A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP

Abstract

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Bottom-up or direct? Forecasting German GDP in a data-rich environment

“…The need for time variation also may explain why rank-based weights outperform the equal weighting scheme or in-sample weights which adapt changes later than weights based on out-of-sample performance. Further, we can confirm the findings of Rünstler (2011) andDrechsel andMaurin (2011) that "hard data" (industrial production, turnovers,...) is more and more important as additional information of this series gets available, while at the first forecasting rounds surveys and financial data contribute at most to the forecast.…”

Nowcasting GDP in Real Time: A Density Combination Approach

“…The factor model specification we employ is similar to Giannone et al (2008) (see also Banbura and Rünstler (2011) for an extension. Assume we have a vector of n observable and stationary monthly variables X tm = (x 1,tm , .…”

“…The importance of using non-synchronous data releases (the jagged edge problem) for point nowcasting have also been highlighted by among others Evans (2005) and Banbura and Rünstler (2011).…”

Now-casting inflation using high frequency data

“…In the meanwhile, several monthly indicators are released. Giannone, Reichlin, and Small (2008) for the US, Angelini, Bańbura, and Rünstler (2007) and Bańbura and Rünstler (2007) for the euro area, show that using monthly indicators is crucial in order to nowcast accurately GDP.…”