Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model

Abstract

This paper introduces a regime switching vector autoregressive model with time-varying regime probabilities, where the regime switching dynamics is described by an observable binary response variable predicted simultaneously with the variables subject to regime changes. Dependence on the observed binary variable distinguishes the model from various previously proposed multivariate regime switching models, facilitating a handy simulation-based multistep forecasting method. An empirical application shows a strong bidirectional predictive linkage between US interest rates and NBER business cycle recession and expansion periods. Due to the predictability of the business cycle regimes, the proposed model yields superior out-of-sample forecasts of the US short-term interest rate and the term spread compared with the linear and nonlinear vector autoregressive (VAR) models, including the Markov switching VAR model.

Modeling and predicting U.S. recessions using machine learning techniques

“…More recently, Barsoum and Stankiewicz (2015) analyze business cycle patterns in macroeconomic time series with Markov switching mixed data sampling (MIDAS) models. Nyberg (2018) introduces a regime switching vector autoregressive model with time-varying regime probabilities, where the switching dynamics are generated by an observable binary response variable simultaneously predicted with the variables subject to regime changes. The model reveals a strong bidirectional link between U.S. interest rates and the business cycle as defined by the National Bureau of Economic Research.…”

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