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Monetary Policy in a Data-Rich Environment

  • Ben S. Bernanke
  • Jean Boivin

Most empirical analyses of monetary policy have been confined to frameworks in which the Federal Reserve is implicitly assumed to exploit only a limited amount of information, despite the fact that the Fed actively monitors literally thousands of economic time series. This article explores the feasibility of incorporating richer information sets into the analysis, both positive and normative, of Fed policymaking. We employ a factor-model approach, developed by Stock and Watson (1999a,b), that permits the systematic information in large data sets to be summarized by relatively few estimated factors. With this framework, we reconfirm Stock and Watson's result that the use of large data sets can improve forecast accuracy, and we show that this result does not seem to depend on the use of finally revised (as opposed to 'real-time') data. We estimate policy reaction functions for the Fed that take into account its data-rich environment and provide a test of the hypothesis that Fed actions are explained solely by its forecasts of inflation and real activity. Finally, we explore the possibility of developing an 'expert system' that could aggregate diverse information and provide benchmark policy settings.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 8379.

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Date of creation: Jul 2001
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Publication status: published as Bernanke, Ben S. and Jean Boivin. "Monetary Policy In A Data-Rich Environment," Journal of Monetary Economics, 2003, v50(3,Apr), 525-546.
Handle: RePEc:nbr:nberwo:8379
Note: EFG ME
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