This thesis discusses two central research topics in applied time series econometrics that generally belong in the field of Macroeconometrics. In particular, we investigate issues and methods which are of interest to those researchers who want to analyze economic problems or economic aggregates by means of time series data. The first topic deals with the dynamic interrelationships between sets of theory related variables in a multiple time series context. Research interest is primarily focused on the generalized or extended notion of Granger causality, that is the extension of the standard Granger causality concept to higher forecast horizons. The second topic deals with nonlinear behavior of macroeconomic time series, as well as the modelling of nonlinearities in economic time series using nonlinear econometric models. Specific attention is paid to unit root tests that allow stationarity around nonlinear trends in the form of smooth transitions under the alternative. The dissertation consists of two chapters. The first chapter presents the standard concept of Granger causality, along with the generalized or extended notion of causality, also known as multiple-horizon causality, in the vector autoregressive (VAR) framework. The standard notion of Granger causality restricts prediction improvement to a forecast horizon of one period, while it considers only direct flows of information between the variables of interest. However, in VAR models with more than two variables, the concept of standard Granger causality can be extended by studying prediction improvement at forecast horizons greater than one. If this is the case, then, except for direct causality, indirect flows of information might be revealed through the additional variables of the system. The theoretical framework of the extended concept of causality which is presented in the present dissertation has been developed by Dufour and Renault (1998). In addition, special attention is paid to two recent methods for testing hypothesis of non-causality at various horizons which can provide further information on the dynamic interaction of time series, and more specifically on the direct or indirect nature of causal effects, the distinction between short-run and long-run (non)-causality, as wells as the possibility of causal delays. Finally, the potential implementation of these methods is examined through empirical applications on causality relations among different sets of economic variables. Chapter 2 presents smooth transition (STR) trend models, as well as unit root tests that allow stationarity around smooth transitions under the alternative. Smooth transition regression models presume the presence of nonlinear trends in the long-run evolution of time series. A key feature of these models is the presence of structural changes in the deterministic trend which, given that they represent changes in aggregate behavior (economic aggregates), are modelled through a deterministic component that permits gradual rather than instantaneous adjustment between regimes. Unit root tests that permit a more versatile trend function in the unit root procedure, rather than the standard linear trends, are the main concern of Chapter 2. The necessity of employing additional unit root tests, such as unit root tests that allow stationarity around smooth transitions under the alternative, becomes evident through the unit root test results that are observed in an application in a set of economic time series.