Nowcasting: the real time informational content of macroeconomic data releases

By Domenico Giannone, L. Reichlin, David H. Small. 2008

1,073 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/83f8f7c8f449032a8a330ad17b9473cf497d3a0d

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In Economics and Econometrics

Citations in the corpus

Testing for Granger Causality with Mixed Frequency Data (2016, neutral): “The same argument also applies to MF modeling approaches which rely on a factor structure, see e.g., Mariano andMurasawa (2010), Giannone et al. (2008) and Bańbura and Rünstler (2011).”
Real-Time Measurement of Business Conditions (2007, neutral): “…and Watson (1989, 1991), Mariano and Murasawa (2003), Evans (2005), and Proietti and Moauro (2006), as well as Shen (1996), Abeysinghe (2000), Altissimo et al. (2001), Liu and Hall (2001), McGuckin, Ozyildirim, and Zarnowitz (2003), Ghysels et al. (2004), and Giannone, Reichlin, and Small (2008).”
Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials (2015, background): “…and thus is in particular helpful for nowcasting, namely computing and updating projections of the LF variable for the current period given all potential HF information which is available (Giannone et al., 2008; Marcellino and Schumacher, 2010; Andreou et al., 2010b; Kuzin et al., 2011).”
Internet search behavior as an economic forecasting tool: The case of inflation expectations (2011, positive): “[13] show that composite factors obtained from high-frequency macroeconomic indicators and soft information such as surveys can significantly improve both nowcasts and forecasts of GDP growth.”
Large Bayesian vector auto regressions (2010, methodology): “In NBER Macroeconomics Annual, Gertler M, Rogoff K (eds). MIT Press: Cambridge, MA; 161–200. Giannone D, Reichlin L, Small D. 2008. Nowcasting: the real-time informational content of macroeconomic data. Journal of Monetary Economics 55(4): 665–676. Gordon DB, Leeper EM. 1994. The dynamic impacts of ”
Stock Market Liquidity and the Business Cycle (2011, neutral): “The ability to improve forecasts and " nowcasts " (Giannone, Reichlin, and Small, 2008) of such central macroeconomic variables as unemployment, GDP, consumption and the like will be particularly interesting for central banks and other economic planners.”
Large Bayesian VARs (2008, neutral): “…forecasting results based on the Bayesian VAR with those produced by factor models where factors are 3However, due to their timeliness, conjunctural information, may be important for improving early estimates of variables in the current quarter as argued by Giannone, Reichlin, and Small (2008).”
Dynamic Hierarchical Factor Models (2013, methodology): “Giannone et al. (2008) are interested in taking advantage of the different timing of data releases for the purpose of “now-casting.””
Autoregressive models for matrix-valued time series (2018, neutral): “Multi-level or hierarchical factor models have been introduced in the econometric literature to study a large panel of data consisting of blocks or even subblocks (Moench et al., 2013; Diebold et al., 2008; Giannone et al., 2008).”