Amemiya, Takeshi Introduction to Statistics and Econometrics (Cambridge, Massachusetts: Harvard University Press, 1994).Mittelhammer, Ron C. Mathematical Statistics for Economics and Business (New York: Springer-Verlag, 1996).Amemiya, Takeshi Introduction to Statistics and Econometrics (Cambridge, Massachusetts: Harvard University Press, 1994).Mittelhammer, Ron C. Mathematical Statistics for Economics and Business (New York: Springer-Verlag, 1996).| Homework | |
| Two Mid Term Examinations | |
| Final Examination | |
| Class Participation |
Introduction to Statistics, Probability and Econometrics (August 23-August 25, 2 Lectures)Lecture I: Definitions and Course Overview pdf Notes, PowerPoint, Slideshow
Bierens, Herman J. Introduction to Mathematical and Statistical Foundations of Econometrics Cambridge, UK: Cambridge University Press, 2005. Netlibrary Lecture II: Basic Axioms of Probability pdf Notes, PowerPoint, SlideshowLecture III: Probability Theory and Measure pdf Notes, PowerPoint, SlideshowRandom Variables and Probability Distributions (August 30-September 8, 3 Lectures)Lecture IV: Random Variables and Probability Distributions pdf Notes, PowerPoint, SlideshowGauss Code Code which generates a sequence of Random NumbersGauss Output Resulting matrix of random numbersLecture V: Distribution Functions of Random Variables pdf Notes, PowerPoint, SlideshowLecture VI: Derivation of the Normal Distribution pdf Notes, PowerPoint, SlidesLecture VII: An Applied Sabbatical pdf Notes, PowerPoint, SlideshowMoments and Moment Generating Functions (September 13-September 20, 3 Lectures)Lecture VIII: Mean and Higher Moments pdf Notes, PowerPoint, SlideshowLecture IX: Moments of More than One Random Variable pdf Notes, PowerPoint, SlideshowLecture X: Moment Generating Functions pdf Notes, PowerPoint, SlidesBinomial and Normal Random Variables (September 23-September 29, 3 Lectures)Lecture XI: Binomial and Normal Random Variables pdf Notes, PowerPoint, SlideshowLecture XII: Normal Random Variables pdf Notes, PowerPoint, SlideshowLecture XIII: Bivariate and Multivariate Normal Random Variables pdf Notes, PowerPoint, SlideshowTest I - October 4Large Sample and Asymptotic Theory (October 11-October 18, 3 Lectures)Lecture XIV: Large Sample Theory pdf Notes, PowerPoint, Slideshow
Ohio State Lecture Notes PDF Notes University of California, Berkeley Lecture Notes PDF Notes Kuersteiner, G.M. Asymptotic Theory PDF NotesLecture XV: Limits and the Law of Large Numbers pdf Notes, PowerPoint, SlideshowLecture XVI: Empirical Examples of the Central Limit Theorem pdf Notes, PowerPoint, Slideshow Homework Assignment 6 Characteristic Functions Central Limit Theorem The Central Limit Theorem Artur B. Adib Central Limit Theorem-Statistical Fine Print Charles Annis Jolliffe, I.T. "Sample Sizes and the Central Limit Theorem: The Poisson Distribution as an Illustration." The American Statistician 49(3)(August 1995): 269. JSTOR Proof of Central Limit Theorem H. Krieger Nakata, T. "On an Estimate of the Remainder Term in the Central Limit Theorem." Advances in Applied Probability 11(2)(June 1979): 285-6. JSTOR Setup for the Central Limit Theorem Jim Pitman Tardiff, R.M. "L'Hospital's Rule and the Central Limit Theorem" The American Statistician 35(1)(February 1981): 43. JSTOR Illustration of the Central Limit Theorem Thayer Watkins Wong, C.S. "A Note on the Central Limit Theorem." American Mathematical Monthly 84(6):472. JSTORPoint Estimation (October 20-October 27, 3 Lectures)Lecture XVII: Definition of Estimator and Choosing among Estimators pdf Notes, PowerPoint, SlideshowLecture XVIII: Mean Squared Error and Maximum Likelihood pdf Notes, PowerPoint, SlideshowLecture XIX: Sufficient Statistics pdf Notes, PowerPoint, SlideshowLecture XX: Concentrated Likelihood Functions, Normal Equations, and Properties of Maximum Likelihood pdf Notes, PowerPoint, SlideshowInterval Estimation (November 1-November 3, 2 Lectures)Lecture XXI: Confidence Levels pdf Notes, PowerPoint, SlideshowLecture XXII: Bayesian Estimation and Confidence Intervals pdf Notes, PowerPoint, SlideshowTesting Hypotheses (November 8-November 17, 4 Lectures)Lecture XXIII: Type I and Type II Errors and the Neyman-Pearson Lemma pdf Notes, PowerPoint, SlideshowLecture XXIV: Composite Tests and the Likelihood Ratio Test pdf Notes, PowerPoint, SideshowLecture XXV: Examples and Multivariate Testing pdf Notes, PowerPoint, SlideshowTest II - November 22Old ExamsElements of Matrix Analysis (November 29-December 1, 2 Lectures)Lecture XXVI: Review of Matrix Algebra and Vector Spaces pdf Notes, PowerPoint, SlideshowLecture XXVII: Vector Spaces and Eigenvalues pdf Notes, PowerPoint, SlideshowBivariate and Multivariate Regression (December 6-December 8, 2 Lectures)Lecture XXVIII: Simple Linear Regression pdf Notes, PowerPoint, SlideshowLecture XXIX: Distribution of Estimates and Multivariate Regression pdf Notes, PowerPoint, SlideshowLecture XXX: Restricted Least Squares and Hypothesis Testing pdf Notes R Code to generate linear model Pseudo data R Code restricted estimationLecture XXXI: Exceptions to Ordinary Least Squares pdf NotesLecture XXXII: Generalized Method of Moments Estimator pdf Notes, PowerPoint, SlideShowFinalOld Exams
