1. Applied Econometric Time-Series Data Analysis

2. Types of Data Time series data Cross-sectional data Panel data 1
Data have been collected over a period of time on one or more variables.
Data have associated with them a particular frequency of observation (daily, monthly or annually…) or collection of data points.
Cross-sectional data 2
Panel data 3

3. The Procedure to Analysis
Economic or Financial Theory
Summary Statistics of Data
Basic Econometric
Advanced Econometric
Luukkonen et al. (1988) Linearity Test
not reject
If reject
Linear Model
Nonlinear Model

4. The Procedure to Analysis
Time Series Data
Unit Root Test
Non-Stationarity
Dickey-Fuller
Staionaruty
Augmented DF
Orders of Integration
H0: Yt ~ I(1)
H1: Yt ~ I(0)
VAR in Level
The same
Difference
Phillips-Perron
E-G
J-J
H-I KPSS
ARDL Bounding Test
DF-GLS, NP
H0: Yt ~ I(0)
H1: Yt ~ I(1)
KPSS
Cointegration Test

5. The Procedure to Analysis
Unit Root Test
Staionaruty
Cointegration Test
Yes No
EG,JJ, KPSS
ARDL
VAR in Level
VECM
UECM
(Pesaran et al., 2001)
VAR in differ
Model Specification

6. The Procedure to Analysis
Model Estimation
Economic or Finance
Implication
Impulse
Resp
Variance Dec
Granger
Causality

7. The Procedure to Analysis
Goodness-of-fit
R square
Error specification
Ramsey’s RESET
sationarity
CUSUM (square)
Series autocorrelation
Ljung-Box Q, Q2
Heteroskedastic
ACH-LM Teat
Normality
Jarque-Bera N
Diagnostic
Checking

8. Econometric Soft Packages
EViews
Rats
GAUSS
Matlab
Microfit
EasyReg
STATA
TSP

9. National Statistic, ROC
Sources of Data
DataBase
Website
AREMOS
TEJ Data bank
National Statistic, ROC
DataStream
Thomson Financial DataStream
CRSP
Compustat

10. Currency exchange rate
Example: PPP
Variables
Frequency
Sources
Currency exchange rate
ls=Log (S)
Annual
( )
Hayashi (2000)
Price index of UK
lukwpi=log (ukwpi)
Price index of US
luswpi=log (uswpi)
Real exchange rate

11. Summary Statistics of Data
No trend

12. Summary Statistics of Data

13. Stationary Time Series
Time Series modeling
A series is modeled only in terms of its own past values and some disturbance.
Autoregressive, AR (1)
Moving Average, MA (1)

14. Stationary Time Series
Box-Jenkins (1976) ARMA (p, q) model
The necessary and sufficient stationarity condition

15. Stationary Time Series
The determination of the order of an ARMA process
Autocorrelation function (ACF)
Partial ACF (PACF)
Ljung-Box Q statistic

16. Stationary Time Series
process
ACF
PACF
AR (p)
Infinite: damps out
Finite: cuts off after lag p
MA (q)
Finite: cuts off after lag q
ARMA(p, q)

17. Stationary Time Series
e series is AR(1)
P* = 1

18. Non-stationary Time Series
Autoregressive integrated moving average (ARIMA) model If
Y series is explosive
Y series has a unit root

19. Non-stationary Time Series
How to achieve stationary?
DSP = Difference stationary process
Yt ~ I(1) =
Yt ~ I(2) =
TSP = Trend stationary process

20. Non-stationary Time Series
Unit Root Test
ADF Test
KPSS
De-data
De-trend
De-mean

21. Non-stationary Time Series
Selection Criteria of the Lag Length
Schwartz Bayesian Criterion (SBC)
Akaike Information Criterion (AIC)
Small sample
Big sample
sum of squared residuals
observations
parameters

22. Non-stationary Time Series
Reject H0

23. Non-stationary Time Series
Engle-Granger 2-Stage Cointegration Test
Step 1: regress real exchange rate
Step 2: error term
Hypothesis
ADF Unit Root Test
If reject H0,
We support PPP

24. Non-stationary Time Series
Name as ppp

25. Non-stationary Time Series
Error – Correction Model (ECM)
Where x is independent variables
Residual ( ) Diagnostic Test

26. Non-stationary Time Series

27. Thank You !