General Linear Regression Model: Basic structure, assumptions, and estimation of single equal model using Ordinary Least Square (OLS)/ Maximum Likelihood Estimator (MLE). Use of matrix algebra in inference related to multiple regression analysis.
Handling econometric problems like multicollinearity, autocorrelation and General linear aggression model, generalized least squares and maximum likelihood estimation; Hetetroscedasticity / Autocorrelation Consistent (HAC) estimator, dummy variables and structural shifts; non-linear models and estimation algorithms, panel data seemingly unrelated equations and simultaneous estimation.
Estimation with limited dependent variables, data censuring and selectivity bias.
Concept of stationary, analysis of stationary and integrated data, generalizing process, ARIMA models, forecasting and time series decomposition; analysis and decomposition of forecast errors. ARCH models and risk return analysis.
VAR models. Casualty influence, response analysis and multivariate decomposition and co-integration and error correction analysis.
Generalized least squares method. Use of dummy variables. Use of Instrumental variables.
Cross-Sectional / Panel Data: Estimation of single quation models for cross-sectional and panel data. Fixed and random effect models. Estimation of dynamic panel data models.
Time Series Analysis: ARIMA Models. Comparison of forecast based on ARIMA and regression models. Unit roots and co-integration. Dummy trap and its detection. Econometric forecasting: Stationarity, impulse response analysis. ARCH Models, VAR Models. Co-integration and error correction models.
Asterious, Dimitrios, and Stephen G. Hall. 2011. Applied Econometrics. 2nd Palgrave Macmillan.
Baltagi, B. H. 1999. 3rd ed. Springer Valog.
Enders, Walter. 2009. Applied Econometric Times Series. 3rd New York: Wiley.