This paper illustrates the application of observable index models to the problem of macroeconomic forecasting. In this context, a Bayesian prior is used to describe a class of models which impose the index structure with more or less weight. An out-of-sample forecasting experiment is used to measure the possible benefits of this approach. In addition, impulse response functions and the decomposition of forecast variance are analyzed to suggest a possible separation of real and nominal shocks into separate channels.
- Federal Reserve Bank of Minneapolis. Research Department
- Federal Reserve Bank of Minneapolis
- Dans Collection:
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