Dr. Steven Cassou | Working Papers
- "Government Capital and Production: Industry Level Estimates." with Vladimir Bejan.
- "Optimal monetary policy with real-time policy targets." with C. Patrick Scott and Jesús Vázquez.
Abstract: This paper estimates sectoral level production functions using U.S. data for nine industry groups. It is shown that the public capital elasticity differs across industries. This implies that not all industries are equally impacted by public capital spending and that using a single generic production function with constant returns to scale to represent all industries is not appropriate.
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Abstract: Croushore (2011) and others have noted that monetary policy may be sensitive to inconsistencies between real-time data used by policy makers to make decisions and revised data which more accurately measure economic performance. This paper extends the asymmetric preference model suggested by Ruge-Murcia (2003) in order to focus on these inconsistencies which arise because of the long lag between the real-time data release and the revised data release. We focus on two isomorphic monetary policy models: One in which the central banker targets real-time inflation and output and the other in which the central banker targets real-time inflation and unemployment. Our model identifies several new potential sources of inflation bias due to data revisions in addition to the ones suggested in the literature. The paper also contributes to the real-time data processing literature by describing a method for computing an output revision series, which is the difference between (the logs of) the revised output series and the real-time output series. This computation is important because an output revision series is not available from standard data sources, yet such a series is useful for a variety of empirical exercises. Our empirical results obtained from US data suggest that the inflation bias induced by the predictability of data revisions is rather small whereas the one induced by asymmetric central bank preferences remains significant when considering real-time data.
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