Date of Award
Doctor of Philosophy
Dr. Chiayang James Hueng
Dr. Mark Wheeler
Dr. Kevin Corder
Data revision, rationality, forecasting, real-time analysis, monetary, uncertainty
Initial estimates of macroeconomic variables based on incomplete source data can be unreliable. Because of the methodology used by reporting agencies and the presence of reporting errors in the survey data, I argue that initial-released output estimates tend to be irrational and unreliable under uncertainty. Using U.S. nominal and real output real time data from 1985 to 2014 and the Economic Policy Uncertainty (EPU) index proposed by Baker et al. (2013), I investigate the impact of economic policy uncertainty on aggregate output data revisions, modeling the output revisions, and the effect of output data revision on inflation forecasts.
In Chapter 2, I find a strong evidence of asymmetric impact of the uncertainty on rationality of the initial-released output data. Also, the results show that the magnitudes of output data revisions tend to be larger when the uncertainty is greater. The out-ofsample predictions indicate that the ability of the EPU index on forecasting the revisions is superior to that of business-cycle indicators suggested by previous study.
Chapter 3 analyzes the nature of the output data revisions by applying a common factor model and a large set of information variables (approximately 200 macroeconomic variables) suggested by Giannone, Reichlin, and Small (2008). The results show that the common factors track the revisions quite well. In particular, these factors are able to capture the huge downward revisions of aggregate output during the subprime mortgage crisis in late 2008. Using the common factors as a robustness check for examining the rationality of the initial-released output data, I find that the results in some of previous studies are likely to have omitted-variable bias.
Chapter 4 applies the findings in Chapter 2 to literature examining the impact of output data revision on inflation forecasts. The results show that the difference between the forecasting performance of the use of fully revised output gap estimates and that of the real-time output estimates tends to be greater during periods of high uncertainty. This finding implies that previous empirical results on examining the inflation-output gap relationship using unrevised data released during the periods of high uncertainty are likely to be special cases that are not representative of all vintages of the data.
Sung, Pei-Ju, "The Impact of Uncertainty on Data Revision" (2015). Dissertations. 1198.