Page 226 - DMGT404 RESEARCH_METHODOLOGY

Research Methodology

Notes Further, SS.I. = 1198.9  1200. Thus, we have to adjust these values such that their total is 1200.
1200
This can be done by multiplying each figure by . The resulting figures are the adjusted
1198.9
seasonal indices, as given below:

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
101.5 99.2 96.9 103.8 103.8 104.7 106.0 97.8 94.6 93.2 96.2 102.3
Remarks: The totals equal to 1200, in case of monthly indices and 400, in case of quarterly
indices, indicate that the ups and downs in the time series, due to seasons, neutralise themselves
within that year. It is because of this that the annual data are free from seasonal component.

Task Compute the seasonal index from the following data by the method of simple
averages.

Year Quarter Y Year Quarter Y Year Quarter Y
1980 I 106 1982 I 90 1984 I 80
II 124 II 112 II 104
III 104 III 101 III 95
IV 90 IV 85 IV 83
1981 I 84 1983 I 76 1985 I 104
II 114 II 94 II 112
III 107 III 91 III 102
IV 88 IV 76 IV 84

Merits and Demerits

This is a simple method of measuring seasonal variations which is based on the unrealistic
assumption that the trend and cyclical variations are absent from the data. However, we shall
see later that this method, being a part of the other methods of measuring seasonal variations,
is very useful.

10.6.2 Ratio to Trend Method

This method is used when cyclical variations are absent from the data, i.e., the time series
variable Y consists of trend, seasonal and random components.

Using symbols, we can write Y = T.S.R.
Various steps in the computation of seasonal indices are:
1. Obtain the trend values for each month or quarter, etc., by the method of least squares.
2. Divide the original values by the corresponding trend values. This would eliminate trend
values from the data. To get figures in percentages, the quotients are multiplied by 100.

S
Y T . .R
Thus, we have  100 =  100 = S.R.100
T T
3. Finally, the random component is eliminated by the method of simple averages.

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