ECON 2040: Econometric methods (Spring 2012)


NB: Due dates of assignments might change. Check the website regularly for up-to-date information.

Week
Monday - 10.30 to 11.50
Wednesday - 10.30 to 11.50

Solutions and Statistics

Solution
Average
Max





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Jan. 30
Feb. 6
Feb. 13
Feb. 20
Feb. 27
Mar. 5
Mar. 12
Mar. 19
Mar. 26
Apr. 2
Apr. 9
Apr. 16
Apr. 23




No class




No class (Spring break)



Review session
Jan. 25
Feb. 1
Feb. 8
Feb. 15
Feb. 22
Feb. 29
Mar. 7
Mar. 14
Mar. 21
Mar. 28
Apr. 4
Apr. 11
Apr. 18
Apr. 25
1st class





Midterm exam


No class (Spring break)



Final exam - 10.30 to 12.30

* No course notes will be provided.

* Material is based on the required book: F. Hayashi., Econometrics - I recommend that you solve all the exercises in this book.
Online resources for the book are available from the website http://fhayashi.fc2web.com/hayashi_econometrics.htm.


* Some books have been reserved at the library.


- PART 1: Linear regression model (Ref. Hayashi 1,2,3)

1.       Basic assumptions (Linearity, exogeneity, no multicollinearity)
2.       Geometric and statistical interpretations of least squares (Frish-Waugh theorem; Statistical interpretation; Geometric interpretation in the space of random variables)
3.       Large sample theory of least squares estimators (SLLN; MSLLN; Consistent estimators; Asymptotic probability distribution)
4.       Asymptotic tests (Wald tests;  Restricted least squares under a linear hypothesis; Testing for conditional homoskedasticity)


- PART 2: Dynamic regression models and Single-equation GMM
(Ref. Hayashi 2.2, 3, 7.1 to 7.3)

1.       General formulation (Need for ergodic stationarity; Need for martingale difference sequences; GMM orthogonality condition; Simultaneity issues and relevant instruments; Identification)
2.       Consistent GMM estimator (Definition; Consistency; Asymptotic normality)
3.       Efficient GMM estimation (Efficient weighting matrix; 2S-GMM, iterated GMM and CU-GMM; Connection with ML)

* Probability theory: (i) probability basics; (ii) joint distribution; (iii) inference basics
* Mathematics: many math. for econ. textbooks (focus on optimization and matrix algebra); here is one example among many
* Matlab tutorials: getting started
many ressources available online (including the help and getting started in Matlab)!! here is one example: 3 day tutorial from MIT