Forecasting with Mixed-Frequency Data

2012/09/18 by Elena Andreou, Éric Ghysels, Andros Kourtellos · 1 citation
Economics, Econometrics and Finance · #Financial Risk and Volatility Modeling #Monetary Policy and Economic Impact #Complex Systems and Time Series Analysis

paper · doi:10.1093/oxfordhb/9780195398649.013.0009

openalex publication_date 2012/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/11

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.

Citations

• Nowcasting: The real-time informational content of macroeconomic data
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• The Generalized Dynamic-Factor Model: Identification and Estimation
• Generalized autoregressive conditional heteroskedasticity
• A long memory property of stock market returns and a new model
• Systematic Monetary Policy and the Effects of Oil Price Shocks

Cited by

• Nowcasting (economics) [wikipedia]