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 This course unit aims at equipping students with in-depth regression analysis and related Techniques.  The course will involve simple and multiple linear regressions (OLS); matrix representation of the regression model; statistical inferences (correlation analysis, T-test, F-test CHi2 test, ANOVA) for regression model; properties of OLS; diagnostics and remedies for multicollinearity and heteroskedasticity; diagnostics for model selection, variable selection, transformations (such as log, Box-Cox, etc.); appropriate Statistical packages (e.g. STATA) will be used in the course unit to demonstrate how to apply the Techniques on real data.

Model specification and data: developing a conceptual framework; types and sources of data, data mining, model specification and data generation; the mathematical Programming approach to policy analysis: the classical MP models, limitations of of MP models and extension to positive mathematical Programming, classification of mathematical Programming models commonly used in policy analysis, application in hypothesis testing, application in analyzing policy instruments and commodity policy, application in forecasting; econometric approach to policy analysis.

Classification of econometrics models, linear and none linear models, limited and censor-dependent variable approaches (logit, probit, tobit, and their extensions such as multinomial logit and probit etc.), system of equations or simultaneous equations, application in hypothesis testing, application in analyzing policy instruments and commodity policy, application in forecasting, the problem of causality in policy analysis, limitation of econometric models; impact assessment: propensity score matching, regression discontinuity designs, panel data to analyze with staggered entry.

·         Introduction to Econometrics Preview

·         Simple Linear Regression: Basic Concepts

·         Simple Linear Regression: Estimation of Parameters

·         Simple Linear Regression: Outliers

·         Multiple Linear Regression: Basic Concepts

·         Multiple Linear Regression: Estimation of Parameters

·         Autocorrelation: Durbin-Watson Test Statistic

·         Multivariate Regression

·         Logistic Regression

·         Polynomial Regression

·         Autoregressive Model

·         Vector Autoregressive Model

.         Limited and censor-dependent variable approaches (logit, probit, tobit, and                their extensions such as multinomial logit and probit etc.),

Detailed Course Content

Referencences:

Econometrics (2022) by Bruce Hansen, recommended but not required.

https://www.ssc.wisc.edu/~bhansen/econometrics/

Other complementary Texts include:

Angrist, J.D. and J. Pischke (2009): Mostly Harmless Econometrics - An Empiricists Companion, Princeton University Press

Amemyia, T. (1985): Advanced Econometrics, Blackwell

Berndt, E.R., The Practice of Econometrics: Classic and Contemporary. Addison-Wesley, 1991.

Chow, G. C. Econometrics. McGraw Hill, 1983.

Cameron, A. C. and Travedi, P.K. (2005): Microeconometrics – Methods and Applications, Cambridge University Press

Davidson, R. and McKinnon, J.G. (1993): Estimation and Inference in Econometrics, Oxford University Press

Drhymes, P. (1994) Topics in Advanced Econometrics, Vol 2: Linear and Nonlinear Simultaneous Equations, Springer Verlag

Green, W.A., Econometric Analysis, 8th edition, Prentice Hall.

Goldberger, A. S., A Course in Econometrics. Harvard University Press, 1991.

Hayashi, F. (2000). Econometrics. Princeton: Princeton University Press

Malinvaud, E. Statistical Methods of Econometrics, 3rd Edition. North-Holland, 1980.

Ramanathan, R. Statistical Methods in Econometrics, Academic Press, 1993.

Ruud, P.A. (2002): An Introduction to Classical Econometric Theory, Oxford University Press.

Sargan, J. D. Lectures on Advanced Econometric Theory. Basil Blackwell, 1988

Wooldridge, J.M . (2002): Econometric Analysis of Cross-Section and Panel Data, MIT Press.

Schervish, M.J. (1995): Theory of Statistics, Springer Verlag

Spanos, A. Statistical Foundation of Econometric Modeling. Cambridge University Press, 1986.

Theil, H. (1971): Principles of Econometrics, Wiley

White, H. (2000): Asymptotic Theory for Econometricians (Revised Edition), Academic Press

Zellner, A. An Introduction to Bayesian Inference in Econometrics. Wiley-Interscience, 1996.

 Gurmu, Shiferaw, and Pravin K. Trivedi, (1996)"Excess Zeroes in Count Models for Recreational Trips." Journal of Business and Economics Statistics 14: 469-477.
  

 Blundell, Richard, Rachel Griffith, and Frank Windmeijer, (1984)"Individual Effects and Dynamics in Count Data Models," London: Institute for Fiscal Studies Working Paper No. W99/3 (1999).

Hausman, Jerry A., Zvi GriliLHes, and Bronwyn H. Hall, "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica 52: 909-938.

Hall, Bronwyn H., Zvi GriliLHes, and Jerry A. Hausman, (1986)"Patents and R&D: Is There a Lag?" International Economic Review 27: 265-283.

Montalvo, Jose Garcia, (1993) "GMM Estimation of Count Panel Data Models with Fixed Effects and Predetermined Instruments," Journal of Business and Economic Statistics 15: 82-89.

LHamberlain, Gary, "Multivariate Regression Models for Panel Data, (1982)" Journal of Econometrics 18:5-46.

Exercise Collections:

P. C. B. Phillips and M. R. Wickens, Exercises in Econometrics, Vol. I & II. Allen/Ballinger, 1978.

K. Abadir and J. Magnus, Econometric Exercises Vol 1: Matrix Algebra. Cambridge University Press, 2005.

This is a continuation of biblical Greek studied last semester.

Strictly, a student who has not learnt Greek one is not supposed to enroll in this course unit.

The main focus of this course unit is on:

1. Declension

2. Verbs

3. Short Bible verses translation

Course description 

The course aims at providing understanding of the economic context of social work with special focus on grassroots social-economic structures, institutions and economic policy management. It emphasizes critical analysis and thinking of non-market aspects of the social economy in least developed economies taking into account community-based economic management initiatives world-wide.

It focuses on the interface between the “community economy” and the market economy while emphasizing the need to re-orient/direct policies towards communities as a tool for social development through pro-poor, inclusive economic growth. Much of the focus is on the transition from subsistence-based peasant economy to a fully monetized economy through policy-driven commercialization of agriculture and service facilities.

The student is expected to understand and explore the links between grass roots socio-economic change and the role of social workers in improving the well-being of citizens in general and specific vulnerable groups (VGs) in particular.

The main objective therefore is to assist social work professionals and other development professionals to learn how to build, manage and develop communities through effective and sustainable public and private action for poverty alleviation, employment creation, empowerment of marginalized groups and community solidarity using diversity of community development approaches.

The key questions addressed   in this module include among others: How communities organize, assess and plan for development. How community development is defined by the cultural, social, political, and economic realities of particular countries and communities how and   Gender sensitive and Microfinance lead to  development, what good governance entails and how it leads to development , what is needed to integrate economic and social development among others

This course focuses on the characteristic features of different human parasites including their life cycles, Taxonomy of the parasites and the role of Zoonoses in the transmission of diseases

Click here to find the course curriculum