Department of Economics
Econometrics
III
Lecture by Professor Chien-Fu Jeff Lin
Office hours: Mon. 17:20~19:00
Wed.
e-mail 約時間。
I. Introduction to the course
· Course Objective: Develop the tools needed
to read about with understanding and to do empirical research in economics
using the current body of techniques.
This
course will provide fundamental econometric tools for Ph. D. or advanced Master
students. It is designed to prepare
the student for doing thesis or dissertation and understanding the track of
Econometrics. Knowledge of basic Statistics and Econometrics is required for
this course. The course is divided
into two parts. The first half we will study the mathematical (statistical)
foundation for Econometrics. The second half is the advanced tools of
asymptotic analysis for Econometrics. After being familiar with these material, students can understand what the content of
most formidable papers. This is a course of theoretical Econometrics not an
application one. However, the
programming software can help students to understand the theory behind. Some
programming software can be used in this course are Gauss, Matlab
or Sas IML. Students should be able to operate one
after this course.
· Course requirements: Lectures, readings, midterm and final exams, several home works. A short term paper is optional.
· Textbooks
1. Ramanathan, Ramu, (1993), Statistical Methods in Econometrics, Academic Press.
2. White, Halbert, (2000), Asymptotic Theory for Econometricians: Revised Edition, Academic Press.
· Reference Textbooks
1.
Hayashi, F. (2000), Econometrics,
2. Greene, W.H. (2002), Econometrics Analysis, 5th edition, Prentice-Hall.
3.
Hamilton, (1994), Time Series Analysis,
4. Johnston, J. and J. Dinardo (1998), Econometric methods, 4th Edition, McGraw-Hill.
5. Kennedy, P. (2003), A Guide to Econometrics, 5th edition, the MIT Press.
6. Mittelhammer, Ron C., George G. Judge, and Douglas J. Miller.
(2000), Econometric Foundations,
7. Wooldridge, J.M. (2002), Introductory Econometrics: A Modern Approach, 2nd edition, South Western College Publishing.
· Lecture notes
1. Hansen, B.
(2005), Econometrics,
http://www.ssc.wisc.edu/~bhansen/notes/notes.htm
2. Weber, A. (2003), Lecture
Notes: Econometrics I, Institute for Advanced Studies,
Topics
Basic Probability
Random Variables and
Distributions.
Some Special Distributions
Multivariate
Distributions.
Sampling Theory
Asymptotic
Distribution Theory.
Estimation.
Tests of Hypothesis.
Multiple Regression.
Functional Forms and Dummy
Variables
Nonspherical Disturbances.
Midterm
The Linear Model and Instrumental
Variables Estimators
Consistency
Laws of Large Numbers
Asymptotic Normality
Central Limit Theory
Estimating Asymptotic Covariance
Matrices
Functional Central Limit Theory and
Applications
Directions for Further Study
Final
Software: I recommend to students who want to carry out research in Econometrics or Applied Finance/Economics to use Gauss or Matlab (or Octave, its freeware version) or S-Plus (or R, its freeware version). The rest can use E-views or any other statistical package.
LeSage, J.P. (1999) Applied Econometrics using MATLAB. (Available at
http://www.spatial-conometrics.com/html/mbook.pdf).
GAUSS Source Code Archive at
http://www.american.edu/academic.depts/cas/econ/gaussres/GAUSSIDX.HTM)
MATLAB Tutorials.
http://www.cyclismo.org/tutorial/matlab/
http://www.math.siu.edu/matlab/tutorials.html
http://www.engin.umich.edu/group/ctm/basic/basic.html
Gauss
http://www.wws.princeton.edu/~mwatson/ec518/gauss_tutorial.html
http://www.arec.umd.edu/gauss/gauss.htm
www.aae.wisc.edu/aae636/gausscode/nerlove/Basics.doc
Others:
Textbook:
Ramanathan, Ramu,
(1993), Statistical Methods in Econometrics, Academic Press.
Book Description
This book is appropriate for beginning graduate
courses in mathematical statistics and econometrics in which the foundations of
probability and statistical theory are developed for application to econometric
methodology. Because econometrics generally requires the study of several
unknown parameters, emphasis is placed on estimation and hypothesis testing
involving several parameters. Accordingly, special attention is paid to the
multivariate normal and the distribution of quadratic forms. Lagrange
multiplier tests are discussed in considerable detail, along with the
traditional likelihood ration and Wald tests.
Characteristic functions and their properties are fully exploited. Also
asymptotic distribution theory, usually given only cursory treatment, is
discussed in detail.
The book assumes a working knowledge of advanced calculus (including integral
calculus) basic probability and statistics, and linear algebra. Important
properties from matrix algebra are summarized in the appendix. Numerous
examples, exercises, and practice problems are included.
White, Halbert, (2000), Asymptotic Theory for
Econometricians: Revised Edition, Academic Press.
Book Description
This book provides the tools and concepts necessary to study the behavior of
econometric estimators and test statistics in large samples. An econometric
estimator is a solution to an optimization problem; that is, a problem that
requires a body of techniques to determine a specific solution in a defined set
of possible alternatives that best satisfies a selected object function or set
of constraints. Thus, this highly mathematical book investigates situations
concerning large numbers, in which the assumptions of the classical linear
model fail. Economists, of course, face these situations often.
Key Features
* Completely revised Chapter Seven on functional central limit theory and its
applications, specifically unit root regression, spurious regression, and
regression with cointegrated processes
* Updated material on:
* Central limit theory
* Asymptotically efficient instrumental variables estimation
* Estimation of asymptotic covariance matrices
* Efficient estimation with estimated error covariance matrices
* Efficient IV estimation
·
Lecture
Progress
09/18 Ramanathan Chapter 1,2,3
09/25 Ramanathan Chapter 4,5
10/2 Ramanathan Chapter 6,7
10/9 Ramanathan Chapter 8
10/16 Ramanathan Chapter 9
10/23 出國開會No class
10/30 Ramanathan Chapter 10,11
11/6 Ramanathan Chapter 12 White 1
11/13 期中考
11/20
White Chapter 2,3
11/27
White Chapter 4
12/4 White Chapter
5
12/11
White Chapter 6
12/18
White Chapter 7
12/25
White Chapter 7
1/1 No class
1/8 White Chapter
8
1/15 期末考
Class
Presentation
09/18
Econometric methodology
09/25
Bayesian
10/2 Application
of log-normal distribution or exponential family
10/9 Application
of extreme value distribution to VaR
10/16
Numerical Optimization procedure
10/23 出國開會No class
10/30
Misspecification test
11/6 Model
selection criteria
11/13 期中考 Multicollnearity
11/20
Application of IV & GMM
11/27
Bootstrapping, MCMC, Gibbs Sampler
12/4 Nonparametric
(Kernel) estimation
12/11
Q-MLE
12/18 Var-cov estimation
12/25
Unit root test
1/1 No class
1/8 Panel unit
root
1/15 期末考