Background: As the name suggest, Applied Time Series modeling is all about modeling time series data and how to forecast with the data. Forecasting accuracy can be improved by analysis of time-series data, using appropriate modeling techniques and adopting analytical refinements. Macro and Micro economic data can be modeled using time series analysis to provide a sound basis for corporate planning.
Objectives: This is a three-day programme and will be organized by IFMR. It is intended for those who want a relatively non-technical introduction to applied time series econometrics analysis and forecasting techniques, involving non-stationary data. The emphasis is on why and how, and we will try to tell the story about technical portion of time series analysis in the most simplistic way as is possible. The latest techniques will be illustrated using software package EVIEWS and the results will be interpreted.
At the end of the program participants will be able to understand concepts, like, stationarity, modeling time-series, model selection criteria, forecasting a data generating process using Box-Jenkins methodology, vector auto regression (VAR), non-stationarity, co-integration, vector error corrrection (VEC), autoregressive conditional heteroskedastic (ARCH) and generalized ARCH (GARCH). Understanding these concepts are essential in trying to understand future movement of important variables like, price, foreign exchange and interest rates; understanding market structure (competitive, oligopolistic, monopolistic, or monopoly); and interpret basic numbers that decision makers/managers has to face in their day to day operation.
Target audience: Economists and senior managers working in corporate planning department, economist working in banks, senior executives in marketing function and researchers will find it useful. Preliminary knowledge of quantitative techniques is essential.
Date: MAY 7-9, 2008
Venue: IFMR Chennai
Fee: Rs. 15000/-(Non-residential)
Course Co-ordinator: Prof. Nilanjan Banik and Prof. Maheshwaran
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