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  • 标题:Measuring the Cycle and Structural Shocks
  • 本地全文:下载
  • 作者:Marta Lopresto ; Garry Young
  • 期刊名称:Economic and Social Review
  • 印刷版ISSN:0012-9984
  • 出版年度:2019
  • 卷号:50
  • 期号:1
  • 页码:103-117
  • 出版社:Economic and Social Research Institute
  • 摘要:The rules that guide fiscal policy in many countries are often specified in terms of cyclicallyadjusted measures of the budget deficit. Accounting for the cycle is important because public finances are flattered when the economy is cyclically strong. However measuring the cycle is difficult as the uncertainty surrounding estimates of potential output is significant, especially when the economy is hit by structural shocks. As a consequence, a fiscal framework targeting measures of cyclically-adjusted budget positions and point forecasts has led to spending and taxes being influenced by contentious forecasting assumptions. A more robust approach would take account of the unreliability of the cyclical adjustment or forecasts. A suggestion is to focus on risks around medium-term fiscal forecasts, supported by an independent watchdog. This would contribute to sustainable policy, while avoiding excess sensitivity to forecasts.
  • 其他摘要:The rules that guide fiscal policy in many countries are often specified in terms of cyclically-adjusted measures of the budget deficit. Accounting for the cycle is important because public finances are flattered when the economy is cyclically strong. However measuring the cycle is difficult as the uncertainty surrounding estimates of potential output is significant, especially when the economy is hit by structural shocks. As a consequence, a fiscal framework targeting measures of cyclically-adjusted budget positions and point forecasts has led to spending and taxes being influenced by contentious forecasting assumptions. A more robust approach would take account of the unreliability of the cyclical adjustment or forecasts. A suggestion is to focus on risks around medium-term fiscal forecasts, supported by an independent watchdog. This would contribute to sustainable policy, while avoiding excess sensitivity to forecasts.
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