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Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts
16 references·5 citing·44 second hop
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this work references cited by second hop

References

  • 1966The Scree Test For The Number Of Factors13,549 cited
  • 1995Comparing Predictive Accuracy5,214 cited
  • 2002Determining the Number of Factors in Approximate Factor Models4,110 cited
  • 2009Large Bayesian vector auto regressions1,158 cited
  • 2008Nowcasting: The real-time informational content of macroeconomic data1,153 cited
  • 2004Combination forecasts of output growth in a seven‐country data set1,138 cited
  • 2007MIDAS Regressions: Further Results and New Directions1,003 cited
  • 2003Monetary policy in a data-rich environment702 cited
  • 2006Determining the Number of Primitive Shocks in Factor Models540 cited
  • 2012Dynamic Factor Models468 cited
  • 2008Macroeconomic Forecasting With Mixed-Frequency Data392 cited
  • 2003Bridge models to forecast the euro area GDP391 cited
  • 2010Introducing the euro‐sting: Short‐term indicator of euro area growth353 cited
  • 2010MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area312 cited
  • 2010Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP*298 cited
  • 2010

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A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP
258 cited

Cited by

  • 2018News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions59 cited
  • 2020A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study55 cited
  • n.d.The use of big data analytics and artificial intelligence in central banking53 cited
  • 2021Factor extraction using Kalman filter and smoothing: This is not just another survey29 cited
  • 2021Back to the Present: Learning about the Euro Area through a Now-casting Model15 cited