Welcome to Economics 345

Course Introduction and Overview

Basics Martin Farnham n  BEC 354 n  mfarnham@uvic.ca

Course website: http://web.uvic.ca/~mfarnham/345.html

n  Lecture notes, problem sets, solutions, etc. will be posted there

n  Please see website for basic info before emailing me! n  When emailing, please sign your name, don’t use text

message symbols etc. Office Hours n  Wed 10:30-12:15; Fri 1:30-2:30 (BEC 354)

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Evaluation 2 Midterms – in class n  First midterm, Tuesday Oct 4 n  Second midterm, Tuesday Nov 1

Lab Exam, 15% (lab session in week of November 28) Ungraded Problem Sets Cumulative Final Exam (40%) (optional) iClicker in lecture (part attendance, part correctly answering questions) 2 options for evaluation n  iClicker option n  No iClicker option

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Evaluation iClicker option n  You can boost your grade with class participation with this option Midterm 1: 15% Midterm 2: 20% iClicker: 10% Lab Exam: 15% Final Exam: 40%

non iClicker option n  If you’d rather not attend lectures regularly, you can use my old

evaluation weighting for this course Midterm 1: 20% Midterm 2: 25% Lab Exam: 15% Final Exam: 40%

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Evaluation You don’t need to tell me which method to grade you by—I’ll automatically grade you by the one that gives you a higher score. You must receive at least a 50% average across the exams (midterms, lab exam, final) to pass the course. An overall course score below 50% is a failing grade (see syllabus for details) n  No exceptions n  Don’t mess around if you plan to graduate this year and need to

pass this course. All exam dates are clearly marked on syllabus – see me now about conflicts!

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iClickers

These are required if you want to be graded by the iClicker option

You can pick them up used or at the bookstore.

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The Textbook

Introductory Econometrics: A Modern Approach, by Jeffrey M. Wooldridge (Sixth Edition) n  Required reading; earlier editions are fine

Each chapter has lots of problems if you’d like more practice. Topics and text readings are listed on the syllabus

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Rules

Class attendance not required; strongly recommended. n  I may stray from lecture notes in class; so you may miss something

important (i.e. could be on exam) n  If you miss a class, don’t ask me what you missed; ask a classmate n  Note that one evaluation option in the course gives points for

attendance (through iClicker points).

Read the syllabus. You’re responsible for adhering to it. Please don’t talk, eat conspicuously, text, or surf web during class. I strictly enforce rules against cheating

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Prerequisites for Economics 345

I will assume that you recall the material that was covered in Econ 246 (Statistical Inference)

I will do a brief review of important points from Econ 245-246 over the next couple lectures.

To refresh your memory, I require that you read carefully through Appendix A, Appendix B and Appendix C in your textbook; the first midterm will include topics from these appendices. n  Lots of pages--start now

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Using EViews to do Econometrics

EViews is available on PCs in the lab in the basement of BEC, in CLE, and in HSD lab Much of your lab sessions will be spent learning how to do statistical analysis with EViews. n  Problem sets will reinforce this n  Lab exam at end of term will involve using EViews

While computers make implementing econometric methods relatively easy, it’s still important to understand what you’re doing

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Lab Sessions and TAs There are 4 weekly lab sessions for Econ 345 (in BEC lab) 2 TAs Lab attendance is not strictly required, but highly recommended. Labs start week of September 19. Lab exam in final full week of class will test your understanding of how to use EViews to do applied econometric analysis. TA will make links between theory learned in class, and practice done in the lab. Econometrics is a prime example of something best learned by doing, rather than book learning

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Lab Sessions and TAs TA will also hold weekly office hours n  Can help with problem sets, questions from lecture

You MUST register for a lab session. All labs are held in BEC basement labs. See lab schedule on website.

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Problem Sets Since metrics is best learned by doing, doing problem sets is another excellent way to develop a real understanding for metrics There will be several problem sets distributed throughout the course n  Will include a mix of econometric theory and practice n  Some problems will require use of EViews n  All problem sets will be ungraded (answer keys will be posted)

Problem sets are given to help you firm up what you learn in lecture n  Key to learning from problem sets is to work HARD on your own n  Then consult a friend in the class and discuss tricky points n  Then come to office hours

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Problem Sets

Doing problem sets carefully and thoroughly is probably the single biggest thing you can do to prep for exams n  Exam questions will sometimes be similar to problem

set questions n  Learning by doing is much more complete than

learning by reading. n  Students who make a serious attempt at all of the

problem set problems will tend to perform significantly better on exams than other students

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Keys to Success Study text and lecture notes carefully n  Do you really understand what’s being said? n  Relying on memorization will kill you!

Do problem sets Work in groups (ideal size is probably about 3) n  Help each other out; teaching someone else material teaches you as

well Break your study into daily (or near daily) segments rather than saving up lots of material to learn in one marathon session each week. n  Graze, don’t binge.

Keep up with the material. n  The material can be quite cumulative n  Don’t fall behind.

