- to provide the theoretical background that is useful for research in applied economics
- to equip the students with the advanced theory of econometrics and relevant applications of the methods.
- to acquaint the students with advanced techniques in time-series and panel-data analysis as well as the implementation of theory through software applications to gear them towards the execution of independent research projects.
- to introduce students to basic modeling techniques in the analysis of cross-section, panel and time-series economic data
- to provide students with sufficient econometric training to read the applied literature in core journals that use these standard techniques
- to prepare students for a dissertation topic that analyses either cross-section, panel or time-series data using basic econometric techniques
After the completion of the course, students will be able to –
- Interpret the results from regression models involving panel data and instrumental variables
- understand how to use instrumental variables to account for endogenous regressors
- understand how to estimate binary response models
- understand how to set up, estimate, and analyze panel data regression models; understand the basic concepts of stationary and non-stationary time series
- Chris Brooks (2014): Introductory Econometrics for Finance,3rd Ed, Cambridge University Press.
- Gujarati Damodar & Dawn C Porter (2017): Basic Econometrics,5th Ed, McGraw Hill. Gusti Ngurah Agung (2014): Panel Data Analysis Using EViews, Wiley.
- James H Stock and Mark W. Watson, Introduction to Econometrics, Pearson Education; 3rd edition
- Jeffrey M. Wooldridge (2010): Econometric Analysis of Cross Section and Panel Data, 2nd Ed, The MIT Press.
Unit- 1: Stochastic Process and Stationarity
1.1.Stochastic Process, Ergodicity and Stationary—White Noise Processes 1.2.Non-Stationarity and Random Walk Models—Deterministic and Stochastic Trends / Trend and Difference Stationary Processes-Integrated Stochastic Process 1.3. Non-Stationary Time Series and the problem of Spurious Regression—Solutions 1.4.Transforming the Non-Stationary Time Series—Tests of Stationarity — Correlogram (ACF, PACF), and Unit Root Test—Augmented Dicky-Fuller test—Non-parametric PP test—Structural Change
Unit- 2: ARIMA Modelling and Cointegration
2.1. The Wold Decomposition Theorem—AR and MA processes—ARMA and ARMAX— ARIMA Modelling 2.2. Linear combination of non-stationary series and Cointegration—Difference between Unit Root and Cointegration Tests– Augmented Engle-Granger test and Johansen –Juselius tests— Granger Representation Theorem 2.3. Cointegration and Error Correction Mechanism— VECM (Vector Error Correction Model) – Granger Causality
Unit- 4: Volatility Measurement and Growth Rate Estimation
4.1. Volatility Measurement—Measurement of Volatility ARCH and GARCH Models and Estimation—GARCH Forecasting 4.2. Growth Rate Estimation—Robustness—Endogenous and Exogenous Breaks—Kinked Exponential Growth Rates