Season - Mathematics and Statistics - Study Notes, Study notes of Mathematical Statistics

This document has following main points Season, Model, Moving Average Series, Ratios, Differences, Seasonal Factors, Seasonally Adjusted Series

Typology: Study notes

2011/2012

Uploaded on 10/31/2012

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SEASON
Based on the multiplicative or additive model, the SEASON procedure decomposes
the existing series into three components: trend-cycle, seasonal, and irregular.
Model
Multiplicative Model
XTCSI t n
tttt
==,,,1K
Additive Model
XTCSI t n
tttt
=++ =,,,1K
where TCt is the “trend-cycle” component, St is the “seasonal” component, and
It is the “irregular” or “random” component.
The procedure for estimating the seasonal component is:
(1) Smooth the series by the moving average method; the moving average series
reflects the trend-cycle component.
(2) Obtain the seasonal-irregular component by dividing the original series by the
smoothed values if the model is multiplicative, or by subtracting the smoothed
values from the original series if the model is additive.
(3) Isolate the seasonal component from the seasonal-irregular component by
computing the medial average (average) of the specific seasonal relatives for
each unit of periods if the model is multiplicative (additive).
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Based on the multiplicative or additive model, the SEASON procedure decomposes the existing series into three components: trend-cycle, seasonal, and irregular.

Model

Multiplicative Model

X (^) t = TC S It t t, t = 1 , K,n

Additive Model

X (^) t = TC (^) t + S (^) t + I (^) t, t = 1 , K,n

where TCt is the “trend-cycle” component, S (^) t is the “seasonal” component, and I (^) t is the “irregular” or “random” component. The procedure for estimating the seasonal component is: (1) Smooth the series by the moving average method; the moving average series reflects the trend-cycle component. (2) Obtain the seasonal-irregular component by dividing the original series by the smoothed values if the model is multiplicative, or by subtracting the smoothed values from the original series if the model is additive. (3) Isolate the seasonal component from the seasonal-irregular component by computing the medial average (average) of the specific seasonal relatives for each unit of periods if the model is multiplicative (additive).

Moving Average Series

Based on the specified method and period p, the moving average series Z (^) t for X (^) t is defined as follows: p is even, weight all points equally

Z

X p t t^ p^ n^ p

j j t p

t p

= =^ +^ −^ +

%

&

K K KK

'

K K K K

= −

  • − ∑ 2

2 1

, K, 1

SYSMIS otherwise

p is even, weights unequal

Z

X X p X p t p^ n p t

t p^ t p^ j j t p

t p

 



 

 +





  





  

%

&

K K K

'

K K K

− + = − +

  • − ∑ 2 2 2 1

2 1 2 , 2 1 , K, 2

SYSMIS (^) otherwise

p is odd

Z

X p t t^ p^ n^ p

j j t p

t p





  





   (^) =  !

" $#

  • −  !

" $#

%

&

K K KK

'

K K KK

= −! "$#

+! "$# ∑ 2

2

, K,

SYSMIS otherwise

SAF F p F

t t t^ p t t

= (^) p =

=

1

, , K,

Additive Model

Ft is defined as the arithmetic average of the series shown above. Then

SAFt = Ft − F,

where

F Ft p t

p

=

∑ 1

Seasonally Adjusted Series (SAS)

SAS X^ SAF

t (^) X SAF t m t m

% &

K 'K

1 6 100, ,

if model is multiplicative if model is additive

where

m = t −t p p

Smoothed Trend-Cycle Series

The smoothed trend-cycle series (STC) is obtained by applying a 3 × 3 moving average on seasonally adjusted series (SAS). Thus,

STC SAS SAS SAS SAS SAS

t n

t =^ t +^ t +^ t +^ t + t = −

− − + +

1 6 2 1 6 1 1 6 1 6 1 1 6 2 , , K,

and for the two end points on the beginning and end of the series

STC SAS SAS SAS

STC (^) n SAS (^) n SAS (^) n SASn

0 5 0 5 0 5 0 5

0 5 0 5 0 5 0 5

2 1 2 3

1 2 1

− =^ − +^ − +

STC STC STC STC

STC (^) n STC (^) n STC (^) n STCn

1 2 2 3

1 1 2

Irregular Component

For (^) t = 1, K,n

I

SAS STC

t SAS STC t t t t

% & '

0 5 0 5 0 5 0 5

if model is multiplicative if model is additive