A0176
Title: Nonlinear fore(back)casting and innovation filtering for causal-noncausal VAR models
Authors: Joann Jasiak - York University (Canada) [presenting]
Abstract: Closed-form formulas of out-of-sample predictive densities are introduced for forecasting and backcasting of mixed causal-noncausal (structural) vector autoregressive VAR models. These nonlinear and time-irreversible non-Gaussian VAR processes are shown to satisfy the Markov property in both calendar and reverse time. A post-estimation inference method for assessing the forecast interval uncertainty due to the preliminary estimation step is introduced, too. The nonlinear past-dependent innovations of a mixed causal-noncausal VAR model are defined, and their filtering and identification methods are discussed. The approach is illustrated by a simulation study, and an application to cryptocurrency prices.
