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Using data for U.S. and Canada, the authors find evidence of the time-varying nature of risk premia, which are obtained as difference between long term interest rates and their expected values. They then apply Kalman filtering to extract the conditional variance of term premia prediction errors; the results highlight that this variable is informative beyond term premia and spreads, and it significantly improves upon prediction capability of standard models. In particular, the conditional variance of term premia, reflecting the high volatility of financial markets, anticipates movements in the output growth. Empirical evidence supports the inverse correlation between term premia and business cycle fluctuations.
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