Econometrics 2 provides the basic tools of applied econometric analysis. The course is based on regression analysis (covered in Econometrics 1), and gives a thorough introduction to the problem of endogeneity with possible treatments, time series regressions, linear panel models, and nonlinear probability and censored outcomes models. 

Successful completion of the course enables students

 1. To understand how econometric methods are used to estimate causal relationships from observational data, 2. Possess a critical understanding of identification and estimation problems in economics and other social sciences, 3. Formulate simple research questions and carry out independent analyses in order to answer those and argue for and against endogeneity of right-hand side variables, 4. Prove consistency and asymptotic bias of estimators, 5.Understand the logic of sampling variance and distribution of estimators,6. Understand the properties of time-series.

Overview of issues of multicollinearity, Heteroscedasticity and Autocorrelation; Causes, effects, tests and remedial measures. Model specification issues, limited dependent variables (LPM, Tobit, Logit and Probit Models). Auto regressive and distributed lag models. Time series analysis. Simultaneous equation models and their estimation approaches. Panel Data Analysis.

1. Gujrati, D.N. (2009). Essentials of Econometrics. 4thEdition, London: McGraw-Hill.
2. Wooldridge J.M. (2012). Introductory Econometrics: A Modern Approach.5th Edition. USA: Cengage Learning Publisher. 
3. Mirar, T.W. (1990). Economic Statistics and Econometrics. New York: McMillan Publishing Co.
4. Ramanathan R. (2001).  Introductory Econometrics with Applications. 5th Edition. South-Western College Publisher.
5. Butt A.R. (1999). Least Square Estimation of Econometric Models, Islamabad.

Sessional:                 08 (Class Attendance:2, Presentation:2, Assignments: 4)

Mid Term Test:          12

Final Term Test:         20(T)+20 (P)