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ECONOMETRICS

Source: www.econ.upf.edu
Topic: Econometrics

Sort Desciption: econometrics and an introduction to topics that will be deeply covered in other ... econometric sequence. The basic estimations procedures (least squares ...

Content Inside: UPF GPEFM Department of Economics Fall Semester 2006 ECONOMETRICS Professor Jaume Garca Jaume I 20.203 Office hours: By appointment. Pone: 93 542 1748 Email: jaume.garcia@upf.edu Course Description The course is designed to provide a detailed and technical exposition of the basic procedures of econometrics and an introduction to topics that will be deeply covered in other courses of the econometric sequence. The basic estimations procedures (least squares maximum likelihood and generalized method of moments) are discussed. A working knowledge of multivariate calculus and linear algebra is required. Grading Grades will be based on the problem sets (20% with the lowest grade on the problem sets dropped) and a final exam (80%) with a minimum of 4 over 10 in the final exam for this rule to apply. The problems sets have empirical and theoretical questions. Readings and Reference Material Any of the following books covers the concepts that will be developed in the course. References to more specific material will be given during lectures. Greene W. H. (2002) Econometric Analysis Maxwell MacMillan International Editions. Basic textbook. Hayashi F. (2000) Econometrics Princeton University Press. Too advanced for some issues. Johnston J. and J. Dinardo (1997) Econometric Methods McGraw-Hill. Wooldridge J. (2002) Econometric Analysis of Cross Section and Panel Data MIT Press. It has a very interesting approach but it is too advanced for many issues. Course outline 1. Introduction 1.1. Course requirements and overview 1.2. Econometric software 1.3. Econometric Methodology 2. Classical regression model 2.1. Multivariate regression and the least squares principle 2.2. The Gauss-Markov theorem 2.3. Hypothesis testing 3. Asymptotic theory for the classical regression model 3.1. Convergence modes 3.2. Laws of large numbers and central limit theorems 3.3. Consistency and asymptotic normality of the LS estimator 4. Maximum-likelihood ...

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