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Working Paper
Interconnectedness in the Corporate Bond Market
Brunetti, Celso; Carl, Matthew; Gerszten, Jacob; Scotti, Chiara; Shin, Chaehee (2024-08-16)
Does interconnectedness improve market quality? Yes.We develop an alternative network structure, the assets network: assets are connected if they are held by the same investors. We use several large datasets to build the assets network for the corporate bond market. Through careful identification strategies based on the COVID-19 shock and “fallen angels,” we find that interconnectedness improves market quality especially during stress periods. Our findings contribute to the debate on the role of interconnectedness in financial markets and show that highly interconnected corporate bonds ...
Finance and Economics Discussion Series , Paper 2024-066

Discussion Paper
Exploring the use of anonymized consumer credit information to estimate economic conditions: an application of big data
Wilshusen, Stephanie M. (2015-11-06)
The emergence of high-frequency administrative data and other big data offers an opportunity for improvements to economic forecasting models. This paper considers the potential advantages and limitations of using information contained in anonymized consumer credit reports for improving estimates of current and future economic conditions for various geographic areas and demographic markets. Aggregate consumer credit information is found to be correlated with macroeconomic variables such as gross domestic product, retail sales, and employment and can serve as leading indicators such that lagged ...
Consumer Finance Institute discussion papers , Paper 15-5

Working Paper
Variable Selection in High Dimensional Linear Regressions with Parameter Instability
Chudik, Alexander; Sharifvaghefi, Mahrad; Pesaran, M. Hashem (2024-08-05)
This paper considers the problem of variable selection allowing for parameter instability. It distinguishes between signal and pseudo-signal variables that are correlated with the target variable, and noise variables that are not, and investigates the asymptotic properties of the One Covariate at a Time Multiple Testing (OCMT) method proposed by Chudik et al. (2018) under parameter insatiability. It is established that OCMT continues to asymptotically select an approximating model that includes all the signals and none of the noise variables. Properties of post selection regressions are also ...
Globalization Institute Working Papers , Paper 394

Working Paper
The Transmission of Financial Shocks and Leverage of Financial Institutions: An Endogenous Regime-Switching Framework
Waggoner, Daniel F.; Hubrich, Kirstin (2022-06-02)
We conduct a novel empirical analysis of the role of leverage of financial institutions for the transmission of financial shocks to the macroeconomy. For that purpose, we develop an endogenous regime-switching structural vector autoregressive model with time-varying transition probabilities that depend on the state of the economy. We propose new identification techniques for regime switching models.Recently developed theoretical models emphasize the role of bank balance sheets for the build-up of financial instabilities and the amplification of financial shocks. We build a market-based ...
FRB Atlanta Working Paper , Paper 2022-5

Working Paper
A one-covariate at a time, multiple testing approach to variable selection in high-dimensional linear regression models
Chudik, Alexander; Kapetanios, George; Pesaran, M. Hashem (2016-11-01)
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 290

Working Paper
Investing in the Batteries and Vehicles of the Future: A View Through the Stock Market
Plante, Michael D. (2023-09-26)
A large number of companies operating in the EV and battery supply chain have listed on a major U.S. stock exchange in recent years. This paper investigates 1) how these companies’ stock returns are related to systematic risk factors that can explain movements in the stock market and 2) how these companies’ idiosyncratic returns are related to one another. To do so, I compile a unique data set of intradaily stock returns that spans the supply chain, including companies focused on the mining of battery and EV-related critical minerals, advanced battery technology, lithium-ion battery ...
Working Papers , Paper 2314

Working Paper
Measuring Uncertainty and Its Effects in the COVID-19 Era
Clark, Todd E.; Marcellino, Massimiliano; Mertens, Elmar; Carriero, Andrea (2022-01-05)
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
Variable Selection and Forecasting in High Dimensional Linear Regressions with Structural Breaks
Chudik, Alexander; Pesaran, M. Hashem; Sharifvaghefi, Mahrad (2020-08-19)
This paper is concerned with the problem of variable selection and forecasting in the presence of parameter instability. There are a number of approaches proposed for forecasting in the presence of breaks, including the use of rolling windows or exponential down-weighting. However, these studies start with a given model specification and do not consider the problem of variable selection. It is clear that, in the absence of breaks, researchers should weigh the observations equally at both the variable selection and forecasting stages. In this study, we investigate whether or not we should use ...
Globalization Institute Working Papers , Paper 394

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
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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