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Forecasting with Mixed Frequencies
16 references·15 citing·47 second hopsampled
Forecasting with Mixed Frequencies (2010)Forecasting with Mixed FrequenciesShould Macroeconomic Forecasters Use Daily Financial Data and How? 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References

  • 2008A Simple Approximate Long-Memory Model of Realized Volatility2,526 cited
  • 2013Handbook of Economic Forecasting1,216 cited
  • 2008Nowcasting: The real-time informational content of macroeconomic data1,153 cited
  • 2005There is a risk-return trade-off after all1,019 cited
  • 1998Bayesian Methods for Dynamic Multivariate Models1,011 cited
  • 2007MIDAS Regressions: Further Results and New Directions1,003 cited
  • 2009Real-Time Measurement of Business Conditions916 cited
  • 2005Predicting volatility: getting the most out of return data sampled at different frequencies813 cited
  • 2001A real-time data set for macroeconomists787 cited
  • 2004The MIDAS Touch: Mixed Data Sampling Regression Models563 cited
  • 2008Macroeconomic Forecasting With Mixed-Frequency Data392 cited
  • 1981A Methodological Note on the Estimation of Time Series355 cited
  • 2010Introducing the euro‐sting: Short‐term indicator of euro area growth353 cited
  • 2013Should Macroeconomic Forecasters Use Daily Financial Data and How?331 cited
  • 2010MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area312 cited
  • 2003

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Predicting Volatility: Getting the Most Out of Return Data Sampled at Different Frequencies
173 cited

Cited by

  • 2018Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models116 cited
  • 2012Forecasting with Mixed-Frequency Data104 cited
  • 2018Estimating MIDAS regressions via OLS with polynomial parameter profiling67 cited
  • 2014Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences64 cited
  • 2013Changes in predictive ability with mixed frequency data61 cited
  • 2016Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data57 cited
  • 2016A mixed frequency approach to the forecasting of private consumption with ATM/POS data50 cited
  • 2016A comparison of MIDAS and bridge equations48 cited
  • 2011Conditional Skewness of Stock Market Returns in Developed and Emerging Markets and its Economic Fundamentals45 cited
  • 2024Do topic and sentiment matter? Predictive power of online reviews for hotel demand forecasting41 cited
  • 2015Testing for Cointegration with Temporally Aggregated and Mixed‐Frequency Time Series35 cited
  • 2017Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach32 cited
  • 2024FinTech, systemic risk and bank market power – Australian perspective20 cited
  • 2010Doubling Your Monetary Base and Surviving: Some International Experience16 cited
  • 2023Measuring Local Economic Activity Using Pedestrian Count Data*5 cited