Monetary Policy in a Data-Rich Environment

By Ben S. Bernanke, Jean Boivin. 2003

768 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a7012e572352101acbd081ec58e2f991f9e81e3c

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In Economics and Econometrics

Citations in the corpus

Has Monetary Policy Become More Effective? (2006, methodology): “ λt = Et 3 λt+1 + Rt − πt+1 ́ , (8)”
Bayesian VARs with large panels (2007, background): “In particular, it is confirmed that adding variables helps in resolving the price puzzle (on this point see also Bernanke and Boivin, 2003; Christiano, Eichenbaum, and Evans, 1999).”
A Tale of Two Decades: The Ecb's Monetary Policy at 20 (2019, background): “Bernanke and Boivin (2003) find that the number and variety of series included in the forecasters’ data set matters very much for forecasting performance.”
DSGE Models and Central Banks (2009, positive): “Furthermore, research has shown that information contained in large data sets is relevant for the evolution of macroeconomic time series (Bernanke and Boivin 2003).”
DSGE Models in a Data-Rich Environment (2006, neutral): “Combining (8)—(9) and using (5), we obtain the observation equation”
Bayesian Model Averaging and exchange rate forecasts (2003, neutral): “capable of superior predictive performance (Stock and Watson (2002b) and Bernanke and Boivin (2003) are among the many possible cites).”
Determining the number of factors in approximate factor models with global and group-specific factors (2008, neutral): “[5] Bernanke, B.”
Factor-Midas for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP (2010, positive): “MIDAS-basic (7) and its multivariate extension (10) are based on the same nding as the smooth projection: one regresses low-frequency GDP on skip-sampled highfrequency factors, but with a di¤erent functional (exponential lag) form and allows for non-zero lag orders.”
Business Cycle Transmission from the US to Germany: A Structural Factor Approach (2007, neutral): “Bernanke and Boivin (2003)).”
Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases (2005, positive): “On the basis of such models, Bernanke and Boivin (2003) and GRS formalize the real-time application of large datasets to nowcasting and forecasting inflation and output in the United States.”