Aim of this thesis is to propose a test statistic that can test for true structural model in time series. Main concern of the thesis is to suggest a test statistic, which has joint null of unit root and no structural break (difference stationary model). When joint null hypothesis is rejected, source of deviation from the null model may be structural break or (and) stationarity. Sources of the deviation correspond to different structural models: Pure stationary model, trendbreak stationary model and trend-break with unit root model. The thesis suggests a test statistic that can discriminate null model from alternative models and more importantly, one alternative model from another. The test statistic that is proposed in the thesis is able to detect specific source of deviation from the null model. By doing so, we can determine the true structure model in time series. The thesis compares power properties of the test statistic that is proposed with the most favorable test in the literature. Simulation results indicate the power dominance over the test statistics in the literature. Moreover, we are able to specify true alternative model.