Short-term forecasting for empirical economists. A survey of the recently proposed algorithms
AbstractPractitioners do not always use research findings, as the research is not always conducted in a manner relevant to real-world practice. This survey seeks to close the gap between research and practice in respect of short-term forecasting in real time. To this end, we review the most relevant recent contributions to the literature, examining their pros and cons, and we take the liberty of proposing some avenues of future research. We include bridge equations, MIDAS, VARs, factor models and Markov-switching factor models, all allowing for mixed-frequency and ragged ends. Using the four constituent monthly series of the Stock-Watson coincident index, industrial production, employment, income and sales, we evaluate their empirical performance to forecast quarterly US GDP growth rates in real time. Finally, we review the main results having regard to the number of predictors in factorbased forecasts and how the selection of the more informative or representative variables can be made.
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Bibliographic InfoPaper provided by Banco de Espa�a in its series Banco de Espa�a Working Papers with number 1318.
Length: 63 pages
Date of creation: Nov 2013
Date of revision:
Forecasting; GDP growth; time series;
Other versions of this item:
- Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2013. "Short-term Forecasting for Empirical Economists: A Survey of the Recently Proposed Algorithms," Foundations and Trends(R) in Econometrics, now publishers, vol. 6(2), pages 101-161, November.
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-16 (All new papers)
- NEP-ETS-2013-11-16 (Econometric Time Series)
- NEP-FOR-2013-11-16 (Forecasting)
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