TREND ANALYSIS

Trend Analysis of Singapore GDP



Trend Analysis of Singapore GDP

Q 2 B) Analysis of Seasonality of Data

Singapore economy has shown an incline in past few years. It has shown great recovery after economic recession which shows the potential of economy to have further improvement in GDP scale. In order to assess the GDP trend of Singapore economy, expert modeler technique has been applied on the available data of GDP taken from Quarter 1 of Year 2007 to Quarter 4 of 2010. Continuous variation has been observed in the GDP data of economy which requires adjustment in seasonality component to assess the future trend in economy. In order to effectively analyze the given data, multiple statistical techniques can be applied to effectively configure out the seasonality component in the data. Since GDP growth in economy is dependent upon many factors which require suppressing the effect of seasonal components which directly influences the seasonal and dimensional changes in GDP level.

In certain situations, incorporation of moving average trend through ARIMA modeling technique help in converting the non-stationary data to stationary data. In that case, exponential smoothing of the data for managing the sequential trend and seasonality component would need to be integrated (Creswell, 2009). Trend cycle variation as observed in the Singapore GDP Data is comparatively near to seasonal adjusted data as shown in Table 1. Trend cycle component increases the variation between data if seasonal trend remain unadjusted. It is essential to consider the seasonality component that defines the expected variation in the data to enhance the prediction of sequential GDP output change. Moving average regression technique has been applied to assess the effectiveness of seasonal trend in GDP by using multiplicative method of trend line.

Analyzing the impact of seasonal factor component direct impact has been observed on the trend line and estimated level of GDP. Seasonal index figures show close variation in the seasonality analysis as it range between 98.7% - 101.4%. This created a direct variation in the deasonalised time series of GDP. Data presented in the table 1 below eradicates the component of seasonality by excluding the error in separate column to enhance the prediction of estimated GDP for the same period. As shown in graph 2, there is not much difference between the actual GDP and the trend in GDP which is shown in estimated GDP line. Component of seasonality indexes have been adjusted in the ...