This paper considers a class of C-convolution-based Markov models in which we assume that the error term is dependent on the first lagged state variable and the dependence structure is modeled by a copula function. Such models appear suitable for studying nonlinearity in time series. We show...

We propose a general treatment of random variables aggregation accounting for the dependence among variables and bounded or unbounded support of their sum. The approach is based on the extension to the concept of convolution to dependent variables, involving copula functions. We show that some...