ABSTRACT In this article, a methodology for analyzing bivariate time series with missing data is presented. It is assumed that there is a dynamical nonlinear relationship between the two time series, which is described by a threshold autoregressive (TAR) model. The time series analysis consists in the identification and estimation of the model in the presence of missing data. As a main result, the model parameters and the missing observations are estimated jointly. The TAR model analysis is accomplished by means of Markov Chain Monte Carlo (MCMC) and Bayesian approaches.