Conference Agenda

Vector Multiplicative Error Models (vMEMs) provide a flexible framework for modeling multivariate non-negative time series. Within this framework, each variable is expressed as the product of its conditional mean—modeled as a function of past observations—and a positive innovation with unit expectation. Consequently, the model can capture dynamic cross-dependencies and have proven useful in applications such as modeling durations, volatilities, and trading volumes. This contribution focuses on goodness-of-fit (GOF) tests for vMEMs, aiming to assess whether the model structure and the assumed innovation distribution adequately reflect the properties of the observed data. We propose a GOF test statistic and derive its asymptotic distribution under the null hypothesis. The performance of a bootstrap version of the test is illustrated through Monte Carlo simulations.