Credit Market Imperfections and Business Cycle Dynamics: A Nonlinear Approach

Linear Vector Autoregression (VAR) models provide a useful starting point for analysing multivariate relationships between economic variables. They are frequently used for empirical macroeconomic modelling, policy analysis and forecasting. However, linear VAR systems fail to capture non-linear dynamics such as regime switching and asymmetric responses to shocks, suggested by the recent theoretical developments in macroeconomic research. In addition, an increasing body of empirical evidence suggests that the linear conditional expectations implied by standard VAR models do not always accord with the observed facts. For example, a significant number of empirical studies document asymmetries in the effects of monetary policy on output growth. This paper employs a more general nonlinear VAR methodology to re-examine previous findings that credit market conditions contribute to economic fluctuations as a propagator of shocks. Unlike linear projections it allows for nonlinear dynamics and asymmetric effects of shocks. We estimate a threshold vector autoregression (TVAR), in which the system's dynamics change back and forth between credit constrained and unconstrained regimes. Using generalised impulse response functions (GIRF) generated from the estimated nonlinear model, we examine the real effects of monetary policy. We find evidence of asymmetry in the effects of monetary policy in the credit constrained and unconstrained regimes as well as different output effects of monetary contractions and expansions.