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Aparna Sawhney                                                                                 MA(World Economy) CITD

Statistics and Econometrics

This introductory course on statistics and econometrics consists of two modules with the following topics:

Module I (pre-midterm)

  1. Probability and Random Variables:  Definitions and axioms of probability, probability set functions; probability density functions, distribution and characteristic functions; conditional probability.
  2. Some Well-known Distributions: Binomial, Poisson, Uniform, Normal, Gamma, Chi-square, and Bivariate Normal Distribution.
  3. Sampling Methods, Sampling Distributions and Limiting Distributions: Random sampling, transformation of variables, t and F distributions, sampling distributions of the mean and the variance. Limiting distributions, stochastic convergence, the Central Limit Theorem.
  4. Statistical Inference: Point and interval estimation. Unbiasedness, asymptotic unbiasedness, consistency, and efficiency of estimators.  Method of maximum likelihood and properties of MLE estimators. Testing of hypotheses, errors of first and second kind, power of the test, and likelihood ratio test.

Module II (post-midterm) 

  1. Simple Linear Regression: Method of least squares, properties of OLS estimators and goodness of fit. Gauss Markov Theorem.
  2. Multiple Linear Regression Analysis: General case (k-explanatory variables); examples with k=2 & 3.  Relationship between simple correlation, partial regression and multiple regression coefficients. Misspecification of models, omitted variable bias and properties of OLS estimates.  Problem of multicollinearity.
  3. Inference in the Regression Model: Hypothesis testing for significance of a subset of coefficients; and overall significance.
  4. Generalized Least Squares and Feasible Least Squares: Violation of assumption on spherical errors (problems of autocorrelation and heteroscedasticity), GLS and FGLS. Tests to detect autocorrelation and heteroskedasticity.  Problem of autocorrelation in lagged dependent variable models.

References:

  • Robert V. Hogg and Elliott A. Tanis: Probability and Statistical Inference, 7th edition (2006).
  • Robert V. Hogg and Allen T. Craig: Introduction to Mathematical Statistics, 3rd edition.
  • James Stock and R.W. Watson: Introduction to Econometrics (International edition 2007)
  • Jeffrey Wooldridge: Introductory Econometrics: A Modern Approach, (2006).

Course Grade

The grade for the course will be based on your performance in the mid-semester and end-semester examinations (50% weight on each test).