IIMA - Course Catalogue
Prof. Sanket Mohapatra
Course Description & Objectives
Econometric applications have become an integral part of training in modern economics and business management. Modern managers in a number of sectors are increasingly incorporating econometric applications into their businesses to establish sound economic strategies, to develop insight, create value, and outperform competition. Econometric applications provide organisations with a potent set of tools to unlock the power of information and in effective decision making. Therefore, it is imperative that management students have basic grounding on Econometric analysis before handling real life problems.
In PGP 1, under Quantitative Methods (Part 1 and 2), students are introduced to the basic concepts of probability and its applications, notions of sampling, point and interval estimation, testing of hypotheses, analysis of variance, basic framework of regression, etc. Building over this foundation, the main objective of the course is to introduce Econometrics as a decision making tool purely from application point of view. Towards this, the course aims at developing adequate understanding of regression methodology and showcases various applications to real life problems. Beginning with the very nature of Econometrics and economic data, we highlight some important real life problems upfront so as to ignite the problem solving ability of young managers through Econometrics. Following two sessions (Module 1) aim at recapitulating some of the basics concepts of regression from a more applied context. Subsequent sessions take regression methodology forward by questioning some of the fundamental assumptions, without going into the detailed technical stuff of regression methodology. What happens when basic assumptions like linearity, homoscedastic errors, causality, etc., are violated and what are the various tweaks to the data that enable us to overcome such issues forms the crux of Modules 2 and 3. Again all these issues are covered through various examples. The discussion in Modules 1 – 3 is typically restricted to cross-sectional data and an introduction to regression is not complete without discussing time series data in a greater detail. Time series data comes with additional issues like stationarity, trend, seasonality, cyclicality, etc. Module 4 is aimed at providing greater understanding of how to deal with such data. We conclude in Module 5 by discussing panel data where the data is both cross-sectional as well as time series in nature.
In the process of teaching, we will introduce various data sets. The primary idea is to understand the power of data analysis and to demonstrate some of the concepts which are covered in the class. They will cover wide range of topics including finance, marketing, politics, policy evaluation and HR. On top of that, we shall encourage students to come up with examples of application of regression in areas of their interest. There is a software component to this course. We will use Excel as well as more advanced statistical software like STATA and EViews for the course. So students will have benefit to learn the basics of STATA and EViews as well. Prerequisite for this course is the understanding of basic statistics. However, the desire to learn a technical tool which is increasingly used in today’s world to address real life problems is essential.
Lectures, Econometric case studies discussions, project presentations
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