|Course Type||Course Code||No. Of Credits|
Semester and Year Offered: 2nd Semester (Winter Semester 2019)
Course Coordinator and Team: Krishna Ram
Email of course coordinator: krishna[at]aud[dot]ac[dot]in
Knowledge of mathematics (especially Algebra and Calculus), statistics, and Basic Econometrics at level of, say, Gujarati’s Essentials of Econometrics is required.
It is a tool course, which is designed to equip students for analysing real life data, related to economics in particular and social science in general, with the help of mathematical knowledge and computer software. In today’s world students are required to objectively analyse the problem at hand and this is true in social sciences as well. This course will acquaint the students with theoretical knowledge as well as implementation of theory through software applications like STATA. The main thrust of the course will be on cross-section data analysis, along with an introductory component on univariate time series analysis.
On successful completion of this course students will be able to:
- Use matrix approach of OLS estimation, hypothesis testing and diagnostic testing.
- Estimate multivariate linear regression model using cross-sectional data set.
- Identify all possible misspecification in the CLRM using econometric software package, STATA.
- Use the instrument variable technique for the model estimation.
- Estimate ARIMA model using econometric software package, Eviews.
Brief description of modules/ Main modules:
1. Review of Statistics: Empirical relation, Random variable, Probability distribution, Joint Probability distribution, Expectation, Conditional expectation, Estimation & Inference, and Matrix Algebra
Goldberger , Ch-1, & Ch-5; Greene, Appendix-A, B and C
2.Multivariate Regression Analysis with Cross-Section Data
- Matrix Approach of OLS estimation, Goodness of Fit, and Analysis of Variance
- Finite sample proprieties of OLS estimator,
- Asymptotic properties of the OLS estimator
- Hypothesis testing: Linear combination of parameters, Multiple Linear restrictions
- Functional form and Structural Change: Intrinsic linearity and identification, Dummy variable regression model, Testing for a structural break
- Specification issues and Model selection criteria: Omitted variable bias, Inclusion of irrelevant variables, Measurement Errors; R2, Adj R2 , Akaike Information Criterion (AIC) and Schwartz or Bayesian Information Criterion (BIC)
- Hetrocedasticity and Autocorrelation
- Instrumental Variable Estimation of single equation linear model (If time permits)
Greene, Ch-1-8, Ch11-12; Wooldridge, Ch-1-5
3. Univariate Time Series Econometrics
- Stationarity and Non-stationarity stochastic process
- Auto regressive (AR) times series model, Moving Average (MA) time series model,
- ARIMA Model
- Autocorrelation function (ACF), Partial autocorrelation function (PACF), Correlogram
Walter Enders, Ch-2; Brooks, ch-6; Greene, ch-20
Assessment Details with weights
- Two in class- exams, (30% weightage each)
- 1st class test- Mid-February
- 2nd class test- As per AUD end semester exam schedule
- Time Allowed: 3 hours. All question are compulsory. Simple non-programmable calculators are allowed.
- Term Paper (40% weightage)
- Each students has to present a topic of their term paper by 1st week of March which is worth 5% of overall weightage. The term paper is submitted in last week of April which is worth 35% of overall weightage.
- Goldberger, Arthur S. A course in Econometrics, Harvard University Press, England, 1991.
- Wooldridge, J. Econometric Analysis of Cross Section and Panel Data, 2nd ed., MIT Press, 2010.
- Greene, W.H., Econometric Analysis, (8th edition), Pearson, 2018
- Cameron, A.C. and Trivedi, P.K. Microeconometrics using Stata, 2nd ed., Stata Press, 2010
- Brooks, Chris. Introductory Econometrics for Finance, 3rd edition, Cambridge University Press, 2014
- Walter Enders. Applied Econometrics Time series, 2nd edition, Willy India, 2004