Time Series Econometrics and Forecasting | Humanities & Social Sciences

Time Series Econometrics and Forecasting

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For UG students: HUL315 / HUL215

Course Objective

This course will cover time series econometrics and forecasting with focus on applications in macroeconomics and international finance. It introduces students to time series regression models, its estimation, inference and forecasting. The objective of the course is for students to get hands-on experience with analyzing economic time series data. Accordingly, the emphasis of the course is on empirical applications.


Course Content

1. Stationary Univariate Models
a. Difference equation
b. Wold's decomposition
c. ARMA models
d. Box-Jenkins methodology
e. Model Selection
f. Forecasting
2. Non-stationary univariate models
a. Trend/cyclical decomposition
b. Deterministic and stochastic trend models
c. Unit root tests
d. Stationarity tests
3. Structural change and non-linear models
a. Test for structural change with unknown change point
b. Estimation of linear models with structural change
c. Regime switching models
4. Stationary multivariate models
a. Dynamic simulteneous equation models
b. Vector Auto Regression (VAR)
c. Granger causality
d. Impulse response function
5. Non-stationary multivariate models
a. Spurious regression
b. Co-integration
c. Vector Error Correction (VECM) model
6. Time series model of heteroskedasticity
a. ARCH, GARCH models

The information provided here may not be updated. Please check UG/PG section for updated course offering data.