Lecture 8 time series

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KTEE 310-FINANCIAL ECONOMETRICS

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Lecture 6: TIME SERIES ANALYSIS AND
APPLICATIONS IN FINANCE

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Dr TU Thuy Anh
Faculty of International Economics

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QUARTERLY GDP
160000
140000
120000
100000

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80000

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60000

20000
0
1

4

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40000

7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76

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COMPONENTS OF A TIME SERIES
 Time series: An ordered sequence of values of a variable at equally

spaced time intervals
 Such as: vn index, inflation, gdp growth rate, etc.
 Components:

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 Trend
 Cycle

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 Irregular

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 Seasonality

 The 4 components may make up a TS in two ways:

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 additive model: Xt = Tt + St+Ct+It
 multiplicative model: Xt = Tt * St *Ct*It
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ASSUMPTIONS FOR TIME SERIES MODEL

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C.1 The model is linear in parameters and correctly
specified.
Y = b1 + b2 X 2 + … + bk X k + u

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C.2 The time series for the regressors are weakly
persistent

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C.3 There does not exist an exact linear relationship
among the regressors
C.4 The disturbance term has zero expectation

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C.5 The disturbance term is homoscedastic

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ASSUMPTIONS FOR TIME SERIES MODEL

C.6 The values of the disturbance term have
independent distributions
ut is distributed independently of ut' for t' ≠ t

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C.7 The disturbance term is distributed independently
of the regressors

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ut is distributed independently of Xjt' for all t'

(including t) and j

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C.8 The disturbance term has a normal distribution

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Assumption C.6 is rarely an issue with cross-sectional data. When
observations are generated randomly, there is no reason to suppose that
there should be any connection between the value of the disturbance term in
one observation and its value in any other.
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