The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when there are few treated units. SCM assumes the stable unit treatment value assumption (SUTVA), which posits that potential outcomes are unaffected by the treatment status of other units. However, interventions often impact not only treated units but also untreated units, known as spillover effects. This study introduces a novel panel data method that extends SCM to allow for spillover effects and estimate both treatment and spillover effects. This method leverages a spatial autoregressive panel data model to account for spillover effects. We also propose Bayesian inference methods using Bayesian horseshoe priors for regularization. We apply the proposed method to two empirical studies: evaluating the effect of the California tobacco tax on consumption and estimating the economic impact of the 2011 division of Sudan on GDP per capita.