Use office hours regularly 14

Why study Econometrics?

It’s one of the most important skills you will develop as an economics student. Allows you to test economic theory against real world data and to estimate economic relationships You can then: n  Evaluate the effects of government policy n  Evaluate and better implement business policy n  Become an educated consumer of statistics

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Why study Econometrics? We live in an information age Data are being collected at a phenomenal rate. Every time you shop with a credit card, click a web link, etc. data are being gathered. Companies will pay big money to people with the skills to analyze those data. n  Consider Netflix competition http://

www.netflixprize.com/ n  Even if you don’t become a quant geek, if you

can intelligently interpret a statistical report, you’ll likely see salary and promotion benefits from this skill. Statistical literacy pays.

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Example: Returns to Education

In a labour economics class, you might want to try to figure out the return to an extra year of education, in terms of the earnings boost it causes A model of human capital investment implies getting more education should lead to higher earnings In the simplest case, this implies an equation like

ueducationEarnings ++= 10 ββ

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Example: (continued)

The parameters of the model, β0 and β1, are constants n  Think of them as the “true” values; but they are unknown and

unknowable to us

In this case they are the intercept and slope of a function relating education to earnings The error term, u, includes all other factors affecting earnings, including unobserved factors One could estimate the parameters of this model using data on a random sample of working people n  Such data is commonly available in datasets put out by Statistics

Canada, or data collection agencies in other countries

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Interpretation of Estimates

One of the first things we will learn in this course is how to estimate such a model Even if we can estimate the model, understanding the true meaning of the estimated coefficients can be tricky For example, if our estimate of β1 is positive, does that really tell us that education causes increased earnings? Or does it just point to a correlation between earnings and education that is driven by some different economic relationship? n  Chapter 1 has nice discussion of causality--read it!

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Interpretation of Estimates

What if something is causing our estimate of β1 to be biased? n  Can we fix the bias? n  Can we learn something about the likely magnitude or sign (+ or -)

of the bias? How precisely have we estimated β1? n  If we estimate that another year of education raises the wage by 50

cents, what’s the confidence interval on that estimate? n  Is the estimate statistically significantly different from zero (i.e. the

case where education has no effect)? These are some examples of issues we will deal with in this course.

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More Applications of Statistical Modeling

Lots of economics applications n  What’s the effect of Bank of Canada interest rate policy on

economic growth, exchange rates, stock prices? n  How effective are different policies to combat homelessness?

Lots of business applications n  How much did our latest advertising campaign increase sales? n  Has our new worker training policy increased retention of

employees? Lowered accident rates? Increased product quality? Legal liability applications n  How much did corporate mismanagement cause share prices to

drop (this is a very commonly asked question in corporate liability lawsuits)?

n  How much has a workplace injury reduced a worker’s lifetime earning potential?

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Types of Data in Empirical Social Science Research

1) Experimental n  Data generated in laboratory (or quasi-laboratory)

setting. n  Example: Data on cancer recovery rates for two groups

of rats (one treatment group that receives cancer drug, one control group that doesn’t receive drug)

n  While there is a burgeoning field of experimental economics, in most cases it is unethical or very expensive to conduct experiments on humans w  Dr. Rondeau offers a class in experimental economics, for

those who are interested.

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Types of Data in Empirical Social Science Research

2) Observational (mostly what we’ll use) n  Data generated in real life by people, firms, etc. going

about their business n  Typically collected in surveys

w  What is your hourly wage? w  How much education do you have? w  How many children do you have? Etc…

n  Econometrics concerns itself primarily with the analysis of observational data

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Types of Observational Data

Cross-sectional data n  Sample of information on individuals (or firms, or countries, etc.)

taken at a single point in time w  Examples: A one-day survey of shoppers at Mayfair Mall where

respondents are asked about income, education, and product preferences; a one-time survey of voters on their opinions on the BC carbon tax

Time-series data n  Data on phenomena that vary over time (usually involving

aggregate data) w  Example: Unemployment rate, inflation rate, GDP w  Data are collected annually, quarterly, or monthly (etc.) and

assembled into a chronological series

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Types of Observational Data Pooled cross-sectional data n  Same as cross-sectional but lumping together several cross sections taken

at different points of time w  Example: A monthly survey of shoppers at Mayfair Mall where respondents

are asked about income, education, and product preferences, where all 12 months are combined to create a single dataset (with different shoppers queried each time) that is then analyzed; repeated surveys of voters (but different voters each time) about the BC Carbon tax.

Panel data (aka longitudinal data) n  Data that are collected from a cross-section of subjects, who are then

subsequently resurveyed over time so that multiple data points exist (over time) for each study subject. w  A blending of time series and cross-sectional data w  Example: Panel Study of Income Dynamics (University of Michigan) follows

individuals over time, interviewing them every two years. Some individuals have been followed for decades.

The focus of this course will be primarily cross-sectional data analysis (with some time series at the end) 25