A Nonparametric Approach to Augmenting a Bayesian VAR with Nonlinear Factors

Todd Clark, Florian Huber and Gary Koop

No 26-14, Working Papers from Federal Reserve Bank of Cleveland

Abstract: This paper proposes a vector autoregression augmented with nonlinear factors that are modeled nonparametrically using regression trees. There are four main advantages of our model. First, modeling potential nonlinearities nonparametrically lessens the risk of misspecification. Second, the use of factor methods ensures that departures from linearity are modeled parsimoniously. In particular, they exhibit functional pooling where a small number of nonlinear factors are used to model common nonlinearities across variables. Third, Bayesian computation using MCMC is straightforward even in very high-dimensional models, allowing for efficient, equation-by-equation estimation, thus avoiding computational bottlenecks that arise in popular alternatives such as the time-varying parameter VAR. Fourth, existing methods for identifying structural economic shocks in linear factor models can be adapted for the nonlinear case in a straightforward fashion using our model. Exercises involving artificial and macroeconomic data illustrate the properties of our model and its usefulness for forecasting and structural economic analysis.

Keywords: Nonparametric VAR; nonlinear factor model; regression trees; macroeconomic forecasting; scenario analysis (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 40
Date: 2026-06-02
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DOI: 10.26509/frbc-wp-202614

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