Programme PhD Term IV Academic Year 2021-22

Course title Times Series Analysis Area Economics Credits 1.50

Prof. Anindya Chakrabarti

Course Description & Objectives
This course introduces the theory and methods of time series analysis for research in economics and finance. The objective of the course is two-fold. First is to give participants enough technical background to enable them to read research papers in applied time series analysis. The second is to introduce select advanced topics useful for analysis of macroeconomic and financial time series.

After introducing fundamental concepts in time series analysis, the course covers the theory of stationary ARMA processes and reviews the relevant asymptotic distribution theory. This forms the introductory part of the course and forms the basis for studying Vector Autoregressions (VARs) which is discussed next.

Moving on from considering covariance stationary processes, the course next introduces the econometrics of unit roots. The core of the remaining portion consists of studying linear combinations of unit root processes, i.e. Cointegrated Systems (VECMs), and models with conditional heteroskedasticity (GARCH). We introduce an array of extremely important tools and techniques based on fractional integration, complexification of time-series, principle component analysis as well as multivariate analogues of all univariate models under consideration. We end the course by introducing State Space representations of time series models and Bayesian methods.