1. Semester: IV
2. Number of Credits: 4

Preamble
The course aims at building on the Basic Econometrics course to equip the students with econometric techniques of time series analysis. Though the approach will largely be applied, some derivations of theoretical results will be emphasized. Applications to financial econometrics at the level of Chris Brooks’ Introductory Econometrics for Finance will be emphasized.

Module 1: Introduction and Single Equation ARIMA Models (16 lecture hours)
Difference equations and lag operators, Data generating process, Characteristic equations, Ergodicity and Stationarity, Autocorrelation and Partial autocorrelation functions, Stationary ARMA processes, Invertibility, Box-Jenkins Approach to identification, estimation and diagnostic checking, Time series analysis in the frequency domain, Uses of spectral analysis.

Module 2: Modelling Volatility and Trends (8 lecture hours)
ARCH and GARCH processes, Deterministic and Stochastic trends, Dickey-Fuller tests and extensions including testing for structural change.

Module 3: Multi-equation Time Series Models (12 lecture hours)
Transfer Function Models, Intervention analysis, VAR models, Impulse-response functions and forecast error variance decomposition, Cholesky and Blanchard-Quah decompositions, Structural VAR models.

Module 4: Cointegration and Error-Correction Models (12 lecture hours)
Cointegration and Common trends, Error-correction models, Engle-Granger methodology, Cointegration in multivariate models, Johansen Methodology, General-to-specific modelling, topics in non-linear time series models: Threshold autoregresive models – Estimating Regime switching models.