Welcome to Economics 345

Course Introduction and Overview

Basics !   Martin Farnham

  BEC 354   mfarnham@uvic.ca

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

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

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

message symbols etc. !  Office Hours

  TBA (BEC 354)   Or by appointment

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Evaluation !   2 Midterms – in class

  First midterm, Jan 30   Second midterm, Feb 27

!   Lab Exam, 15% (lab session in week of March 23) !   Ungraded Problem Sets !   Cumulative Final Exam (40%) !   (optional) iClicker in lecture (part attendance, part

correctly answering quesitons) !   2 options for evaluation

  iClicker option   No iClicker option

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Evaluation !   iClicker option

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

!   non iClicker option   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: 20%

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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)   No exceptions   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   I’m trying them out   Interested in using them to get feedback from you,

encourage engagement in the classroom

!  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 (Fifth Edition)   Required reading; editions 3 or 4 are fine

!  Each chapter has lots of problems if you’d like more practice.

!  Online solutions manual for selected problems is available http://login.cengage.com/sso/   You may need a password that comes with the

textbook.

!  Topics and text readings are listed on the syllabus 6

Rules

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

important (i.e. could be on exam)   If you miss a class, don’t ask me what you missed; ask a classmate   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, read paper 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.   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 (maybe others)

!  Much of your lab sessions will be spent learning how to do statistical analysis with EViews.   Problem sets will reinforce this   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 2 weekly lab sessions for Econ 345 (in BEC lab) !   2 TAs

  Henry Yeh   Alisha Chicoine

!   Lab attendance is not strictly required, but highly recommended. Labs start week of January 20.

!   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 10

Lab Sessions and TAs !  TA will also hold weekly office hours

  Can help with problem sets, questions from lecture !  You MUST register for a lab session. !  All labs are held in BEC basement labs or in CLE. !  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   Will include a mix of econometric theory and practice   Some problems will require use of EViews   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   Key to learning from problem sets is to work HARD on your own   Then consult a friend in the class and discuss tricky points   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   Exam questions will sometimes be similar to problem

set questions   Learning by doing is much more complete than

learning by reading.   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

  Do you really understand what’s being said?   Relying on memorization will kill you!

!   Do problem sets !   Work in groups (ideal size is probably about 3)

  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.   Graze, don’t binge.

!   Keep up with the material.   The material can be quite cumulative   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:   Evaluate the effects of government policy   Evaluate and better implement business policy   Become an educated consumer of statistics

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Why study Econometrics? !  We live in an information age !  Data are being collected at phenomenal rates.

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.   Consider Netflix competition http://

www.netflixprize.com/   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 ββ17

Example: (continued)

!   The parameters of the model, β0 and β1, are constants   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   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?   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?   Can we fix the bias?   Can we learn something about the likely magnitude or sign (+ or -)

of the bias? !   How precisely have we estimated β1?

  If we estimate that another year of education raises the wage by 50 cents, what’s the confidence interval on that estimate?

  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   What’s the effect of Bank of Canada interest rate policy on

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

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

employees? Lowered accident rates? Increased product quality? !   Legal liability applications

  How much did corporate mismanagement cause share prices to drop (this is a very commonly asked question in corporate liability lawsuits)?

  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   Data generated in laboratory (or quasi-laboratory)

setting.   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)

  While there is a burgeoning field of experimental economics, in most cases it is unethical or very expensive to conduct experiments on humans   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)   Data generated in real life by people, firms, etc. going

about their business   Typically collected in surveys

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

  Econometrics concerns itself primarily with the analysis of observational data

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Types of Observational Data

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

taken at a single point in time   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   Data on phenomena that vary over time (usually involving

aggregate data)   Example: Unemployment rate, inflation rate, GDP   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

  Same as cross-sectional but lumping together several cross sections taken at different points of time   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)   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.   A blending of time series and cross-sectional data   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