Materials
Slides and Handouts
Introduction to Matlab. Slides 1. Slides 2.
STATA: ML estimation. Slides. Do-file.
STATA: Heteroskedasticity and Autocorrelation. Do-file.
Computational Problem Sets
Computational Problem Set 1. OLS
Suggested Solutions: OLS function, OLS function with structures (Valeria and Orestis), Ex1, MC_unbiasedeness, MC_consistency, MC_asymptnorm
All previous m-files in one archive here
Computational Problem Set 2. Heteroskedasticity and Autocorrelation (due February 10)
Suggested Solutions: Problem1_main (p1), Problem2_main (p2),
Functions: OLS finction(olsq), FGLS function for heteroskedasticity (fglsq),
Cohrane_Orcutt FGLS for autocorrelation (Cohrane_Orcutt_GLS), Newey-West estimator (NeweyWest)
Tests: Heteroskedasticity (White (testwhite), Breusch-Pagan and Harvey-Godfrey (hettest)),
Autocorrelation BreuschGodfrey (BreuschGodfreyTest)
All previous m-files in one archive here
Computational Problem Set 3. Maximum Likelihood (due February 21). Data for the third problem: PSID_income.xls
Suggested solutions: Main file (PS3.m), Likelihood function (MLE_normal.m), OLS.m
All m-files in one archive here
Computational Problem Set 4. Non-linear estimation (due February 28). Data for the 4th problem set: Data_Fair_1978.xls
Analytical Problem Sets. Solutions.
Brief Content of Classes
Class 1 (January 17)
Introduction to Matlab (Slides 1)
Class 2 (January 24)
Matlab. Optimization (Slides 2)
Class 3 (January 31)
Computer Problem Set 1. Hypothesis testing refresher. Basics of Stata
Class 4 (February 3)
Solve Analytical Problem Set 1
Class 5 (February 10)
Solve Computational Problem Set 2 and start Analytical Problem Set 2 (LĂdia will finish the APS 2 in her lecture).
Class 6 (February 17)
Finish Analytical Problem Set 2. Solve Analytical Problem Set 3
Class 7 (February 21)
Solve Computational Problem Set 3. Maximum likelihood estimation, heteroskedasticity and autocorrelation analysis in STATA.