Michael T. Owyang

A State-Level Analysis of Okun's Law

Okuns law is an empirical relationship that measures the correlation between the deviation of the unemployment rate from its natural rate and the deviation of output growth from its potential. This relationship is often referred to by policy makers and used by forecasters. In this paper, we estimate Okuns coefficients separately for each U.S. state using an unobserved components framework and find variation of the coefficients across states. We exploit this heterogeneity of Okuns coefficients to directly examine the potential factors that shape Okuns law, and find that indicators of more flexible labor markets (higher levels of education achievement in the population, lower rate of unionization, and a higher share of non-manufacturing employment) are important determinants of the differences in Okuns coefficient across states.

Asymmetry and Federal Reserve Forecasts

Forecasts are a central component of policy making; the Federal Reserve’s forecasts are published in a document called the Greenbook. Previous studies of the Greenbook’s inflation forecasts have found them to be rationalizable but asymmetric if considering particular subperiods, e.g., before and after the Volcker appointment. In these papers, forecasts are analyzed in isolation, assuming policymakers value them independently. We analyze the Greenbook forecasts in a framework in which the forecast errors are allowed to interact. We find that allowing the losses to interact makes the unemployment forecasts virtually symmetric, the output forecasts symmetric prior to the Volcker appointment, and the inflation forecasts symmetric after the onset of the Great Moderation.

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

Studies of the predictive ability of the Federal Reserve’s Beige Book, an anecdotal measure of regional economic conditions, for aggregate output and employment have proven inconclusive. This might be attributed, in part, to the irregular release schedule of the Beige Book. In this paper, we use a model that allows for data sampling at mixed frequencies to analyze the predictive power of the Beige Book for both aggregate and regional data. 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. [JEL: C50, E27, R11] 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. Research Division. Federal Reserve Bank of St. Louis. P.O. Box 442. St. Louis, MO 63166-0442. Corresponding author: owyang@stls.frb.org Department of Economics. 1285 University of Oregon. Eugene, OR 97403.