2011
DOI: 10.2139/ssrn.1881867
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Real-Time Data and Fiscal Policy Analysis: A Survey of the Literature

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Cited by 27 publications
(33 citation statements)
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References 40 publications
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“…Incidentally, the reparametrization allows a relatively easy interpretation of the parameters we need to estimate. 9 Indeed, E(h t ) = !, so that ! is the log-modal volatility, and var(h t ) = e 2 , so that sd(h t ) = e .…”
Section: Introductionmentioning
confidence: 99%
“…Incidentally, the reparametrization allows a relatively easy interpretation of the parameters we need to estimate. 9 Indeed, E(h t ) = !, so that ! is the log-modal volatility, and var(h t ) = e 2 , so that sd(h t ) = e .…”
Section: Introductionmentioning
confidence: 99%
“…For our estimation, like most authors, we rely on final rather than real-time data. Given the sometimes significant revisions to real-time data, this raises conceptual challenges for the interpretation of policy shocks; seeCimadomo (2011).…”
mentioning
confidence: 99%
“…Paloviita (2012) explores the uncertainty in euro area fiscal policies due to real time uncertainty and finds that the real time uncertainty has an important role in unexpected fiscal outcomes. Some of the uncertainties and possible biases might, however, be associated with the institutional and political features of the individual country (Cimadomo, 2011). Frankel and Schreger (2013) find that the forecasts of euro area governments are biased upward and that the forecasts are particularly optimistic when the governments have difficulties in meeting their MTOs.…”
Section: Estimating the Structural Balance Ex Post And Ex Antementioning
confidence: 99%