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Applied Econometric Time-Series Data Analysis

Applied Econometric Time-Series Data Analysis. 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. Time series data. 1. Cross-sectional data.

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Applied Econometric Time-Series Data Analysis

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  1. Applied EconometricTime-Series Data Analysis

  2. 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. Time series data 1 Cross-sectional data 2 Panel data 3 Types of Data

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

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

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

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

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

  8. Econometric Soft Packages

  9. Sources of Data

  10. Example: PPP • 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

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

  18. Non-stationary Time Series • Autoregressive integrated moving average (ARIMA) model • If • 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. De-data De-trend De-mean Non-stationary Time Series • Unit Root Test • ADF Test • KPSS

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

  22. Non-stationary Time Series Reject H0

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

  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 !

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