Short-Term Forecasting for Empirical Economists. A Survey of the Recently Proposed Algorithms
63 Pages Posted: 13 Nov 2013
Date Written: November 13, 2013
Practitioners 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 factor-based forecasts and how the selection of the more informative or representative variables can be made.
Keywords: Forecasting, GDP growth, time series
JEL Classification: E32, C22, E27
Suggested Citation: Suggested Citation