In this paper we specify and estimate different Markov-switching (MS) regime autoregressive models. The empirical performance of the univariate MS models used to describe the switches between different economic regimes for the G-7 countries is in general not satisfactory. We extend these models to verify if the inclusion of asymmetric oil shocks as an exogenous variable improves the ability of each specification to identify the different phases of the business cycle for each country under scrutiny. Following the wide literature on this topic, we have considered six different definitions of oil shocks: oil price changes, asymmetric transformations of oil price changes, oil price volatility, and oil supply conditions. We measure the persistence of each economic regime, as well as the ability of each MS model to detect the business cycle dates as described by widely acknowledged statistical institutions. Our empirical findings can be summarized as follows. First, the null hypothesis of linearity against the alternative of a MS specification is always rejected by the data. This suggests that regime-dependent models should be used if a researcher is interested in obtaining statistically adequate representations of the output growth process. Second, three-regime MS models typically outperform the corresponding two-regime specifications in describing the business cycle features for each country. Third, the introduction of different oil shock specifications is never rejected. Fourth, positive oil price changes, net oil price increases and oil price volatility are the oil shock definitions which contribute to a better description of the impact of oil on output growth. Finally, models with exogenous oil variables generally outperform the corresponding univariate specifications which exclude oil from the analysis.