Econometric Applications for Healthcare Management
Faculty Offering
Swarna Parameswaran
Assistant Professor, General Management & Public Policy
- swarna@gim.ac.in
- + 91 9013368272
Programme / Term
HCM / VI (at GIM)
No. of sessions
24 sessions
Course description
With healthcare management becoming significantly data-driven, it becomes essential for management graduates to develop robust business strategies, develop insights, and create value. Econometric applications provide organisations with potent tools to unlock the power of data and aid in effective decision-making. This course aims to introduce econometric techniques to healthcare management students as an evidence based decision-making tool, purely from an application point of view. By the end of the course, students will develop an adequate understanding of contemporary econometric methods and will be equipped to provide solutions to real-world problems using them.
Programme / Term
- Students will acquire knowledge of different econometric methods and datasets.
- Students will apply econometric methods to examine business problems in healthcare.
- Students will employ software skills to undertake econometric analysis for HCM problems.
- Students will analyse business outcomes in healthcare from econometric analysis.
- Students will interpret results from econometric analysis and recommend solutions to business problems
- Students will be able to select appropriate techniques to evaluate business scenario
1. Core Courses
a. Synchronous (topics and sub-topics to be covered in class) –
I. Introduction to Econometrics- different data types and datasets
II. Linear Regression Models- Simple and multiple linear regressions, choice of functional forms, dummy variables, and interaction effects
III. Limited Dependent Variable Models- Linear Probability, logit, probit, multinomial logit, count data, and duration models
IV. Time-series and forecasting- AR, MA, ARMA models
V. Panel Data Analysis- Fixed, and random effects models, difference-in-difference method.
b. Asynchronous (topics and subtopics for self-study through provided learning resources and
MOOCs) –
Learning applications of econometric techniques through research papers/cases.
Methodology
Lectures, hands-on practical application exercises using datasets (R exercises)
Group Projects and Journal
Group projects are an integral part of learning. Students are expected to form into groups of 4/5 to work on their projects. The projects would be due a week from the close of the term. Proposals are due by the 6th class. Students are expected to take a project in consultation with the instructor. The proposals must include a statement of the problem they seek to address, breaking down the same in to questions and an indication of how they would address the same. They should pose the questions sharply, and if the working involves data they must indicate the data sources as well.
Students would maintain a journal in which they would record questions that they raised and the learnings, on a session by session basis. The same would be submitted for evaluation.
For whom
- Students seeking a deep understanding of the new opportunities in PPPs and private capital in infrastructure, and willing to work hard would find the course engaging and useful.
- Consultants in government and infrastructure verticals, developers, regulators, and senior (credit) staff of BSFIs seeking to finance infrastructure projects, would find the course very useful saving them many years of learning on the job. Prerequisites for the course would be basics of microeconomics, capital finance, and financial markets.
- Students should have a sound knowledge of basic concepts in financing, microeconomics, and organizational management. This course would not suit the causal attendee.
Evaluation
Title
Percentage
Class Participation
15%
Assignment 1
25%
Assignment 2
25%
End-term examination
35%
TOTAL
100%
Section wise outline
Session Seq /Date & Time
Topic
Cases/Readings
1-2
Introduction to econometrics, types of data, and datasets
Basic Econometrics by Gujarati et al
3-5
Simple and Multiple Linear Regression Models and their applications- OLS estimation, hypothesis testing, and goodness of fit
Basic Econometrics by Gujarati et al
6-7
Choice of functional forms, and testing model restrictions and application to healthcare problems
Basic Econometrics by Gujarati et al.
8-9
Econometric applications in the case of binary independent variables and interactions between variables
Basic Econometrics by Gujarati et al
10-11
Violation of regression model assumptionsmulticollinearity and heteroscedasticity
Basic Econometrics by Gujarati et al
12-13
Econometric applications of linear probability, logit, and probit models in HCM
Basic Econometrics by Gujarati et al
14-15
Econometric applications of multinomial logit models, count data, and duration regressions in HCM
Basic Econometrics by Gujarati et al
16-20
Introduction to time- series and forecasting techniques- AR, MA, ARMA models
Econometrics by Gujarati et al
21-23
Panel data applicationsfixed, and random effects models, and difference-in-difference applications
Basic Econometrics by Gujarati et al
24
Assignment feedback and review
23 and 24
Non-linear regression models & Qualitative response regression models
Reference Books
1. Basic Econometrics by Damodar Gujarati, Dawn Porter, and Sangeetha Gunasekar, 5th Edition, Tata McGraw Hill, 2017
2. J.M. Wooldridge, Introductory Econometrics: A Modern Approach
Institutions where the course is offered
GIM