UCSC Econ 113  Introduction to Econometrics (Summer 2013) This course is an introduction to the theory and application of statistics to economic problems. This course focuses on the techniques used in empirical research with a particular focus on intuitive understanding. Weekly problem sets will introduce real world applications and teach you the fundamentals of statistical programming. No prior knowledge of computer programming is required. Class meets three times a week for lectures and once per week for section, which are strongly suggested though not mandatory.
Lectures  Timeline of Topics Covered
Quizzes (10% of grade)  Short multiple choice quizzes will be given at random points during lecture. The goal of quizzes are to incentivize attendance and a minimum level of lecture prep and review. Thus no makesup.
Homework (15% of grade)  Problem sets are assigned weekly (or more frequently). Although you may work in groups on problem sets, you must turn in your own homework and write up the answers on your own. Solutions are posted when HWs are due. Turning in Homework: at lecture on the due date.
 Homework 0  Run through all 7 lessons on CodeSchool's TryR.  Should take less than two hours. Thirty minutes if you're quick.
 Homework 1  Expected value, mean, variance, and bias. Inference about the location of the mean. Using R.
Questions [pdf]  All Solutions [pdf]
Problem 1  due Monday July 1st at start of class Expected value calculation, discrete random event. solutions [pdf] Problem 2  due Monday July 1st at start of class Expected value calculation, discrete random event. solutions [pdf] Problem 3  due Monday July 1st at start of class Probability. solutions [pdf] Problem 4  due Monday July 1st at start of class Expected value of mean and variance estimators, systematic error and mean zero error. solutions [pdf] Problem 5  due Friday June 28th at start of class Hand calc mean, variance and standard deviation, simple summary stats with R. solutions [pdf] Problem 6 due Monday July 1st at start of class Continuous random events, normal distribution, zscore probability calculation. solutions [pdf] Problem 7  due Wednesday July 3rd at start of class Hypothesis test with sample statistic. solutions [pdf] Problem 8  due Friday June 28th at start of class R! Load data, summary statistics, subsetting, measurement error revisited. solutions [pdf] Problem 9  due Wednesday July 3rd at start of class R continued, unbiasedness assumptions, smallsample hypothesis test. solutions [pdf] Problem 10  due Wednesday July 3rd at start of class R continued, largesample hypothesis test. solutions [pdf]
 Homework 2  Simple OLS, inference on regressions, pvalue, Rsquared, unbiasedness assumptions. Also regressions with R, calculating omitted variable bias calculations. Also some nonlinear regressions (loglevel, levellog, etc).
Questions [pdf]  Solutions  RCode
Problem 1  due Monday July 8th at class start Derive covariance, correlation and OLS estimates by hand, rescale cov, plot OLS estimate, interpret regression coefficient estimates and consider bias, walk through assumptions that lead to unbiased result, Rsquared, plus some R. solutions (pdf) Problem 2  due Monday July 8th at class start Example of omitted variable bias. Derive OLS, calculate bias. solutions (pdf) Problem 3  due Monday July 8th at class start Quick fire omitted variable bias questions. solutions (pdf) Problem 4  due Monday July 8th at class start Nonlinear regression (levellevel, loglevel, etc.), derive interpretations, additional issues. solutions (pdf)
 Homework 3 & 4  Multivariate regressions and inference, Enriched Models. Ftest. (Wololdridge Ch 37)
Questions [pdf]
Problem 1  due Wednesday July 17th Multivariate
Regression by hand, SST, SSR, Rsquared and pvalue. solutions (pdf) Problem 2  due Monday July 22nd Example of
attenuation bias, measurement error in x. solutions (pdf) Problem 3  due Wednesday July 17th We work through a quadratic
model, estimate the model, report results, predict values, find the max, interpret
coefficients, and plot. solutions (pdf) Problem 4  due Wednesday July 17th Correction on
observables, the effect of adding variables to a model, hypothesis test and pvalue practice, presenting results in
organized table. solutions (pdf) Problem 5  due Monday July 22nd Standardizing/normalizing
results, interpret results. solutions (pdf) Problem 6  due Monday July 22nd Confidence intervals,
Dummy Variables and dummy plus interaction terms. solutions (pdf) Problem 7  due Monday July 22nd Categorical variables,
dummy variable trap, multiple dummy variables and interpretation. solutions (pdf) Problem 8  due Monday July 22nd Category variables,
multiple dummy variables and interpretation. solutions (pdf) Problem 9  due Wednesday July 17th Accuracy of a
prediction, simple regression, rewrite to get a prediction, interpret it and
error. solutions (pdf) Problem 10  due Monday July 22nd Multivariate
regression with a quadratic term, Ftest. solutions (pdf)  Problem 11  due Monday July 22nd
Ftest and adjusted Rsquared, nested vs
unnested models. Dummy plus dummy with interaction term. solutions (pdf) Problem 12  due Wednesday July 17th Regression predictions, and the accuracy of our prediction. Standardize variables and reinterpret. solutions (pdf) Problem 13  due Monday July 22nd General review,
Ftest, joint significance test, standardizing data. solutions (pdf)

Instructor: Curtis Kephart, curtiskephart+econ113@gmail.com Course Location: Jack Baskin Auditorium 101 (map) Course Time: Mon, Wed & Fri 9:00 AM to 11:30 AM Course Syllabus [pdf]
Teaching Assistant(s):
Other Resources
Textbook  Introductory Econometrics (Any Edition) by Jeffrey Wooldridge More Optional Readings Reserve copies at Sci & Eng Library
Some disorganized Econometrics and R Resources
Statistical Analysis Software: We will be using R.
1. Download and Install R: bit.ly/158DWaG
R is a free and open source statistical software. Feel free to use SAS or STATA if you have a strong preference for either.
2. Download and Install RStudio: www.rstudio.com/ide/download/desktop
RStudio is a free and open source integrated development environment that is easier to work with and vastly superior to the consoleandscript setup that comes with the basic R installation.
 
Office Hours
Curtis' Office Hours When: 12:30 PM Wedneday Where: E2, Room 403C (map)
TA Office Hours Thursdays 1011AM Room E2, 403G
Sections
With Jae Tues 10AM  Noon
MSI Meeting Times
krubio1@ucsc.edu Mon 12:151:15PM JBE 169 Tuesday 23:00PM ARC 202
Wednesday Noon1PM Arc 216
Thurs 2:303:30PM SS2 137
Tentative Exam Dates
Exams  no calculators. no notes
(30% of grade)
Final: July 26th at 911:30 AM Solutions Sketch (45% of grade) The Final will be comprehensive, but will emphasize later material. 

