Predictive Macro-Finance With Dynamic Partition Models

Dynamic partition models are used to predict movements in the term structure of interest rates. This allows one to understand historic cycles in the performance of how interest rates behave, and to offer policy makers guidance regarding future expectations on their evolution. Our approach allows for a random number of possible change points in the term structure of interest rates. We use particle learning to learn about the unobserved state variables in a new class of dynamic product partition models that relate macro-variables to term structures. The empirical results, using data from 1970 to 2000, clearly identifies some of the key shocks to the economy, such as recessions. We construct a time series of Bayes factors that, surprisingly, could serve as a leading indicator of economic activity, validated via a Granger causality test. Finally, the in-sample and out-of-sample forecasts from our model are quite robust regardless of the time to maturity of interest rates.

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