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

By Michelle T. Armesto, Rubén Hernández-Murillo, Michael T. Owyang, Jeremy Piger. 2009

72 citations recorded by the source corpus.

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. Copyright (c) 2009 The Ohio State University.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/8cbfe807f9a92c4526911a87421ba7877e08f2d8

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1. Yue‐Jun Zhang
2. Jin-li Wang

Citations in the corpus

• Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, background): “For example, the use of monthly data to forecast quarterly macroeconomic variables (Armesto et al., 2009; Clements and Galvao, 2009; Monteforte and Moretti, 2013), or the use of daily stock market data to forecast quarterly andmonthly macroeconomic variables (Andreou et al.”