Dr. Harry M. Karamujic. Univariate Analysis of Seasonal Variations in Building Approvals for New Houses: Evidence from Australia. Objectives.
Univariate Analysis of Seasonal Variations in Building Approvals for New Houses: Evidence from Australia
- Within a structural time series approach, the term ”structural” implies that a time series (in this paper, BANHs) is observed as a set of components not observable directly. The approach allows the selected time series, including intervention variables, to be modelled simultaneously with the unobserved components. The intention is to decompose the selected time series in terms of its respective components and to understand how these components relate to the underlying forces that shape its evolution.- The empirical analysis uses the model as presented in Harvey (1985, 1990), whereby time series are modelled in terms of their components. The model can be written as: rt = µt + γt + εt (1)where rt represents the actual value of the series at time t, µt is the trend component of the series, γt is the seasonal component and εt is the irregular component (assumed to be ‘white noise’).
- The major reason for selecting the structural time series modeling approach is that it allows for both stochastic and deterministic seasonality . - Conventional dynamic modeling with a deterministic seasonality approach totally ignores the likely possibility of stochastic seasonality (manifested as changing seasonal factors over the sampleperiod). - Evidently, a problem with the conventional procedure is that deterministic seasonality is imposed as a constraint, when in fact it should be a testable hypothesis .
Table 1:Estimated Coefficients of Final State Vector
Figure 1: Model 1 - Seasonal Component
Figure 2: Model 2 - Seasonal Component
Figure 3: Model 3 - Seasonal Component
Figure 4: Individual Seasonals