©2019 by Leland Bybee.


I am a PhD student in financial economics at the Yale School of Management.  My research focuses on asset pricing, financial econometrics, text data, and high-dimensional Bayesian statistics.  Before Yale, I was a researcher at the Booth School of Business, received a master's degree in statistics from the University of Michigan, and completed my undergraduate degree in economics at the University of Chicago.



(With Bryan Kelly, Asaf Manela, and Dacheng Xiu)

We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984–2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the proportion of news attention allocated to each theme at each point in time. We then use our news attention estimates as inputs into statistical models of numerical economic time series. We demonstrate that these text-based inputs accurately track a wide range of economic activity measures and that they have incremental forecasting power for macroeconomic outcomes, above and beyond standard numerical predictors. Finally, we use our model to retrieve the news-based narratives that underly “shocks” in numerical economic data.

Data available here: http://structureofnews.com/

(With Yves Atchadé)

Graphical models with change-points are computationally challenging to fit, particularly in cases where the number of observation points and the number of nodes in the graph are large. Focusing on Gaussian graphical models, we introduce an approximate majorize-minimize (MM) algorithm that can be useful for computing change-points in large graphical models. The proposed algorithm is an order of magnitude faster than a brute force search. Under some regularity conditions on the data generating process, we show that with high probability, the algorithm converges to a value that is within statistical error of the true change-point. A fast implementation of the algorithm using Markov Chain Monte Carlo is also introduced. The performances of the proposed algorithms are evaluated on synthetic data sets and the algorithm is also used to analyze structural changes in the S&P 500 over the period 2000-2016.

An implementation of our method is available on CRAN: https://cran.r-project.org/web/packages/changepointsHD/index.html