Course Overview


A Detailed Schedule is available here  (updated on July 15, 2020)

Times indicated refer to Eastern Daylight Time Zone*


This Summer School is intended for students, researchers and professionals in statistics, econometrics and finance. It assumes familiarity with basic regression analysis, principles of univariate and multivariate time series analysis as well as basic models of volatility but is otherwise self-contained.

  • Lecture 1: Introduction to MIDAS Regressions
  • Lecture 2: Kalman Filter, Mixed Frequency Data and Nowcasting
  • Lecture 3: Vector Autoregressive Models with Mixed Frequency Data
  • Lecture 4: MIDAS Volatility Models, Correlation Models and Quantile regressions
  • Lecture 5: Practical applications
  • Lecture 6: Forecasting and empirical risk minimization
  • Lecture 7: Regularized regressions
  • Lecture 8: Machine learning with mixed frequency data
  • Lecture 9: Factor models with mixed frequency data


* Use Worldclock to conveniently convert these EST times (New York City) to your time zone.