New York University
Department of Economics
Applied Statistics and Econometrics G31.1101
Topic
Review of Descriptive Statistics &
Basic Mathematical Tools
Probability Theory and Random Variables
Probability Theory
Single and Multi-dimensional Random Variables
Mathematical Expectations of Random Variables
Text &
Fall 2005
TEXTS :
Mathematical Statistics with Applications, 6 th
Edition, by D. Wackerly, W. Mendenhall, and R. Schaeffer (Duxbury)
Introductory Econometrics , 3 rd
Edition, by Jeffrey Wooldridge (South Western)
COURSE OUTLINE
MS: 1;
Chapter(s)
IE: Appendix A,
Appendix D
MS: 2 – 6;
IE: Appendix B
Appendix D
Estimation Techniques and
Statistical Inference
Properties of Estimators
Methods of Estimation
Confidence Intervals
Hypothesis Testing
Simple Linear Regression Analysis
Standard Assumptions and Functional Forms
Least Squares Estimation of Parameters
Statistical Tests of Model Parameters
MS: 7 – 10; 12 – 14;
IE: Appendix C
IE: 1 – 2;
MS: 11.1 – 11.9
Forecasting using a Single Explanatory Variable
Multiple Regression Analysis
Standard Assumptions and Matrix Formulation
Least Squares Estimation of Parameters
Statistical Tests of Model Parameters
IE: 3 – 6; Appendix E
MS: 11.10 – 11.14
Forecasting using Multiple Explanatory Variables
_______________________________________________________________________

Office Hours:
Office Information
Telephone Number:
Email:
Thursdays, 5:15 – 6:00 p.m.
269 Mercer Street, Room
(212) 435-4408 as44@nyu.edu
Course Requirements:
1.
2.
Mid-term Examination
Final Examination
(40%)
(45%)
3.
4.
Class Project
Homework
(10%)
( 5%)
There will be no “make-up” exam for the mid-term or the final. If you are unable to take the mid-term exam you may (a) choose to take an incomplete for the course and complete the requirements when the course is next offered, or (b) place a weight of 75% on the final exam.
Computer Requirements:
The statistical package EVIEWS will be used throughout the course. You will be issued a computer account with which to gain access to the software.
Lab Session:
The course consists of lecture and lab sessions. You should use the lab sessions to go over course materials, homework assignments, and computer-related issues.

Statistical Theory and Applications
Population
Central Tendency – Mode, Median, Arithmetic Mean, Geometric Mean
Dispersion -- Range, Mean Absolute Deviation, Variance/Standard Deviation, Coefficient of
Variation
Shape -- Skewness, Kurtosis
Probability
Counting outcomes; Permutation, Combination
Probability of an event
Marginal probability
Conditional probability
Independent events
Mutually exclusive events
Random Variables (Discrete and Continuous)
Density functions
Probabilities using a random variable
Expected value of a random variable
Variance/standard deviation of a random variable
Jointly distributed random variables
Marginal density
Conditional density
Covariance
Correlation coefficient
Functions of random variables
Expected value and variance of a sum of random variables
Specific random variables: Binomial, Hypergeometric, Geometric, Negative Binomial,
Poisson, Uniform,Triangular, Exponential, Normal, Logistic, Chi-square, Student “t,” F
Estimation
Important properties of an estimator: unbiasedness, minimum variance, sufficiency, consistency, linearity
Methods of estimation: moments, least squares, maximum likelihood
Point estimators for: mean, proportions, variance, difference of mean, paired differences, difference of proportions, ratio of variances
Confidence intervals
Hypothesis Testing and related issues
Type 1 and Type 11 errors
Hypothesis testing for: mean, proportions, variance, difference of mean, paired differences, difference of proportions, ratio of variances.
Probability of a Type 11 error
Goodness of Fit/Contingency Tables
ANOVA
Non-parametric tests
Optimal sample size

Simple and Multiple Regression Analysis
Population and regression Models
Econometric assumptions for the classical linear model
Functional forms of the population model
Regression models
Point estimation and related statistics
Least squares estimation of unknown population parameters
Residuals
Total, Explained and Unexplained Sum of Squares
Unadjusted R
2
, adjusted R
2
, and F statistics
Variance and standard error of a regression
Variances and covariances of coefficient estimators
Regression through the origin
Matrix formulation and solution of the classical linear model
Hypothesis Testing
Testing of individual coefficients
Testing of joint coefficients
Testing the overall model
Confidence intervals for unknown parameters
Forecasting
Moving average method
Exponential weighted moving average method
Point forecast using structural econometric models
Forecast interval using structural econometric models