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Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach

Authors


  • The authors thank Oscar Jorda, Kevin Kliesen, Ivana Komunjer, Barbara Rossi, and Enrique Sentana for comments and suggestions. We thank Nathan Balke and D'Ann Petersen for the use of their Beige Book measures. Heidi Beyer-Powe, Kristie M. Engemann, George Essig, Christopher Martinek, and Deborah Roisman provided research assistance. The views expressed in this paper are the authors' alone and do not reflect the views of the Federal Reserve Bank of St. Louis or the Federal Reserve System.

Abstract

Studies of the predictive ability of the Federal Reserve's Beige Book for aggregate output and employment have proven inconclusive. This might be attributed, in part, to its irregular release schedule. We use a model that allows for data sampling at mixed frequencies to analyze the predictive power of the Beige Book. We find that the Beige Book's national summary and District reports predict GDP and aggregate employment and that most District reports provide information content for regional employment. In addition, there appears to be an asymmetry in the predictive content of the Beige Book language.

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