- Consulting Editors
- Vars, Cointegration, and Common Cycle Restrictions
- Dynamic Factor Models
- Forecasting With Nonlinear Time Series Models
- Forecasting With DSGE Models
- Forecasting Economic Time Series Using Unobserved Components Time Series Models
- Improving the Role of Judgment in Economic Forecasting
- Forecasting with Mixed-Frequency Data
- Forecasting with Real-Time Data Vintages
- Forecasting from misspecified Models in the Presence of Unanticipated Location Shifts
- Forecasting Breaks and Forecasting During Breaks
- Forecast Combinations
- Multiple Forecast Model Evaluation
- Testing for Unconditional Predictive Ability
- Testing Conditional Predictive Ability
- Interpreting and Combining Heterogeneous Survey Forecasts
- Analyzing Three-Dimensional Panel Data of Forecasts
- Forecasting Financial Time Series
- Forecasting Volatility Using High-Frequency Data
- Economic Value of Weather and Climate Forecasts
- Long-Horizon Growth Forecasting and Demography
- Forecasting the Energy Markets
- Models for Health Care
- Election Forecasting
- Marketing and Sales
Abstract and Keywords
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.
Elena Andreou is an associate professor in the Department of Economics at the University of Cyprus. Her two main research areas are financial econometrics and time series analysis. She has published several articles in the Journal of Econometrics and other top field journals. She received her PhD in economics in 1998 from the University of Manchester and has been faculty member in Tilburg University and the University of Manchester. Elena has received various grants and fellowships, including the European Union Marie Curie Fellowship and the European Research Council (ERC) grant.
Eric Ghysels is the Bernstein Distinguished Professor of Economics at the University of North Carolina at Chapel Hill and professor of finance at the Kenan–Flagler Business School. His main research interests are time series econometrics and finance. He has published more than 100 articles in academic journals, including many in the majoreconomics, finance, and statistics journals, and has published several books. He has served on the editorial boards of many academic journals and has been coeditor of the Journal of Business and Economic Statistics and is currently coeditor of the Journal of Financial Econometrics. He cofounded with Robert Engle (NYU Stern) the Society of Financial Econometrics (SoFiE). He was Resident Scholar at the Federal Reserve Bank of New York in 2008–2009. His most recent research has focused on financial econometrics, and in particular on MIDAS (mixed data sampling) regression models and related econometric methods.
Andros Kourtellos is an assistant professor of economics at the University of Cyprus. His research interests are macroeconometrics and empirical economic growth. His most recent research focuses on macroeconomic forecasting, mixed sampling frequencies, model averaging, and threshold regression. He has recently published articles in these areas in the Journal of Econometrics and Economic Journal. Andros did his undergraduate studies at the University of Cyprus and completed his PhD in economics at the University of Wisconsin–Madison in 2001. During the academic year 2001–2002 he visited the Virginia Tech.
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