ECONOMIC STATISTICS II Economics 3972, Fall 2015                       Instructor: Noel Brodsky

                                                                                                            Office: 2831 (215D) Coleman Hall

                                                                                                            Phone: (910) 688-3788

                                                                                                            http://www.eiu.edu/~econinfo

                                                                                                            email: nbrodsky@eiu.edu

                                                                                                            Hours: 3:00pm-4:00pm MWF

                                                                                                            4:00-5:00 W

                                                                                                            or by Appointment

 

GRADE DETERMINATION:

2 REGULAR EXAMS (250 POINTS EACH)                                                 = 500 POINTS

2 TERM PAPERS (WORD PROCESSED-100&50 POINTS)                     = 150 POINTS

1 FINAL EXAM (CUMULATIVE)                                                  = 350 POINTS

TOTAL    1000 POINTS

 

Expected Grade Distribution and Curve Structure:

Curve Structure: Average exam score will be set at or near a baseline "B", excluding all zeros, whenever necessary. Each exam has its own curve, absolutely determined one week after the exam is returned.

 

The Cutoffs are as follows:

 

88% = A, 77% = B, 66% = C, 55% = D, below 55% is an F

 

Special Information: Term papers: Both papers must utilize a word processor, done by the student turning the paper in for his or her grade. Failure to do this will result in a failing grade for the paper. The second paper is absolutely required for the course, and regardless as to the point value of the paper, failure to turn it in will result in an automatically failing grade for the course.  The first paper will be a short (5-7 pages) exposition of statistical data and a regression that is given to you. The second paper is of your own making, subject to my approval. A poor topic choice is serious, and can result in a loss of 3 (three) letter grades on the final paper. It is a longer paper, say about 10-15 pages. Do not ever copy from someone else.

 

Important Dates: First Paper due: November 18, 2015 at 4:00pm Paper Length: 5-7 pages, word processed, double spaced, your work. The paper may be turned in on Wednesday, November 20, 2015, with the loss of 2 letter grades, each day thereafter, one letter grade is lost.

 

Second Paper: Proposal Due November 20, 2015 Paper Length: 10-15 pages, word processed, double-spaced, your work.  Final Draft Due Wednesday December 9, 2015, at 4:00pm. The paper may be turned in on Friday December 11, 2015, with the loss of 2 letter grades, each day thereafter, one letter grade is lost.

 

Exams: Generally, expect an exam to come at the end of Simple Linear Regression, and another at the end of Multiple Regression. Students have one week to challenge an exam grade, after this the exam is closed, and a final curve for that exam is given. Make-Up Exams: There must be a very compelling reason to be granted a make-up exam, which, if at all possible, should be arranged in advance. I do not intend to give make-up exams, if I can avoid it.  If you have a documented disability, and wish to discuss academic accommodations, please contact me as soon as possible.  You may not use either your cell phone or a graphing calculator on exams. 

 

Special requirement: All students are required to have a computer account.. Please note that this course is designated writing intensive. This means that part of your grade is a result of your writing style. The final paper can be submitted to the electronic writing portfolio required for graduation from Eastern.

 

COURSE LEARNING OBJECTIVES 

 

1.       The student will achieve an understanding of economic statistics, increasing confidence in its application.

2.       Students will learn the basics of ordinary least squares model estimation, and its variants.

3.       Students will learn appropriate alternatives to ordinary least squares, when assumptions underlying the classical linear regression model are violated.

4.       Students will learn model construction and estimation.

5.       Students will gain insights into econometric estimation and diagnostic testing. 

6.       Students will understand the basics of time series analysis.

 


ECONOMIC STATISTICS II ECN 3972 - N. Brodsky, Instructor

Syllabus

Text: Statistics for Economics and Business, 6th ed. Newbold, Carlson and Thorne, Prentice Hall, Upper Saddle River, NJ, 2007

 

Chapter Topic Assignment

10.1,2,3,5 Review: Hypothesis Testing, Confidence Intervals                                   10.6,12,14,46,52,58,62

Tests on means (Z&t), variances (c2), F-test.                                                                                 

12.1 Correlation Analysis                                                                                                                  12.2,4,6

12.2 Linear Regression Model                                                                                                          12.10,12

12.3 Least Squares Coefficient Estimators

12.4 The Explanatory Power of a Linear Regression Equation

12.5 Statistical Inference: Hypothesis Tests and Confidence Intervals                                   12.19,20,24,38

12.6 Prediction                                                                                                                                     12.42, 44

12.7 Graphical Analysis

 

EXAM I

 

13.1 The Multiple Regression Model                                                                                               13.2, 4, 8

13.2 Estimation of Coefficients

13.3 Explanatory Power of a Multiple Regression Equation

13.4 Confidence Intervals and Hypothesis Tests for Individual Regression Coefficients    13.16, 18, 20

13.5 Tests on Sets of Regression Parameters                                                                                 13.24, 26, 30,40, 42

13.6 Prediction                                                                                                                                      

13.7 Transformation for Nonlinear Regression Models                                                               13.60, 62, 66

13.8 Dummy Variables for Regression Models                                                                             13.70, 74,

13.9 Multiple Regression Analysis Application Procedure                                                          13.96, 100, 102, 106, 108

14.1 Model-Building Methodology                                                                                                 

14.2 Dummy Variables and Experimental Design                                                                        14.4

14.3 Lagged Values of the Dependent Variables as Regressors                                                 14.11,12

14.4 Specification Bias                                                                                                                      14.22

14.5 Multicollinearity                                                                                                                         14.24, 25, 44

14.6 Heteroscedasticity                                                                                                                      14.26, 28, 60

14.7 Autocorrelated Errors                                                                                                                14.30, 34, 48, 50

 

EXAM II

 

19.3 Components of a Time Series

19.4 Moving Averages

19.5 Exponential Smoothing                                                                                                            19.28, 30

19.6 Autoregressive Models                                                                                                              19.42, 44

16.1 Goodness of Fit Tests: Specified Probabilities                                                                      16.2, 16.4

16.2 Goodness of Fit Tests: Population Parameters Unknown                                                  16.10, 14

16.3 Contingency Tables                                                                                                                   16.22, 26, 28

 

FINAL EXAM (cumulative)