Forecasting with Mixed-Frequency Data

Elena Andreou · Éric Ghysels · Andros Kourtellos

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Forecasting Techniques and Applications Monetary Policy and Economic Impact Statistical and numerical algorithms

Abstract

Abstract This article, which presents a regression framework that relates the quarterly macro variable (such as GDP growth) to higher-frequency variables in a relatively simple, parsimonious way, is organized as follows. Section 2 covers mixed data sampling (MIDAS) regressions. Section 3 covers so-called nowcasting, and the Kalman filter and its relationship with MIDAS regressions. The final section discusses volatility models using mixed frequencies.

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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 2002Determining the Number of Factors in Approximate Factor Models 1990Forecasting, Structural Time Series Models and the Kalman Filter 1993A long memory property of stock market returns and a new model 2002Forecasting Using Principal Components From a Large Number of Predictors 2008A Simple Approximate Long-Memory Model of Realized Volatility

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2016Forecasting in Economics and Finance 2018Estimating MIDAS regressions via OLS with polynomial parameter profiling 2014Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences 2015Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach 2017Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?

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