[We're working to add the prerequisite for 'Course Title: Econometrics' as soon as possible.]

This course provides the methodology and statistical techniques to test empirically the validity of economic hypotheses and to construct models to explain the evolution of the economic environment. Through the course, we will apply the theories developed to real-world data and interpret the estimation results in many different respects.

The main objectives of this course are to: - Introduce regression analysis to students so that they are able to understand its applications in different fields in economics; - Know the nature, scope, and sources of econometric data; - Understand the assumptions of the classical linear regression model; - Understand econometric modeling and model selection; - Use advanced levels of econometric models (Probability model, Dynamic Econometric Model, Structural Equation Model, Non-Linear Model), estimation methods, and related econometric theories; - Apply the above theories to empirical data or be able to develop new econometric theories.

After the end of the course, students will be able to: - Specify assumptions, formulate and estimate appropriate models, interpret the results, and test their statistical significance; - Conduct research in teams where they apply the techniques learned during the course and present their results; - Explain the mathematical basis of the linear Probability model, logit, probit, and tobit models; - Use the distributed lag model, Autoregressive model, and Structural Equation Model; - Explain the role of Non-Linear models and input-output analysis; - Write a good-quality undergraduate research paper in economics using the econometric methods taught in the class.

Contents: Basic Concept of Econometrics: Meaning, methodology of econometrics, types of econometrics, nature and source of data for econometric analysis, role of computers in econometric analysis, specification of PRF, different functional forms of PRF and transformation of PRF to linear form, meaning of population regression function (PRF) and sample regression function (SRF), stochastic. Detail study of problems of estimation and inference in multiple linear regression models, Likelihood Ratio, Wald, Lagrange Multiplier, and other suitable tests for testing regression, examination of linear regression results for fitted lines and residuals to detect assumption violations, parameters, verification of BLUE properties of linear regression by Monte Carlo experiments. Multicollinearity: Nature of multicollinearity, theoretical multicollinearity, estimation in presence of multicollinearity, theoretical and practical consequences of multicollinearity, detection of multicollinearity, remedy and measures of multicollinearity. Heteroscedasticity: Meaning and nature of heteroscedasticity, ordinary least squares and generalized least squares, estimation in the presence of heteroscedasticity, consequences of using ordinary least squares in the presence of heteroscedasticity, detection of heteroscedasticity by informal Pagan-Godfrey heteroscedasticity tests, remedial measures of heteroscedasticity by weighted least squares and formal methods (Park, Glejser, Spearman's rank correlation, Goldfield-Quandt, and Breusch-Pagan tests). Autocorrelation (Serial Correlation): Meaning and nature, ordinary least squares estimators and best linear unbiased estimators, estimators in presence of autocorrelation, consequences of using ordinary least squares in the presence of autocorrelation, detection of autocorrelation by graphical methods, run test, Durbin-Watson D-Test, H-Test, asymptotic autocorrelation, remedial measures of autocorrelation for both known and unknown rho, Cochrane-Orcutt iterative, Durbin's two-step, and EGLS methods of estimating rho, concept of autoregressive conditional heteroscedasticity (ARCH) model, generalized ARCH (GARCH) model, consequence of autocorrelation. Econometric Modeling: Average economic regression, methodology, and specification errors, types of specification error, nature, consequences, and remedies of specification errors, test of specification error, errors of measurement in dependent and explanatory variables, Monte-Carlo experiment of specification error. Model Selection: Leamer's and Hendry's approach to model selection, non-nested hypothesis test by (i) discrimination approach (ii) discerning approach and (iii) other criteria such as: Hocking's Sp measures, Mallow's Cp measure, Amemiya's PC measure, and Akaike's AIC measure, Schwarz criterion, Hannan Quinn, and Shibata criterion. Detail study of linear probability, logistic, probit, and tobit models to study regression on dummy dependent variables. Multivariate Time Series: Second-Order Properties, mean and covariance functions, multivariate ARMA (MARMA) models, best linear predictors, modeling and forecasting with MAR or VAR processes, VAR models, unit root models, error-correction model, cointegration analysis. State-Space Models: State-Space representation, basic structural model, state-space representation of ARIMA models, Kalman recursions, estimation for state-space models, state-space models with missing observations, EM algorithm, generalized state-space models.

1. Gujarati, D. N. and Porter, D. (2008). Basic Econometrics, 5th Edition, McGraw-Hill, New York. 2. Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach, 6th Edition, Cengage Learning, South-Western. 3. Judge, G. G., Hill, R. C., Griffiths, W. E., Lütkepohl, H. and Lee, T. C. (1988). Introduction to the Theory and Practice of Econometrics, 2nd Edition, John Wiley & Sons, New York. 4. Cameron, A. C. and Trivedi, P.K. (2005). Microeconometrics: Methods and Applications, Cambridge University Press, UK. 5. Johnston, J. and Dinardo, J. (1997). Econometric Methods, 4th Edition, McGraw-Hill, New York.