“Mixed Causal-Noncausal Models; Identification, Estimation and Inference”

This thesis researches time series data (e.g. consumption, inflation) in the field of econometrics. Since most data is correlated with time, it is often modelled as to depend on its own past values. This common methodology is extended such that the dependence of data on its own future values and other related series is allowed. It is found that series containing cycles and economic bubbles can be represented by these models. Their applicability is investigated in various economic frameworks and a software package is developed to perform analysis with these models.