- Consulting Editors
- [UNTITLED]
- Contributors
- [UNTITLED]
- Introduction
- 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
- Nowcasting
- 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
- Index
Abstract and Keywords
This article presents a statistical framework for estimating the current state of the economy (together with the recent past and near future) in a way in which the latest releases of high-frequency economic data can be incorporated, and in a way in which the impact of the latest release on the forecast can be readily assessed (providing a narrative to the changes in the estimate/forecast over time as more information accrues). It is organized as follows. Section 2 defines the problem of nowcasting in general and relates it to the concept of news in macroeconomic data releases. Section 3 explains the details of the approach. Section 4 discusses related literature. Section 5 illustrates the characteristics of the model via an application to the nowcast of GDP and inflation in the euro area. Section 6 discusses issues for further research, while Section 7 concludes.
Keywords: economic forecasting, macroeconomic data, GDP, inflation, euro area, current economic state
Marta Bańbura is economist at the European Central Bank. She holds a PhD in economics from Université Libre de Bruxelles. Her area of research is time series econometrics with a focus on macro forecasting and structural analysis with real-time and large information sets. She has contributions in such fields as factor models, Bayesian shrinkage, and wavelet methods. She has been also involved in developing forecasting models to aid policymaking. She has written several working papers and has been published in the Journal of Applied Econometrics and the International Journal of Forecasting.
Domenico Giannone is a professor of economics at the Université Libre de Bruxelles. He is a research affiliate of the CEPR in the International Macroeconomics Programme and holds his PhD in economics from the Université Libre de Bruxelles, 2004. He has worked as an Economist at the Monetary Policy Research Division of the European Central Bank and has been scientific coordinator of the Euro Area Business Cycle Network. His research has been centered around the theme of exploiting information contained in many macroeconomic and financial variables. His research has been published in international journals, including the Journal of Monetary Economics, Journal of Econometrics, Econometric Theory, and Proceedings of the National Academy of Science.
Lucrezia Reichlin is a professor of economics at the London Business School. Between March 2005 and September 2008 she served as director general of research at the European Central Bank and before that she held academic posts in different universities around the world. She has published numerous pepers on econometrics and macroeconomics. She is an expert on forecasting, business cycle analysis, and monetary policy. One of her main areas of research has been the econometrics of large-dimensional data, where she produced some of the earliest contributions on the theory of factor models with many time series and, more recently, studied Bayesian shrinkage in that environment. Some of the methods she has developed, in particular those for nowcasting, are widely used in central banks around the world. Her papers have appeared in top scientific journals, including American Economic Review, Review of Economic Studies, Review of Economics and Statistics, Journal of Econometrics, Journal of Monetary Econometrics, and Journal of the American Statistical Association.
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- Consulting Editors
- [UNTITLED]
- Contributors
- [UNTITLED]
- Introduction
- 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
- Nowcasting
- 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
- Index
