Business Forecasting
Business Forecasting Coursework
Introduction
The data of this coursework are business investment in the quarterly series in the manufacturing sector from 1994 to the second quarter of 2008 in UK. In the coursework, firstly analyze the former 50 data to forecast the latter 8 ones and then compare with the real data to see if the forecasting model is a good fit or not. As adopting two different approaches to make the forecasting work, including regression with Dummy Variables method and Box-Jenkins ARIMA method, according to the results, relative comparisons will be made to demonstrate which one is a better choice for this certain question. Then discuss the underlying assumptions of the chosen model and evaluate whether it is sensitive to these assumptions. All the analyses are based on the SPSS software and the graphs are from the output.
Part 1. Examine the data
To apply certain model to forecast future value, find out the seasonal component, trends and cycles component is the basic job. There are two approaches to examine the data: see the time series plot (chart 1) or use ACF (chart 2).
Chart 1 Plot of the data
[pic]
Chart 2 ACF/PACF of the data
[pic][pic]
[pic][pic]
From both Chart 1 and Chart 2, the drawing conclusion is that the data has trend-cycle and seasonal components. Firstly, although there is no general upward trend and downward trend, clearly there is a cycle component: the data value climbs up in the first 20 data and then displays a down trend following behind. As to seasonal component, it is clearly from the time series plot that in each year the highest investment happened in the fourth quarter, while the lowest one occurred around the first quarter. From the ACF plot, the most significant autocorrelation is in lag 4, and there is also a spike in lag 8, indicating that there is a quarterly seasonal component. By carrying out a first difference, the ACF series plot display obvious quarterly seasonal component. Therefore the data value
Introduction
The data of this coursework are business investment in the quarterly series in the manufacturing sector from 1994 to the second quarter of 2008 in UK. In the coursework, firstly analyze the former 50 data to forecast the latter 8 ones and then compare with the real data to see if the forecasting model is a good fit or not. As adopting two different approaches to make the forecasting work, including regression with Dummy Variables method and Box-Jenkins ARIMA method, according to the results, relative comparisons will be made to demonstrate which one is a better choice for this certain question. Then discuss the underlying assumptions of the chosen model and evaluate whether it is sensitive to these assumptions. All the analyses are based on the SPSS software and the graphs are from the output.
Part 1. Examine the data
To apply certain model to forecast future value, find out the seasonal component, trends and cycles component is the basic job. There are two approaches to examine the data: see the time series plot (chart 1) or use ACF (chart 2).
Chart 1 Plot of the data
[pic]
Chart 2 ACF/PACF of the data
[pic][pic]
[pic][pic]
From both Chart 1 and Chart 2, the drawing conclusion is that the data has trend-cycle and seasonal components. Firstly, although there is no general upward trend and downward trend, clearly there is a cycle component: the data value climbs up in the first 20 data and then displays a down trend following behind. As to seasonal component, it is clearly from the time series plot that in each year the highest investment happened in the fourth quarter, while the lowest one occurred around the first quarter. From the ACF plot, the most significant autocorrelation is in lag 4, and there is also a spike in lag 8, indicating that there is a quarterly seasonal component. By carrying out a first difference, the ACF series plot display obvious quarterly seasonal component. Therefore the data value