Now-Casting and the Real-time Data Flow

By Martha Banbura, Domenico Giannone, M. Modugno, L. Reichlin. Social Science Research Network, 2013

436 citations recorded by the source corpus.

The term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics In this paper we survey recent developments on economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/bd87b9e2901d34df29af05a86a331d20113292b9

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Citations in the corpus

Tracking the Slowdown in Long-Run GDP Growth (2017, methodology): “(2008) and Banbura et al. (2012) have pioneered the use of DFMs to produce current-quarter forecasts (“nowcasts”) of GDP growth by exploiting more timely monthly indicators and the factor structure of the”
Text as Data (2017, background): “By contrast with the standard exercise of forecasting future variables, this process of using diverse data sources to estimate current variables has been termed “nowcasting” in the literature (Ba´nbura et al. 2013).”
A Survey of Econometric Methods for Mixed-Frequency Data (2013, result): “Banbura et al. (2012) present a similar but updated and extended application that confirms this finding.”
The Impact of Machine Learning on Economics (2018, background): “The emerging literature in academics, government, and industry on “now-casting” in macroeconomics (e.g. (Banbura et al., 2013) and ML begins to address some, but not all, of these issues.”
Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts (2014, background): “Alternatively, relatively accurate short-horizon forecasts can be obtained from bridging equations or factor models, surveyed in Banbura, Giannone, and Reichlin (2013b) and Banbura et al. (2013a).”
Collaborative Intent Prediction with Real-Time Contextual Data (2017, methodology): “Following existing work on nowcasting (Banbura et al. 2013, 2012; Giannone et al. 2008), we model the contextual information as stochastic processes and represent users’ historical and side data as time series.”
Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, background): “Ba nbura et al. (2013) survey the use of DFMs for nowcasting.”