Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than the choice of how frequently to repeat it. It allows for parallel estimation, making it computationally scalable. A first application revisits the partisanship of US congressional speech. We find that earlier spikes in partisanship manifested in increased repetition of different phrases, whereas the upward trend starting in the 1990s is due to entirely distinct phrase selection. Additional applications show how our model can backcast, nowcast, and forecast macroeconomic indicators using newspaper text, and that it substantially improves out-of-sample fit relative to alternative approaches.
We are grateful for helpful comments by Xavier Gabaix, Matthew Gentzkow, Hongyi Liu, Andy Neuhierl (discussant), Jesse Shapiro, Matt Taddy, Paul Tetlock (discussant) and by seminar participants at École Polytechnique Fédérale de Lausanne, Hebrew University, IDC Herzliya, Indiana University, INSEAD, Kansas City Fed, Ohio State, Syracuse University, Tel-aviv University, Rice University, University of Michigan, NBER SI AP, WFA, and the University of Chicago CITE Conference. Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially provided through NIH grant S10 OD018091. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Bryan T. Kelly
I have received research funding from the University of Chicago Fama-Miller Center exceeding $10,000 over the past three years.
I have received consulting income from AQR Capital Management exceeding $10,000 over the past three years. AQR Capital Management is a global investment management firm, which may or may not apply similar investment techniques or methods of analysis as described herein. The views expressed here are those of the authors and not necessarily those of AQR.