model allows comovements to be driven by a global and a cluster component, with the cluster membership endogenously determined. Results indicate that country-industry portfolios tend to cluster mainly within geographical areas that can include one or more countries. The cluster compositions substantially changed over time, with the emergence of clusters among European countries from the early 2000s. The cluster component was the main driver of country-industry portfolio returns for most of the sample, except ...
Working Papers , Paper 2018-038

Working Paper
Dynamic Factor Copula Models with Estimated Cluster Assignments
Oh, Dong Hwan; Patton, Andrew J. (2022-05-06)
This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model ...
Finance and Economics Discussion Series , Paper 2021-029r1

Working Paper
Tax Progressivity, Economic Booms, and Trickle-Up Economics
Owyang, Michael T.; Otrok, Christopher; Jackson, Laura E.; Traum, Nora (2026-03-12)
We study the macroeconomic and distributional effects of changes in income tax progressivity in the United States. We propose a method to decompose changes in the tax structure into orthogonal components measuring the level and progressivity of taxes. Estimating these components jointly with macroeconomic outcomes in a factor-augmented VAR, we find that tax level shocks are contractionary, while tax progressivity shocks are expansionary, raising output and consumption by shifting disposable income from high to low-income households. Despite being revenue neutral by construction, progressivity ...
Working Papers , Paper 2019-034

Working Paper
One Threshold Doesn’t Fit All: Tailoring Machine Learning Predictions of Consumer Default for Lower-Income Areas
Meursault, Vitaly; Moulton, Daniel; Santucci, Larry; Schor, Nathan (2022-11-21)
Modeling advances create credit scores that predict default better overall, but raise concerns about their effect on protected groups. Focusing on low- and moderate-income (LMI) areas, we use an approach from the Fairness in Machine Learning literature — fairness constraints via group-specific prediction thresholds — and show that gaps in true positive rates (% of non-defaulters identified by the model as such) can be significantly reduced if separate thresholds can be chosen for non-LMI and LMI tracts. However, the reduction isn’t free as more defaulters are classified as good risks, ...
Working Papers , Paper 22-39

Working Paper
Tracking U.S. Consumers in Real Time with a New Weekly Index of Retail Trade
Aaronson, Daniel; Brave, Scott A.; Karger, Ezra; Fogarty, Michael; Krane, Spencer D. (2021-05-27)
We create a new weekly index of retail trade that accurately predicts the U.S. Census Bureau's Monthly Retail Trade Survey (MRTS). The index's weekly frequency provides an early snapshot of the MRTS and allows for a more granular analysis of the aggregate consumer response to fast-moving events such as the Covid-19 pandemic. To construct the index, we extract the co-movement in weekly data series capturing credit and debit card transactions, mobility, gasoline sales, and consumer sentiment. To ensure that the index is representative of aggregate retail spending, we implement a novel ...
Working Paper Series , Paper WP-2021-05

Working Paper
A Generalized Factor Model with Local Factors
Freyaldenhoven, Simon (2019-04-19)
I extend the theory on factor models by incorporating local factors into the model. Local factors only affect an unknown subset of the observed variables. This implies a continuum of eigenvalues of the covariance matrix, as is commonly observed in applications. I derive which factors are pervasive enough to be economically important and which factors are pervasive enough to be estimable using the common principal component estimator. I then introduce a new class of estimators to determine the number of those relevant factors. Unlike existing estimators, my estimators use not only the ...
Working Papers , Paper 19-23

Working Paper
Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models
Li, Kunpeng; Li, Qi; Lu, Lina (2018-04-24)
Factor models have been widely used in practice. However, an undesirable feature of a high dimensional factor model is that the model has too many parameters. An effective way to address this issue, proposed in a seminal work by Tsai and Tsay (2010), is to decompose the loadings matrix by a high-dimensional known matrix multiplying with a low-dimensional unknown matrix, which Tsai and Tsay (2010) name the constrained factor models. This paper investigates the estimation and inferential theory of constrained factor models under large-N and large-T setup, where N denotes the number of cross ...
Supervisory Research and Analysis Working Papers , Paper RPA 18-2

Working Paper
Simultaneous Spatial Panel Data Models with Common Shocks
Lu, Lina (2017-08-09)
I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a quasi-maximum likelihood (QML) method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further ...
Supervisory Research and Analysis Working Papers , Paper RPA 17-3

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